AI · alternatives · 2026
24 alternatives to ML Test Score Quiz
Quiz to rate the health of your production ML system
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. ML Test Score Quiz launched in 2021; newer entries below may have overtaken it.
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Monitor ML▲171Real-time production monitoring of ML models, made simple.
2019 · arize.com · its alternatives →
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Lightly▲148Improve your machine learning models by using the right data
2021 · lightly.ai · its alternatives →
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Open source tool for testing & validating your model & data
2022 · github.com · its alternatives →
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Is your data ready for AI, find out now
2022 · validator.obviously.ai · its alternatives →
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LLM Stats▲293I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Oct 2025 · llm-stats.com · its alternatives →
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- 8MM
2020 · mlpowered.com · its alternatives →
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FiftyOne▲75Accelerate your machine learning experimentation
2020 · docs.voxel51.com · its alternatives →
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Weave▲237Hey HN, We’re building Weave: an ML-powered tool to measure engineering output, that actually understands engineering output! Why? Here’s the thing: almost every eng leader already measures output - either openly or behind closed doors. But they rely on metrics like lines of code (correlation with effort: ~0.3), number of PRs, or story points (slightly better at ~0.35). These metrics are, frankly, terrible proxies for productivity. We’ve developed a custom model that analyzes code and its impact directly, with a far better 0.94 correlation. The result? A standardized engineering output…
2025 · app.workweave.ai · its alternatives →
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Aquarium▲103Improve ML models by improving datasets they’re trained on
2020 · aquariumlearning.com · its alternatives →
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Machine Box▲94Run, deploy & scale state of the art machine learning tech
2017 · machinebox.io · its alternatives →
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Hey HN, my name is Vikas, and my cofounders Rish, Gabe and I are building Openlayer: http://openlayer.com/ Openlayer is an ML testing, evaluation, and observability platform designed to help teams pinpoint and resolve issues in their models. We were ML engineers experiencing the struggle that goes into properly evaluating models, making them robust to the myriad of unexpected edge cases they encounter in production, and understanding the reasons behind their mistakes. It was like playing an endless game of whack-a-mole with Jupyter notebooks and CSV files — fix one issue and…
2023 · openlayer.com · its alternatives →
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- 18TM
Hey HN - spending time adding machine learning to your product is almost never worth it for startups. Formulating the problem, managing training data, training models, serving them, etc... for a 10% increase in some metric just isn't worth the time. Using rules + heuristics, although not optimal, does a pretty good job for a long time. There should be a better solution between rules & hardcore ML -- an 80/20 way to add ML to any product. Just slap it on top of the rules you already have, get that 10% gain, and never think about it again. I built an API that does just that. It's used by…
2021 · its alternatives →
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Qualifyed.AI▲80Train a ML model of your buyers, create better lookalikes
2023 · qualifyed.ai · its alternatives →
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How to approach a machine learning/AI project
2016 · soundcloud.com · its alternatives →
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A collection of tools to manage and organize ML projects.
2020 · its alternatives →
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ModelRed▲10Hi everyone, We built ModelRed to test AI models and apps for security issues. Ran 4,182 attack probes against 9 leading models to see what would break. Leaderboard: https://modelred.ai (no signup, just check it out) Claude scored 9.5/10 but still failed on medical/financial prompts. Mistral Large scored 3.3/10. The gap between best and worst is huge. We test for prompt injections, data leaks, jailbreaks, risky tool calls, domain specific hacks, basically everything that goes wrong when your LLM has access to real data and APIs. The platform runs these tests…
Nov 2025 · modelred.ai · its alternatives →
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Deep-ML▲13We just launched Deep-ML Labs, a new section on Deep-ML Each Lab is a hands-on challenge where you implement parts of real models, activations, layers, optimizers, normalization, even small transformers — directly in PyTorch (and sometimes NumPy). You can test your implementations on actual datasets, see how your results compare to others, and understand what’s really happening under the hood of deep learning frameworks. let me know what you guys think and if we should work on making more of these questions
2025 · deep-ml.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →