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- Hacker News
- The problem with eval is the fact that the information is not updating itself fast enough so that you want the latest model performance benchmarks. Bloomberg succeeded because it sells info that is expires in the next hour.by GL26
- I found this pretty hard to read as the author has a very specific understanding of what an eval startup means but it is only implied rather than explicitly described. I would have thought that they were referring to the companies that provide a technology platform to enable you to do evals in an AI application context for example companies like Comet/Opik and Braintrust.
But it sounds like the author does not mean those companies at all since those are actually important in enabling the very Venn diagram he/she describes.
Based on what I assume the author's referring to they are referring to something more like a public benchmark report provider... I would say but yes that's a relatively small total addressable Market space no matter how you look at it
by redwood - Funnily enough, this made immediate sense to me, and I think it derives from being a situation where you need high reliability from a process, eg: I need a bot which has a 99.99% guarantee to not go out of bounds or say something incorrect.by intended
- "Safety evals are an exception I believe eval startups can work when they're targeting safety benchmarks specifically. Researchers who want to work on safety evals tend to be ideologically opposed to working on capabilities, which means they don't migrate to post-training or applications due to monetary incentives."
This is quite interesting. Seems more relevant in 2026.
by yiyingzhang - Maybe it's not that valuable? No snark, but how much confidence do these evals provide?by nilirl
- Exactly this. I find most eval companies get torn in multiple directions and do not end up putting out useful data. Probably genuine value as a B2B/consulting style service but that quickly falls out of being a pure eval company.by alansaber
- Unfortunately, model quality is not the only criterion for users, and often not even the most important one. Adoption is also driven by marketing, UX, integrations, pricing, ecosystem, and a lot of other non-benchmark factors.
Also, model providers are not interested to have their models compared head-to-head under identical conditions. And “Model A is better than Model B” is almost meaningless by itself. Better for what task? With what prompt? What inputs? What budget? What failure tolerance?
It would be nice to have a place where users could run their own benchmarks, define evaluation criteria for their actual use cases, and make those runs verifiable by others.
by PashaGo - What's an eval?by theteapot
- To complement the excellent answers that I read in this thread: an eval is a test.
What makes it particular for the case of AI is:
- there are many situations where you can’t test using pattern matching
- you don’t only like to test correct answers but voice and tone too (imagine a bank support LLM-based chatbot that answers using slang)
- evals can be used to compare the performance of different implementations; given the costs of LLMs, it’s very important
- running evals is more expensive than running standard tests, because you rely on the LLM calls under test, and many times they use LLMs as a judge. It means that running them in every commit of your CI/CD is very expensive
- Knowing all the possible inputs for the LLM is impossible, so evals can also be run on runtime samples to detect anomalies
by diegof79 - IMHO - In an AI context an "eval" is answering the question - "Is this AI / LLM call helping me or is doing the right thing?"
AI is not deterministic like regular code, so imagine you use it for "search" (RAG) or for summarizing or for classifying emails etc. How do you know it is giving you the right results? In this context, AI evals are an important idea and very often neglected.
You can use an initial "dataset" to evaluate your prompt and AI calls + code (think test cases), this dataset will of-course be curated by humans. But as the software is used, you want to incorporate, real production data as well and run the evaluation pre and post launch. Sounds simple, but can get complicated specially since this area is new and as the post mentioned there are too many players and options out there (since everyone thought this is a money maker).
by rockyj - Evaluations of different implementations of a tech. Kind of like a meta service layer on top of an industry, such as "Which frontier model is best?"
I do agree that the author does not do a good job of introducing the term.
by choult - (Author) It's short for "evaluation", a test for an AI model. Specifically, an AI evaluation comprises (1) a dataset of prompts (as questions / tasks / queries), (2) some way to score model performance on each prompt, like a set of correct answers or a grading rubric that you can use with an LLM autograder, and (3) a metric, such as accuracy¹. (If you're already familiar with the term "benchmark", it's the same thing; for some reason the former has become the term of art in the past few years).
For example, a simple eval is a dataset of multiple-choice questions, which each have one correct answer, and scored by accuracy. An example of this kind of eval is the Massive Multitask Language Understanding benchmark (2020) (https://arxiv.org/abs/2009.03300).
