Braintrust vs Confident AI

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.

Confident AI is an AI quality platform for evaluating applications during development and monitoring them in production. Teams can experiment with prompts, models, and parameters, run automated regression evaluations in CI/CD, and assess results with more than 50 metrics. Production tracing records inputs, outputs, latency, tokens, and execution spans, while live alerts indicate declining quality. Red-team testing addresses safety vulnerabilities and adversarial attacks. The platform supports Python and TypeScript SDKs, OpenTelemetry, and more than 20 framework and gateway integrations. APIs cover prompt versioning, dataset creation, trace ingestion, project provisioning, and governance policies. It is available through web and API access, or can be self-hosted in AWS, Azure, or GCP using Docker; setup typically takes 1–2 weeks. The free plan includes two seats, one project, five weekly test runs, and 1 GB-month of trace spans. Paid plans start at $200 a month.