Galileo AI works well for teams that want packaged evaluators and built-in runtime guardrails. Braintrust is the stronger choice for teams that need evaluation embedded in the release process, with greater control over how quality is defined, tighter CI/CD enforcement, and a more direct path from production failures to regression tests. This article compares Galileo AI and Braintrust across features, pricing, and production requirements, helping teams evaluate which platform fits their stack and AI quality-control process.
Galileo AI is an evaluation and observability platform that combines packaged scoring, production monitoring, and runtime guardrails. Luna-2, Galileo's family of proprietary small language models for evaluation tasks, delivers low-latency scoring at high volume. Galileo prioritizes prebuilt evaluators and opinionated workflows over customizable evaluation logic, which can reduce setup time but gives teams less control as evaluation requirements become more domain-specific.
Braintrust is an AI evaluation and observability platform that connects production traces, structured evaluation, CI/CD quality gates, and feedback-driven iteration in a single workflow. Braintrust keeps production traces, test cases, scorers, and release thresholds connected so teams can evaluate changes against the same quality standards in development and on live traffic. Braintrust also gives teams full ownership of evaluation logic.
Both Galileo AI and Braintrust cover tracing and evaluation, but they differ in how much control teams have over scoring logic, release enforcement, and production workflows.
| Dimension | Braintrust | Galileo AI |
|---|---|---|
| Best for | Framework-agnostic teams, eval-first workflows | Packaged scoring and runtime guardrails |
| Evaluation approach | 25+ built-in scorers, custom code scorers, LLM-as-a-judge, offline/online evals, autoevals library, Loop for automated scorer and dataset building | Proprietary Luna-2 SLMs, 20+ prebuilt evaluators, CLHF auto-tuning |
| Eval logic ownership | Fully inspectable and versionable in your codebase | Vendor-maintained, opaque |
| AI assistant | Loop (generates datasets, creates scorers, identifies failures, suggests prompt fixes) | Insights (pattern detection, root cause analysis) |
| CI/CD quality gates | Native GitHub Action blocks merges on quality thresholds | CI/CD testing supported, less tightly integrated |
| Production to test case | One-click trace-to-test-case conversion | Requires more manual assembly |
| Real-time guardrails | No built-in runtime blocking | Galileo Protect (Enterprise tier only) |
| Prompt management | Playground with side-by-side comparison against production traces | Prompt Store with version tracking |
| Query performance | Brainstore (speed optimized for AI workloads) | Standard infrastructure |
| Framework integrations | Native integrations (OpenTelemetry, Vercel AI SDK, OpenAI Agents, LangChain, Google ADK, Mastra, Pydantic AI, LlamaIndex) and AI Gateway | CrewAI, LangGraph, OpenAI Agent SDK, LlamaIndex, Strands, OTEL |
| TypeScript support | First-class SDK, identical API to Python | TypeScript SDK available |
| Free tier | 1M trace spans, 10K scores, unlimited users | 5,000 traces/month, unlimited users |
| Pro pricing | $249/mo with unlimited trace spans | $100/mo for 50K traces (usage-based scaling) |
| Self-hosting | Enterprise hybrid deployment and self-hosting | Enterprise VPC/on-prem |
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Galileo is a better choice in situations where packaged protection and faster setup matter more than evaluation flexibility.
Runtime guardrails: Galileo Protect scans prompts and responses and blocks outputs that trigger safety or quality violations. Teams working in regulated environments or other high-risk settings may view runtime blocking as a required capability rather than an optional safeguard. Galileo Protect is available only on Galileo's Enterprise tier, so teams adopting Galileo for runtime blocking need to evaluate Enterprise pricing.
Prebuilt evaluator library: Galileo offers 20+ out-of-the-box evaluators for common quality dimensions, including hallucination detection, context adherence, and completeness. CLHF also improves evaluator performance over time by leveraging human feedback. For teams whose evaluation requirements map closely to those predefined categories, Galileo can reduce setup work and make it easier to get started without writing custom scoring logic.
Braintrust is a better choice for teams that need evaluation tied directly to development, release review, and production feedback.
Braintrust lets teams write, inspect, and version scoring logic through code-based scorers, LLM-as-a-judge configurations, and custom eval functions stored alongside application code. When a scorer produces unexpected results, teams can inspect the logic directly and debug it in the codebase. As evaluation requirements become more domain-specific, teams that can't inspect or modify their scoring logic end up working around it instead of improving it.
