Braintrust - 2026 Pricing, Features, Reviews & Alternatives

Braintrust is an AI observability and evaluation platform that helps organizations build, monitor, and improve artificial intelligence products at scale. The platform supports AI engineering teams, product managers, and organizations that deploy large language models and AI agents in production environments. Braintrust addresses the unique challenges of monitoring and evaluating AI systems by providing comprehensive tracing, evaluation frameworks, and automated pattern discovery capabilities. The platform serves early stage startups building their first AI agents as well as enterprise organizations managing AI systems across dozens of engineers and multiple use cases.

The platform is structured around three interconnected pillars of observability, evaluation, and automation. The observability pillar provides real time trace inspection that captures every prompt, response, and tool call within AI workflows, enabling teams to search across millions of logs and track key metrics such as latency, cost, and quality scores. Custom views and annotations can be created to match specific team workflows without requiring frontend development. The evaluation pillar enables teams to define quality benchmarks through experiments run against versioned datasets and to compare different prompts and models side by side. Evaluation scoring can be performed by large language models, custom code, or human review. The automation pillar centers on Topics, a feature that surfaces patterns across production traces by clustering data according to dimensions such as use case, customer segment, sentiment, and issue type, and supports continuous online scoring with quality gates that block releases when performance thresholds are not met.

Features include Loop, an AI powered optimization agent that generates improved prompts, scorers, and datasets based on performance data and user objectives. A Model Context Protocol server enables developers to query logs, run evaluations, and update prompts directly from integrated development environments, connecting coding agents to the broader AI stack. Braintrust is framework agnostic and integrates with existing technology stacks without requiring rewrites or vendor lock in. Native software development kits are available for Python, TypeScript, Go, Ruby, and C sharp, which allow teams to implement production tracing with only a few lines of code. Production traces can be converted into evaluation datasets with a single click, enabling teams to build regression tests from real failures and edge cases instead of relying on synthetic examples. The underlying infrastructure is powered by Brainstore, a proprietary database designed specifically for nested AI trace data, which optimizes full text search, write latency, and span load times compared to traditional systems.

The platform maintains SOC two Type two certification and GDPR compliance, with support for HIPAA compliant deployments, single sign on via SAML, and role based access control at granular project and resource levels. Hybrid deployment options allow organizations to run the Brainstore data plane on their own infrastructure while retaining access to the platform control plane and analytical capabilities. Comprehensive integration support for major AI providers and frameworks ensures compatibility with diverse technology ecosystems. Organizations can configure custom alerts and quality gates based on performance thresholds to enable proactive issue detection and automated release blocking. Live performance monitoring dashboards provide cost breakdowns across model components including completion tokens, prompt cache writes, cache reads, and standard prompt tokens as part of a scalable trace ingestion architecture capable of handling enterprise production workloads.