The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture:
> The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, evaluate, serialize the result, and finally send it back.
> This was costing us ~$300K/year in compute, and the number kept growing as more customers and detection rules were added.
For something so core to the business, I'm baffled that they let it get to the point where it was costing $300K per year.
The fact that this only took $400 of Claude tokens to completely rewrite makes it even more baffling. I can make $400 of Claude tokens disappear quickly in a large codebase. If they rewrote the entire thing with $400 of Claude tokens it couldn't have been that big. Within the range of something that engineers could have easily migrated by hand in a reasonable time. Those same engineers will have to review and understand all of the AI-generated code now and then improve it, which will take time too.
I don't know what to think. These blog articles are supposed to be a showcase of engineering expertise, but bragging about having AI vibecode a replacement for a critical part of your system that was questionably designed and costing as much as a fully-loaded FTE per year raises a lot of other questions.
I mostly agree, but it's more appropriate to weigh contributions against an FTE's output rather than their input. If I have a $10m/yr feature I'm fleshing out now and a few more lined up afterward, it's often not worth the time to properly handle any minor $300k/yr boondoggle. It's only worth comparing to an FTE's fully loaded cost when you're actually able to hire to fix it, and that's trickier since it takes time away from the core team producing those actually valuable features and tends to result in slower progress from large-team overhead even after onboarding. Plus, even if you could hire to fix it, wouldn't you want them to work on those more valuable features first?
They were running a big kubernetes infrastructure to handle all of these RPC calls.
That takes a lot of engineer hours to set up and maintain. This architecture didn't just happen, it took a lot of FTE hours to get it working and keep it that way.
Kube is trivial to run. You hit a few switches on GKE/EKS and then a few simple configs. It doesn't take very many engineer hours to run. Infrastructure these days is trivial to operate. As an example, I run a datacenter cluster myself for a micro-SaaS in the process of SOC2 Type 2 compliance. The infra itself is pretty reliable. I had to run some power-kill sims before I traveled and it came back A+. With GKE/EKS this is even easier.
Over the years of running these I think the key is to keep the cluster config manual and then you just deploy your YAMLs from a repo with hydration of secrets or whatever.
Exactly. Reddit did one last year like: “We migrated from python to golang and fixed a bunch of non-performant SQL queries. It was so fast, isn’t golang awesome?”
I was once asked to migrate a Microsoft Access application to C#/MS SQL Server because it was too slow. I just added a few database indexes to make it an order of magnitude faster.
(They still wanted to go ahead with the migration, but that's a different story.)
I have about a dozen projects I’d love to tackle in this vein. (Not as low hanging fruit, but enough effort they’re languishing in the backlog.) we’ll actually be able to get to more those projects with agents and good specs
In my experience, a lot of these types of migrations aren't incredibly deep in terms of actual code being written. It's about being able to assess all of the affected facets accurately. Once that's all mapped out, it's pretty straight forward to migrate.
I wonder how much it would have cost them if they weren't paying cloud rates for all of that, and they kept the same general inefficient architecture, sans the Kubernetes bloat.
Doubt they'd have a blog post to write about that, though.
> If they rewrote the entire thing with $400 of Claude tokens it couldn't have been that big.
The original is ~10k lines of JS + a few hundred for a test harness. You can probably oneshot this with a $20/month Codex subscription and not even use up your daily allowance.
You aren’t accounting for managerial politics. A product manager won’t gamble on a large project to lower operating cost, when their bonus is based on customer acquisition metrics.
The original author said he built this on the weekend, so my assumption is that this was something engineers had advocated for before but were shut down because management wanted them elsewhere.
The use of ai agents allowed them to shrink the problem down to the point where it was small enough to fit in their free time and not interrupt their assigned work.
I was thinking the same - if JSONata was a priority for them, why not choose a language with good support, like JS or Java? OTOH if development language was a priority why not choose a format that is well supported in it?
No offence, but inexperienced JS fanatics always do this because of some weird affectionado they have for the language itself. Otherwise, even a decently qualified CTO would have chosen to keep everything in Go from the beginning or might have not waited until they were bleeding $300k. JS is also the worst possible language choice for this problem. So, it definitely sounds a bunch of script kiddies with fancy titles bought with VC money rather than actual experience.
I've seen it happen and it's usually just Normalization of Deviance in an organization that is focusing on something else. Someone needs some kind of functionality and Kube makes creating services trivial so they launch it into a different service[0]. Over time, while people are working on important things this thing occasionally has load issues so someone goes and bumps the maxReplicas up periodically. Eventually you come back to it a year later and maxReplicas is at 24 and you've removed the code paths for almost everything that is hitting the server except some inexplicable hot-loop.
Then you look at it and you're like "Jesus! What the fuck, I meant to have this be a stop-gap". I've done as bad when at near 100% duty-cycle. Often you're targeting just the primary thing that's blocking some revenue and if you get caught yak-shaving you're screwed. A year ago, I did one of these things because I was in the middle of two projects that were blocking a potential hundred-million in revenue.
A year down the line, Claude Opus 4.6 could have live-solved it. But Claude of that time would have required some time and attention and I was doing something else.
That engineering team is some 15 people strong and the company is at $400m+ revenue. If you saw the code, you'd wonder why anyone would have done something like this.
0: I once did this because some inscrutable code/library was tying us to an old runtime so I just encapsulated it in HTTP and moved it into a service.
Think this is pure piggyback marketing on what cloudflare did with next.js. In my experience a company that raised $30MM a month ago is extremely unlikely to be investing energy in cost rationalization/optimization.
edit: saw the total raise not the incremental 30MM
Completely agree. We have > $50m from our most recent funding round, and even a cloud expense of $50k/year (in our case for storage) is considered a high priority to address. If it was $300k, our CTO would be running around with a butane torch setting everyone’s hair on fire until the problem was resolved.
But, venture funding does create a lot of weird inefficiencies which vary from company to company.
> At Reco, we have a policy engine that evaluates JSONata expressions against every message in our data pipeline - billions of events, on thousands of distinct expressions.
The original architecture choice and price almost gave me a brain aneurysm, but the "build it with AI" solution is also under-considered.
