Braintrust vs LangSmith | Comparing Features, Pricing, and More

Selecting tools for AI application development is a consequential decision. The infrastructure you choose directly influences product quality, iteration speed, and operational reliability. Braintrust and LangSmith address similar needs—yet their approaches, capabilities, and intended audiences diverge in meaningful ways. Below, we examine their differences with clarity and precision.

Braintrust: An Integrated Platform for LLM Evaluation and Collaboration

Braintrust positions itself as a comprehensive hub for teams working with large language models (LLMs). Its architecture empowers users to evaluate, improve, and monitor AI-driven applications from initial prototype through production deployment.

Key Features:

• Comprehensive LLM Evaluation: Braintrust enables systematic creation of “evals”—each combines prompts, scoring logic (built-in or custom), and datasets. Teams can answer critical questions: “Did this prompt revision improve output quality? Did it introduce regressions?”
• Interactive Prompt Playground: The visual interface allows for immediate, side-by-side prompt and model comparisons. Users see results instantly and iterate without friction.
• Human Review Integration: Beyond automated metrics, Braintrust invites domain experts, analysts, and product managers to participate directly. Anyone with access can rate, comment, or annotate outputs through an accessible dashboard.
• Reusable Functions and Chaining: Advanced users can assemble atomic logic blocks, including prompts and scorers, and chain them together via API. This modularity supports sophisticated orchestration without cumbersome custom code.
• Monitoring and CI/CD Hooks: Integrate Braintrust into your continuous integration pipeline to automate regression testing. The platform also provides real-time production monitoring, so issues surface before they reach users.

User Experience:

The interface strips away clutter and spotlights the essentials. Visual trace viewers reveal LLM execution step-by-step—illuminating successes, pinpointing failures. Both technical and non-technical team members can access and edit prompts or review results. Any adjustment made in the UI syncs seamlessly with the codebase, ensuring continuity between rapid experimentation and production code.

Integration Approach:

Deploy Braintrust by routing LLM API calls through its proxy (a middleware that logs and scores each request), or by embedding its SDKs and REST API directly into your workflow. This proxy enables instant, consistent metric collection across providers. For organizations with stringent data requirements, Braintrust offers self-hosted deployment—though this option is exclusive to enterprise customers.

Pricing Structure:

• Free: Supports up to 5 users, 1 million trace spans per month, and 10,000 scores monthly—ample for pilots and small teams.
• Pro: $249/month for 5 users, with increased quotas and extended data retention.
• Enterprise: Custom pricing, with access to self-hosting and premium support.

The free plan offers genuine value for small groups, while higher usage triggers clear paywalls. Self-hosting remains limited to large-scale deployments.

Strengths:

• Unified workflow—rapid prompt iteration, evaluation, and monitoring in one environment.
• Approachable interface for all roles; true cross-functional collaboration.
• Evaluation capabilities range from automated similarity checks to nuanced human review.
• Bidirectional sync between UI and code eliminates silos.
• Generous free plan for teams starting out.

Limitations:

• Closed-source: self-hosting requires enterprise agreement.
• Proxy architecture can introduce latency or privacy concerns for some workloads.
• The platform prioritizes LLM evaluation and prompt optimization over cost analytics or generalized app telemetry.
• The Pro tier may be cost-prohibitive for individual developers.

Ideal For:

AI product teams and organizations seeking a collaborative environment for rigorous prompt evaluation, version control, and stakeholder engagement. Braintrust shines when diverse teams—including business leaders and subject-matter experts—need a direct hand in shaping LLM outcomes.

LangSmith: Deep Observability and Reliable Monitoring for LLM Applications

LangSmith, developed by the creators of LangChain, offers deep visibility into every layer of an LLM-driven system. It equips engineering teams to trace, debug, monitor, and evaluate LLM pipelines—especially those running in production.

Key Features:

• Full-Stack Tracing and Debugging: LangSmith captures each input, output, tool call, and step in an LLM run. Developers can inspect agent logic, dissecting decision-making at every stage.
• Production-Ready Monitoring: Dashboards surface live metrics—latency, error rates, token consumption, and custom business indicators. Teams can catch performance dips or anomalies in real time.
• Versatile Evaluation Suite: Batch evaluations support both automated (LLM-as-judge, reference-based) and human-in-the-loop review. Custom logic can be written in Python for highly tailored scoring.
• Prompt Versioning and Side-by-Side Experiments: While prompt design remains code-first, the UI supports comparing outputs from different prompt versions or settings, streamlining experimentation.
• Robust Alerting: Easily configure alerts for deviations in error rates or output quality—a vital feature for critical deployments.

