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Braintrust Data, Inc.

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Braintrust helps teams observe, evaluate, and improve AI agents in production.

Overview

Braintrust is the active observability platform for instrumenting, understanding, and improving AI agents. It enables teams to observe production behavior, evaluate output quality, and drive continuous improvements at scale. The platform combines tracing, evaluation, and collaborative annotation to reveal how agents behave in production, why they decide as they do, and how to optimize performance. It supports scalable data handling, online and offline evaluation, pattern discovery, and workflows that connect experimentation, data pipelines, and dashboards. With deployment options ranging from self-hosted to cloud, Braintrust helps organizations reduce risk, accelerate iteration, and improve AI reliability while keeping data in their own infrastructure if required.

Mission statement

To empower teams to build reliable AI agents at scale by providing end-to-end observability, evaluation, and collaborative workflows that reveal how agents behave in production and guide systematic improvements.

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What we offer

Braintrust Observability Platform

Enhance AI agent performance with comprehensive observability and real-time insights.

www.braintrust.dev/docs

Market segments

Market size by segment

Growth potential (CAGR)

AI observability and monitoring

1.4 Billion USD22.5% CAGR

Monitoring, performance tracking, drift detection, logging, and alerting to maintain model reliability, data quality, and operational performance in production.

Model evaluation and testing

5 Billion USD20.5% CAGR

Automated and human-in-the-loop evaluation, dataset-based tests, online and regression evals, and synthetic-test generation to measure model quality and prevent regressions.

Distributed tracing and root-cause analysis

0.35 Billion USD22.5% CAGR

Trace collection, storage, retrieval, and automated correlation to support end-to-end request analysis and root-cause identification.

Prompt engineering and optimization

6.95 Billion USD42.52% CAGR

Prompt management, experimentation, and optimization workflows that run prompts against production data, evaluate outputs, and iterate to improve prompt performance.

On-premises data residency and privacy for AI

19.5 Billion USD24.6% CAGR

Local-first deployment, on-premises memory storage, identity scoping, and governance features that ensure data residency, privacy, and compliance for persistent AI context.

More information about our offering

Braintrust Observability Platform

Braintrust Observability Platform is an integrated platform for instrumenting, understanding, and improving AI agents in production. It combines agent tracing, evals, and pattern discovery in a single environment to help teams observe production behavior, measure quality, and drive continuous improvements. Key capabilities include scalable trace ingestion, real-time performance monitoring, customizable views and annotation, and support for Loop-driven eval development and pattern discovery across large production datasets.

  • Ingest Agent Traces At Scale
    Collect and analyze traces from multiple agents simultaneously, allowing for comprehensive insights into production behavior.
  • Diagnose Failures Effectively
    Analyze detailed traces to uncover the origins of failures, enabling quicker and more effective resolutions.
  • Ingest Millions Of Traces
    Allows seamless processing of extensive sets of production data without delays.
  • Maintain Fast Queries
    Ensures quick access and processing speed regardless of data scale.
  • Achieve Sub-Second Queries
    Facilitates instant trace analysis for faster debugging and insights.
  • Integrate With Observability Stack
    Streamlines workflows by connecting with existing Braintrust tools for enhanced observability.
  • Ensure Safety
    Protect your data with robust security measures and authentication protocols.
  • Score Outputs Automatically
    Utilize automated scoring mechanisms alongside human insights to ensure optimal performance and quality in outputs.
  • Discover Patterns Automatically
    Leverage machine learning to identify recurring behaviors and critical patterns without manual intervention.
  • Enable Hybrid Deployments
    Choose between cloud and on-premises deployments, ensuring flexibility and control over sensitive data.
  • Monitor Performance Live
    Stay informed of performance metrics in real time to enable quick adjustments and maintain quality.
  • Investigate Traces Naturally
    Streamline the investigation of agent behavior with user-friendly, natural language summarizations.
  • Store Data In Object Storage
    Utilizes modern storage solutions, enhancing data retrieval and scalability.
  • Perform Rapid Full Text Searches
    Enables quick discovery of relevant requests and errors, improving debugging efficiency.
  • Automate Processes
    Optimize your workflow with automated setups that save time and ensure consistency.
  • Customize Dashboards
    Create tailored views and annotation interfaces to suit specific project needs, enhancing collaboration among teams.
  • Develop Prompts Rapidly
    Streamline the process of creating and testing prompts to improve agent output quality swiftly.
  • Automate Improvements
    Enable automated workflows that generate relevant prompts and datasets based on agent performance history.
  • Integrate Coding Agents
    Facilitate seamless integration between coding agents and the observability platform, enhancing workflow efficiency.
  • Categorize Traces Effectively
    Automatically classify production traces by relevant themes, improving insights into agent behavior.
  • Deploy In Your Preferred Environment
    Offers flexibility in data management by allowing custom deployment options.
  • Select Interface
    Choose the most suitable tool for your development needs, enhancing flexibility.
  • Troubleshoot Instantly
    Access real-time insights into production behavior to facilitate rapid problem-solving.
  • Manage Data Versions Flexibly
    Easily create and manage different versions of datasets for iterative testing and validation purposes.
  • Utilize Native SDKs
    Use out-of-the-box SDKs to speed up the integration of the observability platform with existing projects.
  • Query Easily
    Simplify data exploration for teams without extensive technical skills, making insights accessible.