This looks like a perfect candidate for existing, high quality, high performance, production grade solutions such quamina (independent successor to aws/event-ruler, and ancestor to quamina-rs).
There's going to be a lot of "we were doing something stupid and we solved it by doing something stupid with AI [LLM code]" in our near future. :-|
Some background on one of the other two golang implementations mentioned in the comments.
Years ago I hired an Upwork contractor to port v1.5.3 to golang as best he could. He did a great job and it served us well, however it was far, far from perfect and it couldn't pass most of the JS test suite. The worst was that it had several recursion bugs that could segfault with bad expressions.
Early in 2025 I used Claude Code and Codex to do a proper, compliant port that passes the full set of tests and is safe. It was most certainly not a trivial task for AI, as many nuances of JSONata syntax derive from its JS roots.
Regardless, it was a great experience and here's the 2.0.6 AI port, along with a golang exerciser that lets you flip back and forth between the implementations. We did a seamless migration and it's been running beautifully in prod in Blues' Notehub for quite a while - as a core transformation capability used by customers in our JSON message pipeline.
>The approach was the same as Cloudflare’s vinext rewrite: port the official jsonata-js test suite to Go, then implement the evaluator until every test passes.
the first question that comes to mind is: who takes care of this now?
You had a dependency with an open source project. now your translated copy (fork?) is yours to maintain, 13k lines of go. how do you make sure it stays updated? Is this maintainance factored in?
I know nothing about JSONata or the problem it solves, but I took a look at the repo and there's 15PRs and 150 open issues.
That's only important if the plan is to stay feature-compatible with the original going forward.
For this case, where it's used as an internal filtering engine, I expect the goal is fixing bugs that show up and occasionally adding a feature that's needed by this organization.
>expect the goal is fixing bugs that show up and occasionally adding a feature that's needed by this organization.
Even if we assume a clean and bug free port, and no compatibility required moving forward, and a scope that doesn't involve security risks, that's already non trivial, since it's a codebase no one has context of.
Probably not 500k worth of maintainance (because wtf were they doing in the first place) but I don't buy placing the current cost at 0.
The full translation took 7hrs and $400 in tokens. Applying diffs every quarter using AI is much easier and cheaper. Software engineering has completely changed.
except there are 2 go implementations already, and he burnt 500k per year to have a kubernetes clusters to parse json (???), so the total gain is -500000*year - 400 + 1 (deducting prompt to use existing implementation)
The headline seems to be flashy indeed, but ai didn't really solve this imo.
They just seemed to fix their technology choices and got the benefits.
There's existing golang versions of jsonata, so this could have been achieved with those libraries too in theory. There's nothing written about why the existing libraries aren't good enough and why a new one needed to be written. Usually you need to do some due diligence in this area, but no mentions of it in this post
In order to measure the real efficiency, gnata should've been benchmarked against the existing golang libraries. For all we know, the ai implementation is much slower.
The benchmarks in the blog are also weird. The measurement is done within the app, but you're meant to measure the calls within the library itself (e.g calling the js version in its isolated benchmark vs go version in its isolated benchmark). So you don't actually know what the actual performance of the ai written version is?
The only benefit, again, is that they fixed their existing bad technology choice, and based on what is observed, with a lesser bad technology choice. Then it's layered with clickbait marketing titles for others to read.
I'll probably need to expect more of these types of posts in the future.
> There's existing golang versions of jsonata, so this could have been achieved with those libraries too in theory
The only one I found (jsonata-go) is a port of JSONata 1.x, while the gnata library they've published is compatible with the 2.x syntax. Guess that's why.
Looking at the releases, it looks like JSONata's 2.1.0 release from July 2025 added the `?:` and `??` syntax, and there hasn't been an update to the syntax since January 2020's 1.8.0 release that added `%`
Because his prompt said to implement in go, not to check if an go implementation already exists.
They have been running kubernetes clusters to parse json, this is not suprising.
If they're vendoring the dependency anyway, that wouldn't matter much if they're not using features that were added since 2021.
The last release of jsonata was mid 2025, and there hasn't been new features since the last 2022 release until the latest, so it's likely those other ports are fine.
They said in the article that they were running up to 200 pods at a time. Doing some back of the envelope math, 200 pods at $300,000 year is about $0.17/hour, which is exactly what an EC2 c5.xlarge costs per hour (on demand). That has 4 vCPUs, so about 800 vCPUs during peak, with $0.0425/CPU-hour.
I do have some questions like:
* Did they estimate cost savings based on peak capacity, as though it were running 24x7x365?
* Did they use auto scaling to keep costs low?
* Were they wasting capacity by running a single-threaded app (Node-based) on multi-CPU hardware? (My guess is no, but anything is possible)
It is, by orders of magnitude, larger than any deployment that I have been a part of in my work experience, as a 10-year data scientist/Python developer.
>The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, evaluate, serialize the result, and finally send it back.
But either way, we're talking $25k/mo. That's not even remotely difficult to believe.
First I thought they were AWS lambda functions, perhaps possible if they are over-provisioned for very concurrency or something similar $25k/month is in realm of possibility.
But no, the the post is talking about just RPC calls on k8s pods running docker images, for saving $300k/year, their compute bill should be well above $100M/year.
Perhaps if it was Google scale of events for billions of users daily, paired with the poorest/inefficient processing engine, using zero caching layer and very badly written rules, maybe it is possible.
Feels like it is just an SEO article designed to catch reader's attention.
It has to be satire right? Like, you aren't out of touch on this. I get engineers maybe making the argument that $300k / year on cloud is the same as 1.5 devops engineers managing in-house solutions, but for just json parsing????
For numbers like that, I can never tell whether it's just a vastly larger-scale dataset than any that I've seen as a non-FAANG engineer, OR, a hilariously-wasteful application of "mAnAgEd cLoUd sErViCeS" to a job that I could do on a $200/month EC2 instance with one sinatra app running per core. This is a made-up comparison of course, not a specific claim. But I've definitely run little $40 k8s clusters that replaced $800/month paid services and never even hit 60% CPU.
Well, for starters, they replace the RPC call with an in-process function call. But my point is anybody who's surprised that working with JSON at scale is expensive (because hey it's just JSON!) shouldn't be surprised.
