Braintrust vs PetalTrace

Braintrust is an observability platform for teams running AI agents in production. It lets teams inspect agent traces and tool calls while tracking latency, cost, and quality in real time. Users can evaluate datasets, compare prompts and models, and score outputs with LLMs, code, or human reviewers. Discovery tools surface patterns in production traces to help investigate agent behavior, and the Loop agent can run evaluations, generate test cases, and iterate on prompts autonomously. Braintrust lists native SDKs for Python, TypeScript, Go, Ruby, C#, and more, and its MCP server lets coding agents query logs, run evaluations, and update prompts from an IDE. Security options listed include SSO, RBAC, and hybrid deployment; Braintrust states that it is SOC 2 Type II certified and GDPR and HIPAA compliant. Starter is free, with 1 GB processed data, 10,000 scores, and 14-day retention. Pro costs $249.00 per month; Enterprise pricing is custom.

PetalTrace is an open-source observability platform for developers examining AI agent workflows. It captures prompts and completions, tool calls, token use, costs, and execution timelines, with a React web interface plus CLI, HTTP API, and MCP server access. Full prompt capture can include system prompts, message history, tool definitions, and model responses. Users can search prompt and completion text, compare runs for structural, content, and cost differences, and replay runs with changed models or temperatures or in mocked mode. The platform accepts standard OpenTelemetry traces, including from applications that do not use PetalFlow. PetalFlow integration adds graph topology, node inputs and outputs, and replay-capable snapshots. Trace data is documented as stored in SQLite, and the software can be built from source or run from a release binary as a local daemon. Authentication is described as future functionality and disabled by default. The public repository identifies an MIT license; no commercial pricing or usage limits are stated.