References

Methodology and sourcing behind the market figures shown above.

AI observability and monitoring

Primary estimate uses Market.us (AI in Observability) which reports a 2023 market value of USD 1.4B and a 22.5% CAGR to 2033. Supporting sources show a range by subsegment: Mordor Intelligence reports a smaller agentic-observability niche (USD 0.55B in 2025; 30.1% CAGR to 2030), Precedence Research reports AI-based data observability at USD 1.10B in 2025 with ~11.6% long-run CAGR, and MarketsandMarkets shows the broader observability tools market (~USD 11.9B in 2026, 14.1% CAGR). Taken together, the evidence supports a current AI observability market on the order of USD 1–1.5B with high growth potential (roughly mid-to-high double-digit CAGR); the selected point estimate is USD 1.4B and CAGR 22.5% (Market.us) to reflect AI-specific momentum.

Model evaluation and testing

Estimate derived by consolidating niche evaluation-platform figures (Congruence: $1.35B in 2024), broader model-based testing (Fact.MR: $4.6B in 2025), and benchmarking platform forecasts (AstuteAnalytica: $0.35B in 2025). These specialized evaluation/testing submarkets sit inside much larger ML and software-testing TAMs (Fortune: ML ~$48B in 2025; ResearchNester: software testing ~$57.7B in 2026). Combining these sources and weighting toward the larger, established model-based testing market yields an approximate current market size of ~$5B and a blended high-growth CAGR (~20.5%) reflecting rapid platform/benchmark adoption alongside slower, established testing segments.

Distributed tracing and root-cause analysis

Primary estimate uses a focused market projection for distributed tracing tools that cites a USD 350M market in 2024 and a rise to USD 1.77B by 2033 (CAGR ~22.5%). A broader observability market forecast (USD 11.91B in 2026 to USD 22.99B by 2031, CAGR 14.1%) from MarketsandMarkets provides context that tracing is a growing niche within a larger observability market, supporting a higher CAGR for the tracing/root-cause segment.

Prompt engineering and optimization

Estimate based on published market reports in the search results. Mordor Intelligence explicitly sizes the broader prompt engineering and agent programming tools market at USD 6.95B (2025) with a 42.52% CAGR (2025–2030). Other reports show a range (Market Research Future: USD 2.20B in 2024, 27.86% CAGR; Precedence Research: USD 0.505B in 2025, ~31–33% CAGR). I used Mordor’s figure because it most closely matches the enterprise-focused prompt management/optimization tooling described in the segment (includes agent frameworks, optimization platforms, and validation tools).

On-premises data residency and privacy for AI

Estimate anchored to a published projection for the broader sovereign cloud market ($195B in 2026, 24.6% YoY). On-premises data residency and privacy for AI is a specialty subset of sovereign/sovereign-like cloud and data-residency services; assuming ~10% share captures focused on regulated AI deployments, on-prem/region-locked deployments, and adjacent vendor offerings (data-residency, DSPM, agentic AI controls). Growth potential follows the cited sovereign-cloud growth rate given strong regulatory pressure and rising enterprise demand for AI data residency.

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