Well everything is expensive at scale, and any deserialization/serialization step is going to be expensive if you do it enough. However
yes i would be surprised. JSON parsing is pretty optimized now, i suspect most "json parsing at scale is expensive" is really the fault of other parts of the stack
It can be, but $500k/year is absurd. It's like they went from the most inefficient system possible to create, to a regular normal system that an average programmer could manage.
I have no idea if they are doing orders of magnitude more processing, but I crunch through 60GB of JSON data in about 3000 files regularly on my local 20-thread machine using nodejs workers to do deep and sometimes complicated queries and data manipulation. It's not exactly lightning fast, but it's free and it crunches through any task in about 3 or 4 minutes or less.
The main cost is downloading the compressed files from S3, but if I really wanted to I could process it all in AWS. It also could go much faster on better hardware. If I have a really big task I want done quickly, I can start up dozens or hundreds of EC2 instances to run the task, and it would take practically no time at all... seconds. Still has to be cheaper than what they were doing.
You didn't say it was stupid. If you had, I would have just ignored the comment. But you expressed a level of surprised that led me to believe you're unfamiliar with how much of a pain in the ass JSON parsing is.
I think OP’s point was surprise that a company would spend so much on such inefficient json parsing. I’m agreeing. I get that JSON is not the fastest format to parse, but the overarching point is that you would expect changes to be made well before you’re spending $300k on it. Or in a slightly more ideal world, you wouldn't architect something so inefficient in the first place.
But it's common for engineers to blow insane amounts of money unnecessarily on inefficient solutions for "reasons". Sort of reminds me of saas's offering 100 concurrent "serverless" WS connections for like $50 / month - some devs buy into this nonsense.
Congrats! This author found a sub-optimal microservice and replaced it with inline code. This is the bread and butter work of good engineering. This is also part of the reason that microservices are dangerous.
The bad engineering part is writing your own replacement for something that already exists. As other commenters here have noted, there were already two separate implementations of JSONata in Go. Why spend $400 to have Claude rewrite something when you can just use an already existing, already supported library?
This isn’t the first time I’ve read a ridiculous story like this on hackernews. It seems to be a symptom of startups who suddenly get a cash injection with no clue how to properly manage it. I have been slowly scaling a product over the past 12 years, on income alone, so I guess I see things differently, but I could never allow such a ridiculous spend on something so trivial reach even 1% of this level before squashing it.
I'm just kind of confused what took them so long. So it was costing 300k a year, plus causing deployment headaches, etc.
But its a realitively simple tool from the looks of it. It seems like their are many competitors, some already written in go.
Its kind of weird why they waited so long to do this. Why even need AI? This looks like the sort of thing you could port by hand in less than a week (possibly even in a day).
Not saying it is a good thing, but an organization, especially if there has been a lot of turnover, can enter a state of status quo.
> it must have that architecture for a reason, we don't enough knowledge about it to touch it, etc.
That or they simply haven't had the time, cost can creep up over time. 300k is a lot though. Especially for just 200 replicas.
Seems wildly in-efficient. I also don't understand why you wouldn't just bundle these with the application in question. Have the go service and nodejs service in the same pod / container. It can even use sockets, it should be pretty much instant (sub ms) for rpc between them.
If they were paying $500k/year, why haven't they paid someone to rewrite it? Surely would be cheaper still.
But above everything else, this is a great example of how much JavaScript inefficiency actually costs us, as humanity. How many companies burn money through like this?
On top of that there are probably a few more hits for the containers, vm and hypervisor, all those pods have monitoring etc. All the layers of abstraction are just stacks of turtles giving the illusion of being easier but adding complexity and cost/overhead.
It is a security product, so unless they want to deal with the exfiltration charges on the data it's probably better to keep it in AWS. Thats the nasty double edge sword of "cloud", and how we're all getting locked in.
All the bits on their own seem to make perfect sense, but it's become apparent that the orchestra has been blind folded and given noise canceling head phones.
how many billions of compute are wasted because this industry can't align on some binary format across all languages and APIs and instead keep serializing and deserializing things
ASN.1 and its on the wire format BER and DER have been available for close to 30+ years and it is running on billions of devices(cryptography, SSL, etc) and other critical infrastructures.
but, it is very boring stable, which means I can't tell the world about my wartime stories and write a blog about it.
JSON is not really the core issue which is the expression parser. "user.name = foo and user.id > 1000". Even if you were operating on binary data, turning an arbitrary pseudocode string into actual function logic + executing it would be the slow part.
Huh, I just did basically the same thing. My requirements were not due to spending $300k/yr on parsing (lol), but I was amazed how far I got just asking the AI for progressively more functionality.
My use case is a bit different. I wanted JSONata as the query language to query Flatbuffers data (via schema introspection) in Rust, due to its terseness and expressiveness, which is a great combination for AI generated queries.
> The approach was the same as Cloudflare’s vinext rewrite: port the official jsonata-js test suite to Go, then implement the evaluator until every test passes.
This makes me wonder, for reimplementation projects like this that aren't lucky enough to have super-extensive test suites, how good are LLM's at taking existing code bases and writing tests for every single piece of logic, every code path? So that you can then do a "cleanish-room" reimplementation in a different language (or even same language) using these tests?
Obviously the easy part is getting the LLM's to write lots of tests, which is then trivial to iterate until they all pass on the original code. The hard parts are how to verify that the tests cover all possible code paths and edge cases, and how to reliably trigger certain internal code paths.
I've found Claude Code with Opus 4.5+ to be excellent at generating test cases that exercise the different features, and even push into the edge cases. You sometimes need to nudge it into generating more convoluted cases when necessary, but then it is just nudging. I now routinely generate more LOCs of test cases than actual core code, while I used to only write very limited test cases just for the most complex areas amenable to automated testing.
I've been successful at using Claude Code this way:
1. get it to generate code for complex data structures in a separate library project
2. use the code inside a complex existing project (no LLM here)
3. then find a bug in the project, with some fuzzy clues as to causes
4. tell CC about the bug and ask it to generate intensive test cases in the direction of the fuzzy clues
5. get the test cases to reproduce the bug and then CC to fix it by itself
6. take the new code back to the full project and see the issue fixed
All this using C++. I've been a pretty intensive developer for ~35 years. I've done this kind of thing by hand a million times, not any more. We really live in the future now.