Braintrust's native GitHub Action runs evaluations on every pull request, posts a detailed score summary as a PR comment, and blocks the merge when scores drop below defined thresholds. Native pull-request enforcement makes evaluation part of the deployment process instead of a separate testing step.
Braintrust converts production traces into evaluation dataset entries with a single click. When a user reports a bad response, teams can turn the failure into a regression test that runs on future deployments, rather than fixing the issue once and moving on. Over time, the evaluation suite expands beyond real user interactions to include synthetic examples as well.
Teams can describe evaluation goals in natural language, and Loop helps generate scorers, create datasets from production data, and identify failure patterns in logs. Loop helps teams move from issue detection to broader evaluation coverage more quickly.
Braintrust keeps production traces, eval datasets, scorers, and release thresholds connected within a single system, so teams can apply the same evaluation workflow both before deployment and on live traffic. Continuity between development and production makes Braintrust a stronger choice for teams that want evaluation to improve production quality over time.
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Galileo and Braintrust price differently as usage grows. Galileo starts lower and scales by trace volume, while Braintrust starts higher and includes more built-in capacity for teams running broader evaluation workflows.
Galileo Pro starts at $100 per month for 50K traces. The free plan includes 5K traces per month. Runtime guardrails are available on Enterprise, not Pro.
Braintrust Pro starts at $249 per month and includes unlimited trace spans, 50K scores, and unlimited users. Braintrust's free tier includes 1M trace spans, 10K scores, and unlimited users. See Braintrust pricing here
Galileo's lower starting price is easier to justify for lighter workloads, but Braintrust becomes the stronger option when teams need more evaluation capacity and a platform designed to support evaluation as an ongoing production workflow. Galileo Protect also requires Enterprise pricing, which significantly increases costs for teams considering Galileo specifically for runtime blocking.
Galileo AI and Braintrust take different approaches to AI evaluation, and the right choice depends on how your team wants evaluation to function inside the development process.
Choose Galileo AI when runtime guardrails are a core requirement, when prebuilt evaluators already cover the quality your team needs, or when lower-cost packaged scoring is more important than owning evaluation logic in code. Galileo is better suited to teams that value faster setup and are comfortable working within a more predefined evaluation model.
Choose Braintrust when evaluation needs to remain closely connected to product improvement as prompts, models, and application behavior change. Braintrust is better suited to teams that want inspectable and versioned scoring logic, stronger quality enforcement in release workflows, and a more reliable way to turn production failures into regression coverage that continues to improve the system over time.
For most teams building and iterating on AI products, Braintrust is the stronger platform. Notion's AI team went from triaging 3 issues per day to 30 after adopting Braintrust's eval workflows and production-to-test-case pipelines. Braintrust's free tier gives teams enough room to start building evaluation coverage on real usage before committing to a paid plan. Start free with Braintrust
For most teams, yes. Braintrust gives teams full ownership of scoring logic and enforces quality thresholds directly in CI/CD, which means evaluation improves alongside the product. Galileo can be the faster starting point when a team's quality checks map cleanly to its prebuilt evaluator library, and the priority is getting coverage running quickly. Once evaluation requirements diverge from those defaults, Braintrust's code-based approach scales LLM evaluations more predictably.
Galileo Pro starts at $100 per month for 50K traces, and the free plan includes 5K traces per month. Braintrust Pro starts at $249 per month and includes unlimited trace spans, 50K scores, and unlimited users, while the free tier includes 1M trace spans, 10K scores, and unlimited users. Galileo offers a lower starting price, but Braintrust includes substantially more evaluation capacity and broader team access, which makes Braintrust more favorable as evaluation becomes a larger part of the production workflow.
Both platforms support OpenTelemetry, so parallel instrumentation is technically possible. A team could use Galileo Protect as a runtime safety layer while running evaluation, experimentation, and CI/CD gates through Braintrust. In practice, running two evaluation platforms adds operational overhead, so most teams use Braintrust as the primary system and add separate runtime blocking only when needed.
Braintrust is the strongest alternative for teams that need evaluation depth beyond what packaged scoring provides. Inspectable scorers, native CI/CD quality gates, and a one-click path from production traces to reusable test cases make Braintrust better suited to teams that need evaluation to be more closely tied to production workflows.
Braintrust is the strongest option for production AI applications because it supports a more complete evaluation workflow from development through release and ongoing production improvement. Teams that need evaluation to remain useful as systems grow more complex will generally find Braintrust better suited to long-term production use than tools built primarily around packaged scoring or narrower monitoring tasks.