LLM observability & evaluation · Braintrust Data

Braintrust

Evaluation-first platform for testing, scoring, and comparing LLM application changes, with production tracing built in.

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Braintrust is built around systematic evaluation rather than passive tracing alone: teams define datasets and scoring functions, run experiments comparing prompt or model changes side by side, and track whether a change improved or regressed quality before shipping it. It also captures production traces and logs so the same scoring functions used offline can be applied to live traffic for ongoing monitoring, and includes a playground for iterating on prompts against real examples. This combination of a first-class evaluation/experimentation workflow plus observability distinguishes it from tools that are primarily request loggers. Pricing is usage-metered: each plan includes a base allowance of processed data volume and evaluation 'scores,' with per-unit charges beyond that, on top of a monthly platform fee for paid tiers. Deployment is hosted cloud by default, with on-prem or hosted options negotiated at the Enterprise tier.

At a glance

Vendor Braintrust Data
Pricing model Usage-based
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Teams that want evaluation and experimentation as the primary workflow, with production monitoring layered on top.

Pricing

Free Starter tier includes a small credit and data allowance with per-unit overage; Pro adds a larger allowance plus a monthly platform fee; Enterprise is custom with on-prem/hosted deployment options.

Plan Price Notes
Starter $0/month $10 credits + token rates, 1 GB processed data + $4/GB, 10k scores + $2.50/1k, 14-day retention
Pro $249/month $100 credits + token rates, 5 GB processed data + $3/GB, 50k scores + $1.50/1k, 30-day retention + $0.50/GB/mo; custom charts, environments, RBAC
Enterprise Custom Custom retention and export, RBAC, premium support, on-prem or hosted deployment

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Dataset-based experiments comparing prompt/model changes
  • Custom scoring functions applied both offline and in production
  • Production tracing and logging
  • Prompt playground for iterating against real examples
  • Side-by-side experiment comparison views
  • Role-based access control on paid tiers

Integrations

Profile last reviewed September 21, 2026

Alternatives

DeepEval Open-source Python evaluation framework with 30+ research-backed metrics, with an optional paid platform (Confident AI) for CI/CD and hosted reporting. Patronus AI Evaluation-focused platform offering pre-built and custom evaluator models to score LLM outputs for accuracy, safety, and quality. LangSmith Tracing, evaluation, and deployment platform for LLM applications, built by the team behind LangChain and LangGraph. Humanloop Prompt management, evaluation, and observability platform aimed at product teams collaborating on LLM features with domain experts.

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Terms to know

Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. LLM observability Monitoring a language model's live behavior in production, tracking its inputs, outputs, latency, cost, and failure patterns. Role-based access control (RBAC) Granting data or system permissions based on a user's assigned role rather than configuring access for each person individually.

Related guides

How to choose an LLM observability tool Pick an LLM tool by whether you need tracing, scoring or a gateway first — most teams eventually need all three, rarely from one vendor. How to evaluate LLM applications Offline evals, LLM-as-judge, human review and production monitoring, aimed at the whole pipeline rather than the model alone.