Anyone who ships a k8s cluster to make a JS library available over RPC needs to have a long hard look in the mirror. Should have bundled node, quickjs, anything into the go nodes for the first pass. k8s truly is a cancer for many teams.
Everyone is surprised at the $300k/year figure, but that seems on the low end. My previous work place spends tens of millions a year on GPU continuous integration tests.
A principal engineer spending his week end vibe coding some slop at a rate of 13k lines of code in 7h to replace a vendor. Is this really the new direction we want to set for our industry? For the first time ever, I have had a CTO vibe conding something to replace my product [1] even though it cost less than a day of his salary. The direction we are heading makes me want to quit, all points to software now being worthless
What vendor? My understanding is that they replaced one piece of software with similar one that allows them to simplify system and save a lot of money.
And looks like they are happy with quality and have a good test coverage.
In AI era not everything should be npm dependency or 3rd party. Small things are easier to make in house and tailor to one’s needs.
> Is this really the new direction we want to set for our industry?
I think the better question is whether it’s avoidable. I share the concern but is there a real alternative? “Say no to AI!” is fine until your competitors decide they don’t share your concerns. Or at least not enough to stop using it.
Congrats to the team. Unfortunately many comments here are missing the big picture by attacking the previous architectural decisions with no context about why they were taken. It's always easy to say so in retrospect.
Also, I have to comment on the many commenters that spent time researching existing Go implementations just to question everything, because "AI bad". I don't know how much enterprise experience the average HN commenter these days have, but it's not usually easy to simply swap a library in a production system like that, especially when the replacement lib is outdated and unmaintened (which is the case here). I remember a couple of times I was tasked with migrating a core library in a production system only to see everything fall apart in unexpected ways the moment it touched real data. Anyway, the case here seems to be even simpler: the existing Go libs, apart from being unmaintened and obscure, don't support current feature of the JSONata 2.x, which gnata does. Period.
The article missed anticipating such critics and explaining this in more detail, so that's my feedback to the authors. But congrats anyway, this is one of the best use cases for current AI coding agents.
> The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC.
> This was costing us ~$300K/year in compute
Wooof. As soon as that kind of spend hit my radar for this sort of service I would have given my most autistic and senior engineer a private office and the sole task of eliminating this from the stack.
At any point did anyone step back and ask if jsonata was the right tool in the first place? I cannot make any judgements here without seeing real world examples of the rules themselves and the ways that they are leveraged. Is this policy language intentionally JSON for portability with other systems, or for editing by end users?
These "solutions" place a lot of faith in a "complete" set of test cases. I'm not saying don't do this, but I'd feel more comfortable doing this plus hand-generating a bunch of property tests. And then generating code until all pass. Even better, maybe Claude can generate some / most of the property tests by reading the standard test suite.
Well they also shadowed production traffic and fixed some bugs that were causing mismatching results. Not saying that stuff can't still slip through, but it's a good way to evaluate it against real data in a way you can't from just test cases alone
These articles remind me so much of those old internet debates about "teleportation" and consciousness.
Your physical form is destructively read into data, sent via radio signal, and reconstructed on the other end. Is it still you? Did you teleport, or did you die in the fancy paper shredder/fax machine?
If vibe code is never fully reviewed and edited, then it's not "alive" and effectively zombie code?
As long as you are using JSON, you will be able to optimize.
Did you know that you can pass numbers up to 2 billion in 4 constant bytes instead of as a string of 20 average dynamic bytes? Also, fun fact, you can cut your packets in half by not repeating the names of your variables in every packet, you can instead use a positional system where cardinality represents the type of the variable.
And you can do all of this with pre AI technology!
Like other commenters already said, there are numerous ways they could have avoided/reduced the $500k/yr cost pre LLM, including simply paying someone to do port the code.
The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture:
> The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, evaluate, serialize the result, and finally send it back.
> This was costing us ~$300K/year in compute, and the number kept growing as more customers and detection rules were added.
For something so core to the business, I'm baffled that they let it get to the point where it was costing $300K per year.
The fact that this only took $400 of Claude tokens to completely rewrite makes it even more baffling. I can make $400 of Claude tokens disappear quickly in a large codebase. If they rewrote the entire thing with $400 of Claude tokens it couldn't have been that big. Within the range of something that engineers could have easily migrated by hand in a reasonable time. Those same engineers will have to review and understand all of the AI-generated code now and then improve it, which will take time too.
I don't know what to think. These blog articles are supposed to be a showcase of engineering expertise, but bragging about having AI vibecode a replacement for a critical part of your system that was questionably designed and costing as much as a fully-loaded FTE per year raises a lot of other questions.
>If they rewrote the entire thing with $400 of Claude tokens it couldn't have been that big.
It was "A few iterations and some 7 hours later - 13,000 lines of Go with 1,778 passing test cases."
I mostly agree, but it's more appropriate to weigh contributions against an FTE's output rather than their input. If I have a $10m/yr feature I'm fleshing out now and a few more lined up afterward, it's often not worth the time to properly handle any minor $300k/yr boondoggle. It's only worth comparing to an FTE's fully loaded cost when you're actually able to hire to fix it, and that's trickier since it takes time away from the core team producing those actually valuable features and tends to result in slower progress from large-team overhead even after onboarding. Plus, even if you could hire to fix it, wouldn't you want them to work on those more valuable features first?
They were running a big kubernetes infrastructure to handle all of these RPC calls.
That takes a lot of engineer hours to set up and maintain. This architecture didn't just happen, it took a lot of FTE hours to get it working and keep it that way.
But that k8s engineer's cost is spread over all the functions the cluster is doing, not just the rpc setup.
Kube is trivial to run. You hit a few switches on GKE/EKS and then a few simple configs. It doesn't take very many engineer hours to run. Infrastructure these days is trivial to operate. As an example, I run a datacenter cluster myself for a micro-SaaS in the process of SOC2 Type 2 compliance. The infra itself is pretty reliable. I had to run some power-kill sims before I traveled and it came back A+. With GKE/EKS this is even easier.
