LangSmith is a strong choice for teams building primarily on LangChain and LangGraph. Braintrust works best for teams that want AI evaluation to determine what reaches production, with native CI/CD enforcement, broader framework support, and pricing built for team-wide collaboration without per-seat costs. This article compares LangSmith and Braintrust directly across features, pricing, and use cases, so teams can choose based on how they build and ship AI products. Be sure to read our full analysis of all LangSmith alternatives to understand how they stack up to all other LLM observability tools.
LangSmith is an observability and evaluation platform from the LangChain team. LangSmith helps developers trace, debug, evaluate, and monitor LLM applications, with the strongest integration available for teams building on LangChain and LangGraph. LangSmith also supports non-LangChain frameworks via SDK wrappers and OpenTelemetry, but the best developer experience is still within the LangChain ecosystem.
Braintrust is an AI evaluation and observability platform built for teams that need evaluation to control what reaches production. Braintrust connects production traces, structured evaluations, and CI/CD quality gates in a unified workflow, so teams can inspect real application behavior, turn failures into regression tests, and enforce release standards before changes ship. Braintrust works across frameworks and model providers, which makes it a stronger fit for teams that need evaluation discipline and release control without tying their workflow to a single ecosystem.
Both LangSmith and Braintrust cover tracing and evaluation, but they differ in how they support production workflows, enforce quality standards, and scale across teams.
| Dimension | LangSmith | Braintrust |
|---|---|---|
| Best for | LangChain/LangGraph-native teams | Framework-agnostic teams, eval-first workflows |
| Tracing | ✅ Hierarchical traces, deep LangChain instrumentation | ✅ Full tracing via SDK, OpenTelemetry, or Gateway |
| Evaluation | ✅ Offline/online evals, LLM-as-judge, annotation queues | ✅ Built-in scorers, LLM-as-a-judge scorers, custom code scorers, online/offline evals, autoevals, Loop AI for automated scorer and dataset generation |
| CI/CD quality gates | ⚠️ Requires custom GitHub Actions scripting | ✅ Native GitHub Action posts results to PRs and blocks merges on quality thresholds |
| Production-to-eval pipeline | ❌ Manual trace-to-dataset workflow | ✅ One-click conversion of production failures into eval cases |
| Playground | ✅ Prompt playground with Polly AI | ✅ Playground connected to live production traces with side-by-side comparison |
| CLI tooling | ❌ No dedicated CLI | ✅ bt CLI for evals, log queries, and prompt management |
| LLM Gateway | ❌ No built-in gateway | ✅ Unified OpenAI-compatible API across providers with tracing |
| Native framework integrations | ✅ LangChain/LangGraph native, others via wrappers | ✅ Native integrations across LangChain, Vercel AI SDK, OpenAI Agents, Google ADK, Mastra, Pydantic AI, LlamaIndex, and more |
| SDK languages | Python, TypeScript, Go, Java | Python, TypeScript, Go, Ruby, C#, Java, Kotlin |
| AI assistant scope | Polly (prompt optimization) | Loop (scorers, datasets, failure analysis, prompt improvement) |
| Query performance | Standard database infrastructure | Brainstore (optimized for AI workloads) |
| Online scoring | ⚠️ Available, requires separate configuration | ✅ Same scorers run in CI/CD and on live production traffic with configurable sampling |
| Free tier | 5k trace spans, 10K eval scores, 1 seat | 1M trace spans, 10K eval scores, unlimited seats |
| Paid pricing | $39/seat/month with up to 10K traces + usage-based trace charges | $249/month with 50K scores + usage-based billing, unlimited trace spans, unlimited seats |
| Agent deployment | ✅ LangGraph-based managed deployment | ❌ Not offered |
| Self-hosting | ✅ Cloud, BYOC, and self-hosted options | ✅ Enterprise hybrid and self-hosted deployment |
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LangSmith is a better choice for teams building primarily on LangChain or LangGraph.
Native LangChain and LangGraph tracing: A single environment variable enables full tracing across chains, agents, and tool calls without SDK wrapping or additional configuration. For teams working exclusively within the LangChain ecosystem, the level of zero-config instrumentation provided is difficult for other platforms to match.
Managed agent deployment: LangSmith also offers managed deployment for LangGraph agents, with preview environments on each pull request and production promotion through the Control Plane API. Teams building LangGraph-native agents can keep deployment, tracing, and evaluation in the same ecosystem.
Braintrust is the better fit for teams building evaluation into their release process.
Braintrust's native GitHub Action, braintrustdata/eval-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. LangSmith supports CI/CD evaluation through custom scripting, where teams write their own eval runner and build their own reporting. Braintrust treats quality gating as a built-in release-control workflow, with score summaries and merge blocking included out of the box.
