Comparison
Logfire vs Braintrust
Logfire is the production AI improvement system: investigate the complete agent and application trace, annotate the runs that matter, score production and offline datasets with Pydantic Evals, let the optimizer propose a trace-backed change, then ship managed prompts, agent specs, tools, and skills with targeting and rollout controls. Add infrastructure monitoring, browser session replay, feature flags, and the industry-leading agent trace investigator—all in one OpenTelemetry-native product, with $0 per 1,000 scores.
Feature comparison
From a production run to a better agent
| Feature | Logfire | Braintrust |
|---|---|---|
| Production context | Browser, agents, services, databases, logs, metrics, and infrastructure | AI and application traces, logs, and OpenTelemetry spans |
| Full-stack observability suite | Infrastructure monitoring, service maps, logs, metrics, traces, and AI | ✗ |
| Browser session replay | Browser tracing and session replay alongside the agent trace | ✗ |
| First-class feature flags | OpenFeature/OFREP flags, targeting, and controlled rollout | ✗ |
| Evaluation workflow | Pydantic Evals: one evaluator model online and offline | Experiments, playgrounds, CI/CD, and online scoring |
| Score pricing | $0 per 1,000 scores | $1.50 per 1,000 scores after 50K/month on Pro |
| Human review | Annotation queues on production runs and evals | Human-review scores and assigned trace review |
| From failure to change | Trace-backed optimizer and managed agent configuration | Prompt, scorer, dataset, and environment workflows |
| Managed agent configuration | Prompts, agent specs, tools, skills, versioning, targeting, and rollout | ✗ |
| Controlled rollout | Immutable versions, labels, targeting, weighted rollout, and feature flags | Prompt, dataset, and parameter environments |
| Investigation workflow | Agent trace investigator, PostgreSQL-compatible SQL, and MCP across full telemetry | Logs, trace views, SQL, and MCP for Braintrust data |
| Deployment options | Cloud, Dedicated, or the same product self-hosted on Kubernetes | Cloud or an Enterprise self-hosted data plane |
Score economics
Score freely at production scale
| Production coverage | Scores | Estimated Braintrust Pro monthly charge | Logfire score meter |
|---|---|---|---|
| 10M runs × 10% sampled × 3 scores | 3M scores/month | $4,674/month | $0 score charges |
| 10M runs × 25% sampled × 5 scores | 12.5M scores/month | $18,924/month | $0 score charges |
| 100M runs × 10% sampled × 5 scores | 50M scores/month | $75,174/month | $0 score charges |
Illustrative coverage models assume three or five recorded scores per sampled trace. Braintrust Pro published list pricing: $249/month + max(scores − 50,000, 0) ÷ 1,000 × $1.50. Figures include the platform fee, but exclude Braintrust processed-data and retention charges, and do not model provider or model costs an LLM-as-a-judge configuration may incur. Logfire charges $0 per 1,000 scores; telemetry records are billed on the selected Logfire plan.
Why Logfire
The full production improvement loop
Investigate the system, not only the output
An agent failure is often a browser, API, database, retrieval, tool, or infrastructure failure wearing an LLM-shaped mask. Logfire keeps those signals in one nested trace, with service maps, logs, metrics, SQL, and an agent trace investigator built for the production incident behind the score.
Evaluate online and offline without rationing coverage
Use the same Pydantic Evals evaluators for fast offline feedback and online production monitoring. Cheap heuristics can run on every run; LLM judges can sample the traffic that deserves them. With $0 per 1,000 scores, coverage is a quality decision, instead of a new billing meter.
Turn a human judgment into the next improvement
Annotation queues let reviewers work through the production runs that matter, with verdicts, failure categories, expected outputs, comments, and tags. That judgment stays linked to the trace, becomes a reusable evaluation case, and gives the optimizer grounded evidence for the next change.
Change the agent safely, without a second control plane
Logfire manages prompts, agent specs, tools, and skills as versioned configuration. Review a trace-backed proposal, then target a cohort, canary a weighted rollout, watch the live result, and roll back by moving a label. The version that served every run is part of that run's trace.
Decision Guide
Which should you choose?
Choose Logfire if...
- ✓You need to diagnose agents in the context of the browser, service map, database, API, logs, metrics, and infrastructure
- ✓You want online and offline evaluation without a per-score charge
- ✓You want reviewers to work from annotation queues, then export an annotated failure into a reusable evaluation case
- ✓You want a trace-backed prompt optimizer to propose a production-grounded change
- ✓You want to version, target, canary, and roll back managed prompts, agent specs, tools, and skills
- ✓You want your coding agent to investigate the same telemetry with MCP and PostgreSQL-compatible SQL
Choose Braintrust if...
- •You're already standardized on Braintrust and prefer not to migrate
FAQ
Common questions
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