Over the years of running these I think the key is to keep the cluster config manual and then you just deploy your YAMLs from a repo with hydration of secrets or whatever.
Yeah, the situation from TFA doesn't make a lot of sense; I was just highlighting that it's not as clear-cut as "costs > 1 FTE => fix it."
Yeah, it's like those posts "we made it 5,000x faster by actually thinking about what the code is doing."
Exactly. Reddit did one last year like: “We migrated from python to golang and fixed a bunch of non-performant SQL queries. It was so fast, isn’t golang awesome?”
I was once asked to migrate a Microsoft Access application to C#/MS SQL Server because it was too slow. I just added a few database indexes to make it an order of magnitude faster.
(They still wanted to go ahead with the migration, but that's a different story.)
I have about a dozen projects I’d love to tackle in this vein. (Not as low hanging fruit, but enough effort they’re languishing in the backlog.) we’ll actually be able to get to more those projects with agents and good specs
In my experience, a lot of these types of migrations aren't incredibly deep in terms of actual code being written. It's about being able to assess all of the affected facets accurately. Once that's all mapped out, it's pretty straight forward to migrate.
I wonder how much it would have cost them if they weren't paying cloud rates for all of that, and they kept the same general inefficient architecture, sans the Kubernetes bloat.
Doubt they'd have a blog post to write about that, though.
> If they rewrote the entire thing with $400 of Claude tokens it couldn't have been that big.
The original is ~10k lines of JS + a few hundred for a test harness. You can probably oneshot this with a $20/month Codex subscription and not even use up your daily allowance.
You aren’t accounting for managerial politics. A product manager won’t gamble on a large project to lower operating cost, when their bonus is based on customer acquisition metrics.
The original author said he built this on the weekend, so my assumption is that this was something engineers had advocated for before but were shut down because management wanted them elsewhere.
The use of ai agents allowed them to shrink the problem down to the point where it was small enough to fit in their free time and not interrupt their assigned work.
I was thinking the same - if JSONata was a priority for them, why not choose a language with good support, like JS or Java? OTOH if development language was a priority why not choose a format that is well supported in it?
No offence, but inexperienced JS fanatics always do this because of some weird affectionado they have for the language itself. Otherwise, even a decently qualified CTO would have chosen to keep everything in Go from the beginning or might have not waited until they were bleeding $300k. JS is also the worst possible language choice for this problem. So, it definitely sounds a bunch of script kiddies with fancy titles bought with VC money rather than actual experience.
I've seen it happen and it's usually just Normalization of Deviance in an organization that is focusing on something else. Someone needs some kind of functionality and Kube makes creating services trivial so they launch it into a different service[0]. Over time, while people are working on important things this thing occasionally has load issues so someone goes and bumps the maxReplicas up periodically. Eventually you come back to it a year later and maxReplicas is at 24 and you've removed the code paths for almost everything that is hitting the server except some inexplicable hot-loop.
Then you look at it and you're like "Jesus! What the fuck, I meant to have this be a stop-gap". I've done as bad when at near 100% duty-cycle. Often you're targeting just the primary thing that's blocking some revenue and if you get caught yak-shaving you're screwed. A year ago, I did one of these things because I was in the middle of two projects that were blocking a potential hundred-million in revenue.
A year down the line, Claude Opus 4.6 could have live-solved it. But Claude of that time would have required some time and attention and I was doing something else.
That engineering team is some 15 people strong and the company is at $400m+ revenue. If you saw the code, you'd wonder why anyone would have done something like this.
0: I once did this because some inscrutable code/library was tying us to an old runtime so I just encapsulated it in HTTP and moved it into a service.
I've been refactoring stuff with a $20 ChatGPT account.
I've been refactoring stuff with anonymous ChatGPT usage..!
Think this is pure piggyback marketing on what cloudflare did with next.js. In my experience a company that raised $30MM a month ago is extremely unlikely to be investing energy in cost rationalization/optimization.
edit: saw the total raise not the incremental 30MM
Completely agree. We have > $50m from our most recent funding round, and even a cloud expense of $50k/year (in our case for storage) is considered a high priority to address. If it was $300k, our CTO would be running around with a butane torch setting everyone’s hair on fire until the problem was resolved.
But, venture funding does create a lot of weird inefficiencies which vary from company to company.
> At Reco, we have a policy engine that evaluates JSONata expressions against every message in our data pipeline - billions of events, on thousands of distinct expressions.
The original architecture choice and price almost gave me a brain aneurysm, but the "build it with AI" solution is also under-considered.
This looks like a perfect candidate for existing, high quality, high performance, production grade solutions such quamina (independent successor to aws/event-ruler, and ancestor to quamina-rs).
There's going to be a lot of "we were doing something stupid and we solved it by doing something stupid with AI [LLM code]" in our near future. :-|
But if the ai built solution is slightly less stupid, then it's still a win isnt it?
but they saved $500k. Before some humans knew about constraints in it. Now nobody knows.
Jokes aside, we will probably see everyone doing this, trying to remove human hands off of code, because they corrupt and AI does not.
Joke jokes aside why did we even code until AI?
Some background on one of the other two golang implementations mentioned in the comments.
Years ago I hired an Upwork contractor to port v1.5.3 to golang as best he could. He did a great job and it served us well, however it was far, far from perfect and it couldn't pass most of the JS test suite. The worst was that it had several recursion bugs that could segfault with bad expressions.
That was the now-deprecated implementation at
https://github.com/blues/jsonata-go
Early in 2025 I used Claude Code and Codex to do a proper, compliant port that passes the full set of tests and is safe. It was most certainly not a trivial task for AI, as many nuances of JSONata syntax derive from its JS roots.
Regardless, it was a great experience and here's the 2.0.6 AI port, along with a golang exerciser that lets you flip back and forth between the implementations. We did a seamless migration and it's been running beautifully in prod in Blues' Notehub for quite a while - as a core transformation capability used by customers in our JSON message pipeline.
https://github.com/jsonata-go/jsonata
>The approach was the same as Cloudflare’s vinext rewrite: port the official jsonata-js test suite to Go, then implement the evaluator until every test passes.
the first question that comes to mind is: who takes care of this now?
You had a dependency with an open source project. now your translated copy (fork?) is yours to maintain, 13k lines of go. how do you make sure it stays updated? Is this maintainance factored in?
I know nothing about JSONata or the problem it solves, but I took a look at the repo and there's 15PRs and 150 open issues.
That's only important if the plan is to stay feature-compatible with the original going forward.
For this case, where it's used as an internal filtering engine, I expect the goal is fixing bugs that show up and occasionally adding a feature that's needed by this organization.
>expect the goal is fixing bugs that show up and occasionally adding a feature that's needed by this organization.
Even if we assume a clean and bug free port, and no compatibility required moving forward, and a scope that doesn't involve security risks, that's already non trivial, since it's a codebase no one has context of.
Probably not 500k worth of maintainance (because wtf were they doing in the first place) but I don't buy placing the current cost at 0.
This case looks like pure marketing fluff rather than sound engineering tho.
it is all yolo from here on out ... every major ai decision we're making today feels like a bet that agi will eventually show up and clean up the mess
There is a choice, yet.
The full translation took 7hrs and $400 in tokens. Applying diffs every quarter using AI is much easier and cheaper. Software engineering has completely changed.
except there are 2 go implementations already, and he burnt 500k per year to have a kubernetes clusters to parse json (???), so the total gain is -500000*year - 400 + 1 (deducting prompt to use existing implementation)
> the first question that comes to mind is: who takes care of this now?
probably another AI agent at their company, who I'm sure won't make any mistakes
I mean, my first question would be how good the test suite on this project is.
The headline seems to be flashy indeed, but ai didn't really solve this imo.
They just seemed to fix their technology choices and got the benefits.
There's existing golang versions of jsonata, so this could have been achieved with those libraries too in theory. There's nothing written about why the existing libraries aren't good enough and why a new one needed to be written. Usually you need to do some due diligence in this area, but no mentions of it in this post
In order to measure the real efficiency, gnata should've been benchmarked against the existing golang libraries. For all we know, the ai implementation is much slower.
The benchmarks in the blog are also weird. The measurement is done within the app, but you're meant to measure the calls within the library itself (e.g calling the js version in its isolated benchmark vs go version in its isolated benchmark). So you don't actually know what the actual performance of the ai written version is?
The only benefit, again, is that they fixed their existing bad technology choice, and based on what is observed, with a lesser bad technology choice. Then it's layered with clickbait marketing titles for others to read.
I'll probably need to expect more of these types of posts in the future.
> There's existing golang versions of jsonata, so this could have been achieved with those libraries too in theory
The only one I found (jsonata-go) is a port of JSONata 1.x, while the gnata library they've published is compatible with the 2.x syntax. Guess that's why.
Looking at the releases, it looks like JSONata's 2.1.0 release from July 2025 added the `?:` and `??` syntax, and there hasn't been an update to the syntax since January 2020's 1.8.0 release that added `%`
The docs indicate there are already 2 other go implementations. Why not just use one of those? https://docs.jsonata.org/overview.html
Because his prompt said to implement in go, not to check if an go implementation already exists. They have been running kubernetes clusters to parse json, this is not suprising.
Those are compatible with the 1.x syntax while the gnata is compatible with the 2.x. Also, the repos haven't seen new commits in a long time.
Because otherwise they wouldn't have written this meaningless article and contributed to the AI hype.
And to market their AI security product.
Rewrites happen because nobody wants to debug someone else's half-finished mess, and "just use X" often means inheriting its quirks and gaps.
Last commits in those repos are 5 and 7 years ago.
If they're vendoring the dependency anyway, that wouldn't matter much if they're not using features that were added since 2021.
The last release of jsonata was mid 2025, and there hasn't been new features since the last 2022 release until the latest, so it's likely those other ports are fine.
> This was costing us ~$300K/year in compute, and the number kept growing as more customers and detection rules were added.
Maybe I’m out of touch, but I cannot fathom this level of cost for custom lambda functions operating on JSON objects.
They said in the article that they were running up to 200 pods at a time. Doing some back of the envelope math, 200 pods at $300,000 year is about $0.17/hour, which is exactly what an EC2 c5.xlarge costs per hour (on demand). That has 4 vCPUs, so about 800 vCPUs during peak, with $0.0425/CPU-hour.
I do have some questions like:
* Did they estimate cost savings based on peak capacity, as though it were running 24x7x365?
* Did they use auto scaling to keep costs low?
* Were they wasting capacity by running a single-threaded app (Node-based) on multi-CPU hardware? (My guess is no, but anything is possible)
This is a helpful breakdown, thanks, @otterley.
It is, by orders of magnitude, larger than any deployment that I have been a part of in my work experience, as a 10-year data scientist/Python developer.
This is where the cost came from.
>The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, evaluate, serialize the result, and finally send it back.
But either way, we're talking $25k/mo. That's not even remotely difficult to believe.
First I thought they were AWS lambda functions, perhaps possible if they are over-provisioned for very concurrency or something similar $25k/month is in realm of possibility.
But no, the the post is talking about just RPC calls on k8s pods running docker images, for saving $300k/year, their compute bill should be well above $100M/year.
Perhaps if it was Google scale of events for billions of users daily, paired with the poorest/inefficient processing engine, using zero caching layer and very badly written rules, maybe it is possible.
Feels like it is just an SEO article designed to catch reader's attention.
It has to be satire right? Like, you aren't out of touch on this. I get engineers maybe making the argument that $300k / year on cloud is the same as 1.5 devops engineers managing in-house solutions, but for just json parsing????
For numbers like that, I can never tell whether it's just a vastly larger-scale dataset than any that I've seen as a non-FAANG engineer, OR, a hilariously-wasteful application of "mAnAgEd cLoUd sErViCeS" to a job that I could do on a $200/month EC2 instance with one sinatra app running per core. This is a made-up comparison of course, not a specific claim. But I've definitely run little $40 k8s clusters that replaced $800/month paid services and never even hit 60% CPU.
Right, this is roughly my mental situation, too. I guess that streaming JSON things can eat up compute way faster than I had any intuition for!