User Experience:

The LangSmith interface centers on power and transparency. Developers navigate traces and evaluation outcomes with granular control. Product and business users can rate or annotate outputs and flag issues for review—though developers typically handle setup and instrumentation.

Integration Approach:

LangSmith adapts to many environments. For LangChain users, tracing activates in moments through built-in callbacks. For any LLM stack, LangSmith supports industry-standard OpenTelemetry, allowing direct integration without a proxy or proprietary SDK. This approach minimizes operational risk and preserves control over data flow.

Enterprise clients can deploy LangSmith in their own infrastructure, satisfying compliance and data residency demands.

Pricing Structure:

• Developer (Free): 1 user, 5,000 traces monthly.
• Plus: $39/user/month, pooled usage up to 10 users.
• Enterprise: Custom arrangements, including self-hosting and advanced support.

The free option is well-suited for solo developers, but teams will quickly need to budget for seat licenses and usage overages.

Strengths:

• Exceptional tracing and debugging, particularly for LangChain-based pipelines.
• Framework-agnostic via OpenTelemetry.
• Comprehensive monitoring and configurable alerting.
• Team features: role-based access, shared annotation, and review queues.
• Supported by an active developer community.

Limitations:

• Free tier limited to individual use.
• Closed-source; self-hosted only for enterprise customers.
• A code-first setup: non-developers depend on engineers for initial integration.
• Prompt development and experimentation happen mainly in code, not visually.

Best Suited For:

Developer-led teams and organizations prioritizing observability, robust monitoring, and precise debugging—especially those already invested in LangChain or requiring open integration standards.

Comparing Braintrust and LangSmith: Feature Overview

Deciding Between Braintrust and LangSmith

Choose Braintrust if:

• You value fast, collaborative prompt iteration and need to compare model changes visually.
• Multiple disciplines—engineers, product leads, analysts—need to review or score LLM outputs.
• Rigorous evaluation, data versioning, and automated quality checks are core requirements.
• Your team wants to weave LLM testing into CI/CD pipelines.
• You seek a generous free tier for initial team adoption.

Choose LangSmith if:

• Your project relies on LangChain, or you’re building complex, multi-step agent workflows.
• Deep observability and production monitoring, including live alerting, are essential.
• You prefer telemetry-based integration (no proxy), with full control over data flow.
• Your DevOps team treats AI components as microservices and needs fine-grained metrics.
• Enterprise-grade deployment, self-hosting, and compliance are must-haves.

Developers vs. Business Users: What Matters Most

For Developers:
LangSmith appeals to engineers who prize integration flexibility and deep debugging. Its telemetry-driven model fits naturally into code-centric workflows and DevOps automation. Braintrust, in contrast, offers instant visual feedback and a smooth bridge between code and UI—ideal for developers who want to iterate rapidly and share insights with colleagues across the organization.

For Business and Product Teams:
Braintrust’s interface welcomes non-technical reviewers, making it easy to democratize oversight and feedback. The free tier allows small teams to start without financial friction. For larger organizations, LangSmith may offer stronger value, especially when monitoring and compliance come to the fore and when a LangChain investment already exists.

Final Thoughts

Braintrust and LangSmith address the challenge of LLM application development with distinct philosophies. Braintrust thrives as a collaborative, experiment-driven environment where prompt optimization and team feedback drive continuous improvement. LangSmith excels in environments that demand transparency, observability, and detailed monitoring—especially where LangChain pipelines power critical business functions.

Many teams may find value in both: use Braintrust during development for creative iteration and collective review, then rely on LangSmith in production to monitor, alert, and safeguard reliability. Regardless of your choice, prioritizing rigorous tooling elevates both your product’s quality and your team’s confidence.

About PromptLayer

PromptLayer is a prompt management system that helps you iterate on prompts faster — further speeding up the development cycle! Use their prompt CMS to update a prompt, run evaluations, and deploy it to production in minutes. Check them out here. 🍰