Shipping an LLM feature is the easy part — knowing whether it's actually good, and catching the moment a prompt or model swap quietly makes it worse, is the hard part. A whole category of “LLM eval” tools exists to close that gap: they let you run test datasets against your app, score outputs automatically, and block a bad release before it reaches users. Braintrust and Patronus AI are two of the more credible options, but they solve the problem differently — one is a general-purpose eval and observability loop, the other bets on purpose-built judge models instead of a generic LLM grading itself.
Braintrust
Braintrust is an AI observability and evaluation platform built around a trace-to-dataset loop: you capture real production traces, turn the interesting ones into eval datasets, score them, and set quality gates that can block a release if scores drop. It ships SDKs in five languages, so it fits into most existing stacks without much glue code.
For who: AI product teams and ML engineers who want one platform covering tracing, evaluation, and release gating together.
Price: free tier to start; Pro is $249/month plus usage (based on data volume, scores, and tokens); Enterprise is custom.
- Strengths: tight trace-to-dataset workflow that turns real production failures into regression tests; quality gates that can block a bad deploy automatically; SDKs in 5 languages for easy integration
- Limits: usage-based costs on top of the Pro tier are hard to forecast in advance; closed source with a proprietary datastore, so you can't self-host or inspect the internals; less specialized than Patronus AI for regulated-industry hallucination detection
Patronus AI
Patronus AI takes a different bet: instead of using a general-purpose LLM to grade another LLM's output (which inherits the same blind spots), it builds dedicated judge models trained specifically to catch hallucinations and factual errors. It backs this up with open benchmarks like FinanceBench and Lynx, published and checkable on GitHub rather than just claimed in a pitch deck.
For who: AI teams in regulated industries — finance, healthcare — that need hallucination detection they can actually trust, not just a generic similarity score.
Price: Developer tier is free plus pay-per-use API ($10 per 1,000 calls for small evaluators, $20 per 1,000 for large ones); Base is $25/month; Enterprise is quote-based (on-prem/VPC, SSO, custom fine-tuning).
- Strengths: purpose-built judge models instead of a generic self-grading LLM; open, independently checkable benchmarks (FinanceBench, Lynx) on GitHub; recent $50M Series B, a real signal of financial staying power
- Limits: the headline performance numbers are self-reported, not independently audited; more specialized — and potentially overkill — than a generalist platform like Braintrust if you don't need regulated-industry-grade hallucination detection; Series B founding date and investor list aren't publicly disclosed
Side-by-side
| Braintrust | Patronus AI | |
|---|---|---|
| Entry price | Free tier, Pro $249/mo + usage | Free + pay-per-use, Base $25/mo |
| Core approach | Trace-to-dataset eval loop + release gates | Dedicated judge models, not a self-grading LLM |
| Standout feature | Quality gates block bad releases automatically | Open, GitHub-verifiable benchmarks (FinanceBench, Lynx) |
| Backing | Established player in the space | $50M Series B (undisclosed investors) |
| Best fit | Teams wanting one platform for tracing + eval + gating | Regulated-industry teams needing trustworthy hallucination detection |
Pick Braintrust if you want a single platform that captures production traces, turns them into eval datasets, and can automatically stop a bad deploy — and you're fine with usage costs that scale with your traffic. Pick Patronus AI if your top concern is catching hallucinations reliably in a regulated context, and you'd rather trust a purpose-built judge model with published benchmarks than a general LLM grading itself. Both sit in a crowded, fast-moving field — Confident AI, built on the open-source DeepEval library, is worth a look too if you want non-technical teammates to review eval traces without writing code.