I wonder if you've ever worked on a web service at scale. JSON serialization and deserialization is notoriously expensive.
They got a 1000x speed up just by switching languages.
I highly doubt the issue was serialization latency, unless they were doing something stupid like reserializing the same payload over and over again.
Well, for starters, they replace the RPC call with an in-process function call. But my point is anybody who's surprised that working with JSON at scale is expensive (because hey it's just JSON!) shouldn't be surprised.
Well everything is expensive at scale, and any deserialization/serialization step is going to be expensive if you do it enough. However yes i would be surprised. JSON parsing is pretty optimized now, i suspect most "json parsing at scale is expensive" is really the fault of other parts of the stack
It can be, but $500k/year is absurd. It's like they went from the most inefficient system possible to create, to a regular normal system that an average programmer could manage.
I have no idea if they are doing orders of magnitude more processing, but I crunch through 60GB of JSON data in about 3000 files regularly on my local 20-thread machine using nodejs workers to do deep and sometimes complicated queries and data manipulation. It's not exactly lightning fast, but it's free and it crunches through any task in about 3 or 4 minutes or less.
The main cost is downloading the compressed files from S3, but if I really wanted to I could process it all in AWS. It also could go much faster on better hardware. If I have a really big task I want done quickly, I can start up dozens or hundreds of EC2 instances to run the task, and it would take practically no time at all... seconds. Still has to be cheaper than what they were doing.
Would it be better or worse if I had that experience and still said it's stupid?
You didn't say it was stupid. If you had, I would have just ignored the comment. But you expressed a level of surprised that led me to believe you're unfamiliar with how much of a pain in the ass JSON parsing is.
I think OP’s point was surprise that a company would spend so much on such inefficient json parsing. I’m agreeing. I get that JSON is not the fastest format to parse, but the overarching point is that you would expect changes to be made well before you’re spending $300k on it. Or in a slightly more ideal world, you wouldn't architect something so inefficient in the first place.
But it's common for engineers to blow insane amounts of money unnecessarily on inefficient solutions for "reasons". Sort of reminds me of saas's offering 100 concurrent "serverless" WS connections for like $50 / month - some devs buy into this nonsense.
Congrats! This author found a sub-optimal microservice and replaced it with inline code. This is the bread and butter work of good engineering. This is also part of the reason that microservices are dangerous.
The bad engineering part is writing your own replacement for something that already exists. As other commenters here have noted, there were already two separate implementations of JSONata in Go. Why spend $400 to have Claude rewrite something when you can just use an already existing, already supported library?
This isn’t the first time I’ve read a ridiculous story like this on hackernews. It seems to be a symptom of startups who suddenly get a cash injection with no clue how to properly manage it. I have been slowly scaling a product over the past 12 years, on income alone, so I guess I see things differently, but I could never allow such a ridiculous spend on something so trivial reach even 1% of this level before squashing it.
I'm just kind of confused what took them so long. So it was costing 300k a year, plus causing deployment headaches, etc.
But its a realitively simple tool from the looks of it. It seems like their are many competitors, some already written in go.
Its kind of weird why they waited so long to do this. Why even need AI? This looks like the sort of thing you could port by hand in less than a week (possibly even in a day).
Not saying it is a good thing, but an organization, especially if there has been a lot of turnover, can enter a state of status quo.
> it must have that architecture for a reason, we don't enough knowledge about it to touch it, etc.
That or they simply haven't had the time, cost can creep up over time. 300k is a lot though. Especially for just 200 replicas.
Seems wildly in-efficient. I also don't understand why you wouldn't just bundle these with the application in question. Have the go service and nodejs service in the same pod / container. It can even use sockets, it should be pretty much instant (sub ms) for rpc between them.
If I had to guess… The same thing happening to a lot of the industry… the era of cheap money is over.
For context, JSONata's reference implementation is 5.5k lines of javascript.
So it doubled LOC
Go is very verbose.
Golang is a bit more basic and explicit.
If they were paying $500k/year, why haven't they paid someone to rewrite it? Surely would be cheaper still.
But above everything else, this is a great example of how much JavaScript inefficiency actually costs us, as humanity. How many companies burn money through like this?
On top of that there are probably a few more hits for the containers, vm and hypervisor, all those pods have monitoring etc. All the layers of abstraction are just stacks of turtles giving the illusion of being easier but adding complexity and cost/overhead.
It is a security product, so unless they want to deal with the exfiltration charges on the data it's probably better to keep it in AWS. Thats the nasty double edge sword of "cloud", and how we're all getting locked in.
All the bits on their own seem to make perfect sense, but it's become apparent that the orchestra has been blind folded and given noise canceling head phones.
Next maybe they will use a binary format instead of JSON.
Stop reading ahead.
> Correctness: 1,778 test cases from the official jsonata-js test suite + 2,107 integration tests in the production wrapper.
The AI generated code can still introduce subtle bugs that lead to incorrect behaviour.
One example of this is the introduction of functions into the codebase (by AI) that have bugs but no corresponding tests.
EDIT: correct quotation characters
The moment the amount of savings surpassed the annual salary of a good programmer you know you made the wrong investment.
how many billions of compute are wasted because this industry can't align on some binary format across all languages and APIs and instead keep serializing and deserializing things
ASN.1 and its on the wire format BER and DER have been available for close to 30+ years and it is running on billions of devices(cryptography, SSL, etc) and other critical infrastructures.
but, it is very boring stable, which means I can't tell the world about my wartime stories and write a blog about it.
JSON is not really the core issue which is the expression parser. "user.name = foo and user.id > 1000". Even if you were operating on binary data, turning an arbitrary pseudocode string into actual function logic + executing it would be the slow part.
Huh, I just did basically the same thing. My requirements were not due to spending $300k/yr on parsing (lol), but I was amazed how far I got just asking the AI for progressively more functionality.
My use case is a bit different. I wanted JSONata as the query language to query Flatbuffers data (via schema introspection) in Rust, due to its terseness and expressiveness, which is a great combination for AI generated queries.
https://github.com/blues/jsonata-go hmmm
> The approach was the same as Cloudflare’s vinext rewrite: port the official jsonata-js test suite to Go, then implement the evaluator until every test passes.