A more complex eval is FrontierCode (2026). Questions in FrontierCode represent coding tasks needed for real-world repos and are evaluated against rubrics scoring for correctness, code quality, cleanliness, and other factors. https://cognition.com/blog/frontier-code.
¹Note that this is a slightly different definition we used in [0], which used a definition of a fixed input-output correspondence pairs combined with a metric. What's different from 2021 is: models are now given more open-ended inputs (prompts like "find the bug" and a codebase rather than a simple question), have freeform generation (rather than choosing a fixed answer), and are graded in a more complex manner (e.g. beyond correctness, one might care for a coding eval also to grade adherence to coding guidelines, test coverage, etc).
[0] Liao, T., Taori, R., Raji, I. D., & Schmidt, L. (2021, January). Are we learning yet? a meta review of evaluation failures across machine learning. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). https://thomasliao.com/are_we_learning_yet.pdf
by thomasliao - The way eval startup is defined here is very specific and doesn't cover successful eval farmwork/SaaS vendors like Arize, Promptfoo, deepeval, etc
The author does have a point around generic benchmarks not being super valuable for companies. But evals should be seen as verifying design/behaviour constraints and can greatly aid product building, golden dataset creations and good software practices.
It's just that the aim should be "how to generate your own good evals, even if it's hard" as not so much "here's some generic evals about models".
by alexhans - I have written a couple of eval harnesses to see how well LLMs drive software I have written. Basically I have data analysis software that I need LLMs to write code for. The code is complex, and I want to shape my APIs such that LLMs do a better job of quickly getting to the right answer. So I test different prompting and api surfaces, it's really easy to make quick gains this way and save your users from bugs. In this paradigm, I'm explicitly not testing different models, and I'm very interested to see how lesser models do with my software. Also for this type of testing, using the open weight models makes it faster, cheaper, and more reliable to test vs frontier models because I can trust that kimi-2.5-a-bunch-of-specs is going to behave more consistently than whatever tweaks Claude is making to Sonnet this week. API and prompting improvements seem to carry across the different models for gross improvements.
I haven't looked that hard, but I can't find articles about this type of eval testing, curious to hear if others have approached writing APIs in this way.
by paddy_m - "eval startups have a hard time finding customers, because clients have to be technical developers who want to build with APIs, but also not technical enough to run their own evals"
To add to this, they have to be developers who aren't already using a fullstack observability solution, since it's fairly straight forward to add the eval startup featureset to an existing observability solution, and easier (plus cost effective) to just keep it all in one place.
by dbish - Interesting. As someone who has written a fair amount of evals to test and benchmark my skills and tools, I am curious what observability solutions you might be referring to? /me heads off to ddg…by steinnes
- I built a simple (free) eval tool for my own uses (Github Gists + Model Outputs) after not being able to find a suitable one in the market.
The market's being split into
1. Longitudinal LLM observability tooling
Most eval startups have gone down the route of something more like being an observability platform for LLM inference. They want to be in your stack and running the inference to collect data on performance of it.
They collect things like how often a model returns JSON that's out of spec or returns values that aren't expected as well as general timing and cost info.
2. Safety Limiting / Pentesting
Say you're doing something in the medical field or that's sensitive in some way and you want to figure out what model has the best outputs for your task that won't fly off the guardrails.
3. Simple cost + performance + quality swapping
This is what my tool does, basically lets you test if you _really_ need to be running that frontier model in a loop across a million records or if you'd be better with an older model or something else.
Example eval: https://giyd8stidy.evvl.io
- Cool project! I haven't seen that OpenRouter workflow yet (sign into OpenRouter and it creates an API key that your app can use), that looks like an interesting pattern to investigate.
My company recently built a tool that is closer to your first category, but it's an API so it doesn't have the security (supply chain) concern of being embedded in your application.
It's built to help people manage the risk of LLMs changing underneath them and drifting from their designed behavior. Traditional deterministic testing probably won't be sufficient for apps that provide nondeterministic output, like a chatbot backed by an LLM.
The point in the linked article about the challenge of selling developer tools to developers is a good one. I think the first reaction to coding agents is "let's build everything ourselves!" but the long tail of maintenance is still there and the pendulum will probably swing back to "let's stick to our knitting."
by gavinboston