Confident AI vs Braintrust
If you have decided to run LLM evals, both of these are hosted platforms. They store results, track regressions, and give your team a UI to look at. Confident AI is the cloud platform from the DeepEval team, so it is the paid home for your DeepEval tests. Braintrust is a broader eval and logging platform that does not assume any one test framework. It brings its own SDKs, a playground, and production logging.
| Dimension | Confident AI | Braintrust |
|---|---|---|
| What it is | Hosted platform for DeepEval | General eval and logging platform |
| License | Commercial / hosted | Commercial / hosted |
| Tied to a framework | Built around DeepEval (open source) | Framework-agnostic SDK |
| You write evals in | DeepEval (Python) | Its own SDK (TypeScript and Python) |
| Metrics | DeepEval's metric library, including RAG metrics | Built-in scorers plus your own |
| Production logging | Tracing and monitoring included | Strong logging and observability |
| Prompt playground | Present, dataset-focused | A core strength |
| Best for | Teams already using DeepEval | Teams wanting one eval and logging home for any stack |
You already write your evals in DeepEval and want a managed place to store runs, share results, and watch for regressions without building that yourself. Confident AI is the natural upgrade path. It comes from the same team, and the local library maps straight onto the platform.
You want one platform for both offline evals and production logging, and you do not want to be tied to a specific test library. Braintrust is strong when your team works in TypeScript, or when the prompt-iteration playground and trace logging matter as much as the pass/fail gate.
Pick by where you already live. If your evals are DeepEval Python tests, Confident AI is the lowest-friction hosted layer and I would not shop around. If you are starting fresh, want TypeScript support, or care about tying evals to real production logs, Braintrust is more flexible. The common mistake is buying a platform before you have written any real test cases, so you pay for dashboards over an empty dataset. Write ten honest evals in the open-source tool first, then decide if you even need the hosted layer.