This makes me wonder, for reimplementation projects like this that aren't lucky enough to have super-extensive test suites, how good are LLM's at taking existing code bases and writing tests for every single piece of logic, every code path? So that you can then do a "cleanish-room" reimplementation in a different language (or even same language) using these tests?
Obviously the easy part is getting the LLM's to write lots of tests, which is then trivial to iterate until they all pass on the original code. The hard parts are how to verify that the tests cover all possible code paths and edge cases, and how to reliably trigger certain internal code paths.
I've found Claude Code with Opus 4.5+ to be excellent at generating test cases that exercise the different features, and even push into the edge cases. You sometimes need to nudge it into generating more convoluted cases when necessary, but then it is just nudging. I now routinely generate more LOCs of test cases than actual core code, while I used to only write very limited test cases just for the most complex areas amenable to automated testing.
I've been successful at using Claude Code this way:
1. get it to generate code for complex data structures in a separate library project
2. use the code inside a complex existing project (no LLM here)
3. then find a bug in the project, with some fuzzy clues as to causes
4. tell CC about the bug and ask it to generate intensive test cases in the direction of the fuzzy clues
5. get the test cases to reproduce the bug and then CC to fix it by itself
6. take the new code back to the full project and see the issue fixed
All this using C++. I've been a pretty intensive developer for ~35 years. I've done this kind of thing by hand a million times, not any more. We really live in the future now.
Why not use FFI from Go to something in C/C++ that is faster than Go's JSON stuff?
Anyone who ships a k8s cluster to make a JS library available over RPC needs to have a long hard look in the mirror. Should have bundled node, quickjs, anything into the go nodes for the first pass. k8s truly is a cancer for many teams.
Everyone is surprised at the $300k/year figure, but that seems on the low end. My previous work place spends tens of millions a year on GPU continuous integration tests.
The $300K/year figure is surprising because it was for something that didn't need to exist (RPC calls).
I mean, great, but which CTO gave greenlight to such a weird architectural choice. Sorry for the rant!
If you can incorporate Quamina or similar logic in there, you might be able to save even more… worth looking into, at least
So they used an ai trained on the original source code to "rewrite" the original source code.
It was trained on the two existing open source Go implementations of JSONata.
A principal engineer spending his week end vibe coding some slop at a rate of 13k lines of code in 7h to replace a vendor. Is this really the new direction we want to set for our industry? For the first time ever, I have had a CTO vibe conding something to replace my product [1] even though it cost less than a day of his salary. The direction we are heading makes me want to quit, all points to software now being worthless
[1] https://github.com/mickael-kerjean/filestash
What vendor? My understanding is that they replaced one piece of software with similar one that allows them to simplify system and save a lot of money. And looks like they are happy with quality and have a good test coverage. In AI era not everything should be npm dependency or 3rd party. Small things are easier to make in house and tailor to one’s needs.
> Is this really the new direction we want to set for our industry?
I think the better question is whether it’s avoidable. I share the concern but is there a real alternative? “Say no to AI!” is fine until your competitors decide they don’t share your concerns. Or at least not enough to stop using it.
> then pointed AI at it and had it implement code until every test passed.
You used to have two problems. Now you have three.
Darn, I'd wished they improved one of the existing Go or Rust implementations.
AI company selling AI products claims to have solved a problem using AI when it could've solved it with better code and engineering foundations
Congrats to the team. Unfortunately many comments here are missing the big picture by attacking the previous architectural decisions with no context about why they were taken. It's always easy to say so in retrospect.
Also, I have to comment on the many commenters that spent time researching existing Go implementations just to question everything, because "AI bad". I don't know how much enterprise experience the average HN commenter these days have, but it's not usually easy to simply swap a library in a production system like that, especially when the replacement lib is outdated and unmaintened (which is the case here). I remember a couple of times I was tasked with migrating a core library in a production system only to see everything fall apart in unexpected ways the moment it touched real data. Anyway, the case here seems to be even simpler: the existing Go libs, apart from being unmaintened and obscure, don't support current feature of the JSONata 2.x, which gnata does. Period.
The article missed anticipating such critics and explaining this in more detail, so that's my feedback to the authors. But congrats anyway, this is one of the best use cases for current AI coding agents.
> The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC.
> This was costing us ~$300K/year in compute
Wooof. As soon as that kind of spend hit my radar for this sort of service I would have given my most autistic and senior engineer a private office and the sole task of eliminating this from the stack.
At any point did anyone step back and ask if jsonata was the right tool in the first place? I cannot make any judgements here without seeing real world examples of the rules themselves and the ways that they are leveraged. Is this policy language intentionally JSON for portability with other systems, or for editing by end users?
Your most autistic and senior engineer is now named Claude. Point him at nearly any task, pair-program with codex, and review the results.
These "solutions" place a lot of faith in a "complete" set of test cases. I'm not saying don't do this, but I'd feel more comfortable doing this plus hand-generating a bunch of property tests. And then generating code until all pass. Even better, maybe Claude can generate some / most of the property tests by reading the standard test suite.
Well they also shadowed production traffic and fixed some bugs that were causing mismatching results. Not saying that stuff can't still slip through, but it's a good way to evaluate it against real data in a way you can't from just test cases alone
These articles remind me so much of those old internet debates about "teleportation" and consciousness.
Your physical form is destructively read into data, sent via radio signal, and reconstructed on the other end. Is it still you? Did you teleport, or did you die in the fancy paper shredder/fax machine?
If vibe code is never fully reviewed and edited, then it's not "alive" and effectively zombie code?
As long as you are using JSON, you will be able to optimize.
Did you know that you can pass numbers up to 2 billion in 4 constant bytes instead of as a string of 20 average dynamic bytes? Also, fun fact, you can cut your packets in half by not repeating the names of your variables in every packet, you can instead use a positional system where cardinality represents the type of the variable.
And you can do all of this with pre AI technology!
Neat trick huh?
I too have used a similar strategy of packing variables together. I even came up with a name for it. I called it a "building."
Like other commenters already said, there are numerous ways they could have avoided/reduced the $500k/yr cost pre LLM, including simply paying someone to do port the code.
So I don't see there is any point in the article.
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