Braintrust makes it easier to turn production failures into regression tests. When a user reports a bad response, teams can convert the trace directly into an evaluation case, which shortens the path from issue discovery to a test that prevents the same failure from shipping again. In LangSmith, teams manually curate datasets between tracing and evaluation, selecting traces, exporting them, and organizing them into eval sets by hand.
LangSmith's Polly AI focuses on prompt optimization. Braintrust's Loop AI supports a broader set of evaluation tasks, including helping teams generate scorers, create datasets from production data, and identify failure patterns in logs, making Loop AI more useful for teams maintaining Braintrust's evaluation workflow around a live application.
Native integrations across LangChain, Vercel AI SDK, OpenAI Agents SDK, Google ADK, Mastra, Pydantic AI, and LlamaIndex keep instrumentation more consistent as application architecture changes. Braintrust Gateway provides teams with a unified layer to route model calls across providers while keeping tracing and evaluation connected to the same workflow. LangSmith remains strongest inside the LangChain and LangGraph ecosystem, while Braintrust is better suited to teams working across multiple frameworks and providers.
The bt CLI gives developers a terminal-based workflow for running evals, querying logs, and managing prompts without moving everything into the web UI. The command-line workflow is better suited to teams that want evaluation and observability to stay close to code, debugging, and deployment.
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LangSmith and Braintrust price differently as teams grow. LangSmith uses seat-based pricing, then adds usage charges on top. Braintrust includes unlimited users and charges for usage beyond the included plan limits.
LangSmith Plus costs $39 per seat per month and includes 10k base traces per seat. The free plan includes 5k traces and 1 seat.
Braintrust Pro costs $249 per month, with unlimited users and trace spans, and includes 50k scores. The free plan includes unlimited users, 1M trace spans, and 10k scores. See Braintrust pricing here.
The pricing gap becomes more noticeable as more collaborators need access. A 5-person team on LangSmith starts at $195 per month before usage charges. A 10-person team starts at $390 per month before usage charges. Even a 100-person team on Braintrust still starts at $249 per month, because Braintrust does not charge per seat for engineers, product managers, QA reviewers, or other stakeholders who need access to traces and evaluation results.
Teams choosing between LangSmith and Braintrust are not only comparing tracing and evaluation features but also deciding whether evaluation remains tied to a specific framework or becomes part of a production process that supports debugging, regression prevention, and release control.
Choose LangSmith if your team primarily builds on LangChain or LangGraph, wants the deepest native tracing within that ecosystem, and needs managed deployment for LangGraph agents. LangSmith is the better fit when most of the workflow stays inside the LangChain stack.
Choose Braintrust if your team needs evaluation to act as release control. Braintrust is the stronger fit for teams that need production traces, structured evaluations, and CI/CD quality gates in a single workflow, along with broader support across frameworks and pricing that allows team-wide access without per-seat expansion.
Notion's AI team went from fixing 3 issues per day to 30 after adopting Braintrust's eval-driven workflow. Stripe, Vercel, Zapier, Airtable, and Instacart all run Braintrust in production. Braintrust's free plan includes 1M trace spans and unlimited users, giving teams room to build an evaluation workflow based on real usage before committing budget. Start building with Braintrust today.
LangSmith supports framework-agnostic tracing through SDK wrappers and OpenTelemetry, so teams can use it without LangChain. The smoothest developer experience, however, remains tied to LangChain and LangGraph. Teams working across multiple frameworks or planning to evolve beyond LangChain may find Braintrust's broader native integration support a better long-term fit.
Braintrust provides a native LangChain callback handler for both Python and JavaScript, and also offers an experimental LangSmith wrapper for teams migrating. Teams get full LangChain compatibility with the added benefit of Braintrust's quality gates, Loop AI, and the production-to-eval workflow.
Braintrust is the stronger option for teams that need evaluation connected to production workflows and release control. LangSmith is a strong choice for teams centered on LangChain, but Braintrust is better suited to teams that want tracing, evaluation, and collaboration to work across a broader production workflow instead of staying tied to one framework ecosystem.
Braintrust provides an experimental LangSmith wrapper that can send tracing and evaluation calls to both platforms in parallel or route them to Braintrust with minimal code changes, making it easy for teams that want to keep LangSmith in a LangChain-heavy workflow while adding Braintrust for broader evaluation and release workflows.
LangSmith's free tier includes 5,000 traces per month with a single seat. Braintrust's free tier includes 1M trace spans, 10K eval scores, and unlimited users. For teams evaluating both platforms on live workloads, Braintrust makes broader team access easier from the start, while LangSmith keeps free access limited to one seat.
Braintrust is the strongest LangSmith alternative for teams bringing AI systems into production. LangSmith is a strong choice for teams centered on LangChain, while Braintrust is a stronger choice for organizations that need tighter control over quality before release. Braintrust gives engineering, product, and review teams a shared workflow for testing outputs and deciding what ships.