Braintrust vs Langfuse: Open Source, Self-Hosting and Evals

Braintrust and Langfuse offer comparable LLM observability capabilities, including tracing, evaluations, datasets, prompt management, OpenTelemetry support, and CI integrations, but differ substantially in licensing, deployment architecture, and operational responsibility. Langfuse is largely MIT-licensed, can be self-hosted without a license key, and runs its complete application stack in a customer environment using services such as ClickHouse, PostgreSQL, Redis, MinIO, a web app, and a worker; production deployments typically use Kubernetes and Helm. Braintrust is a proprietary platform whose public repositories contain SDKs, evaluation tools, and infrastructure modules, while Enterprise customers may host a data plane in their own cloud but continue to rely on Braintrust’s hosted control plane for the UI, authentication, and metadata. Its AWS deployment uses Terraform to provision a more extensive architecture involving Brainstore nodes, RDS PostgreSQL, Redis, S3, ECS, Lambda, networking, and other cloud resources. Langfuse stores trace data primarily in ClickHouse, whereas Braintrust uses its Brainstore engine with write-ahead logs and object storage. Both support code-based and model-judged evaluations, though Langfuse’s GitHub experiment action can fail builds on regressions while Braintrust’s action emphasizes pull-request reporting. Their cloud pricing also uses different meters, with Braintrust charging for processed data and scores and Langfuse charging for traces, observations, and scores. The comparison concludes that organizations should weigh data-location requirements, source-code access, infrastructure capacity, CI gating behavior, and expected telemetry volume before selecting either platform, while separate agent-assurance tooling may help verify real-world effects of agent actions beyond traces and evaluation scores.

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