Compare
Excellent vs Braintrust: scoring model output vs proving code work
Braintrust evaluates your LLM app. Excellent verifies the code your agent wrote in your repo.
Braintrust
Braintrust in one line
An eval and tracing platform: experiments on datasets, side-by-side prompt and model comparison, turning production traces into datasets, plus the Loop agent and an MCP server.
- Best for
- Teams shipping an LLM feature who need to measure whether a prompt or model change made the product better.
- Limits
- Its scorers judge outputs — LLM, code or human graders against a dataset. That is the right instrument for an LLM application and the wrong one for “did this commit break the build”.
- Stronger than us at
- Eval infrastructure, at a scale and generality we do not attempt: datasets, experiment tracking, human review queues, side-by-side model comparison and serious volume. If you are building an LLM product, Braintrust is the better fit, and it is not close.
- Price 2026-09-23
- $249 / mo (Pro)
Excellent
Excellent in one line
A verification system for agentic tasks — it records what the agent attempted, runs your checks against the claim, and binds the result to evidence you can open.
- Best for
- Teams that want agent work checked before they trust or promote it.
- Limits
- There is no hosted option. You run it on your machines. Verification evidence stays on the machine that produced it — it does not sync to teammates yet. No SSO, and no Mac app has shipped. It does not write tests, and it does not review your diff.
- Stronger than them at
- Keeping the attempt record, running your own checks as the oracle, and trialling prompt, model and tool changes on held-out work from your repository.
- Price 2026-09-23
- Free to install and run (Consulting from $10,000 / month)
Head to head
The capabilities that decide it
Capability by capability — Braintrust on the left, Excellent on the right. Prices are the vendor's own, read on 2026-09-23.
| Capability | Braintrust | Excellent |
|---|---|---|
Scores LLM outputs against a dataset | ||
What decides pass or fail | LLM, code or human scorers | Tests, builds, browser checks and CI |
Runs on your repository's own held-out work | Your datasets and traces | |
Records each agent attempt and the claim it made | Traces of LLM calls | Attempts, checks, results and receipts for code work |
General LLM tracing and observability | ||
Published price (checked 2026-09-23) | Starter $0 per month ($10 of credits, 1GB included then $4/GB, 10k scores then $2.50 per 1k, 14-day retention); Pro $249 per month ($100 of credits, 5GB then $3/GB, 50k scores then $1.50 per 1k, 30-day retention); Enterprise custom | Free to install and run. Consulting starts at $10,000 / month. |
Verdict
When to pick which
If you ship an LLM feature, buy Braintrust — we are not an eval platform and are not trying to become one. If you ship code an agent wrote, the thing you need is not a better judge model; it is a test that ran. Plenty of teams need both, for different systems.
Pick Braintrust if
Teams shipping an LLM feature who need to measure whether a prompt or model change made the product better.
Add Excellent if
You want agent work captured as attempts, checked against evidence you can open, and promoted only when the result is defensible to someone outside the team.
Still shopping? See the full list of Braintrust alternatives →
More matchups
See Excellent against the rest of the category
Trust the evidence, not the agent's confidence
Install Excellent, run it beside the agent you already use, and inspect what the checks actually said.