AI visibility report
Braintrust ranks #1 in LLM Observability Evals & Gateways AI search.
Outside the top three on 11 of the 25 prompts buyers actually ask.
Langfuse is cited on 5 of those losses.
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Track Braintrust across these prompts daily.
Start free trialBest among 11 vendors · still absent from 73.3% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
ChatGPT
PerplexityLeader, with room to expand. Braintrust leads this category on presence and share of voice, but appears in only 26.7% of tracked prompt responses. The priority is defending current wins while expanding absolute coverage.
Where Braintrust is losing
Prompts where competitors are visible and Braintrust is not.
These prompt-level losses are the first prompts to track and repair.
Where Braintrust is winning4
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency?
Avg # 1.0 · 1 platform
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for?
Avg # 1.0 · 2 platforms
What AI eval platforms support on-premise or VPC deployment for regulated industries?
Avg # 3.0 · 3 platforms
Which evaluation platforms let me convert development-time evals into production guardrails automatically?
Avg # 3.0 · 2 platforms
Where Braintrust is losing5
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK?
Competitors on 3 platforms
Track this promptWhich LLM eval platforms support running automated evaluations on production traces with custom metrics?
Competitors on 3 platforms
Track this promptWhich AI observability platforms can be self-hosted with one command using Docker Compose?
Competitors on 2 platforms
Track this promptWhich observability tools include real-time alerting on quality drops, not just latency?
Competitors on 2 platforms
Track this promptWhat's the fastest way to start tracing my LLM application calls without rewriting my code?
Competitors on 2 platforms
Track this prompt
Track Braintrust daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Braintrust (braintrust.dev) is an AI observability and evaluation platform founded in 2023 by Ankur Goyal and headquartered in San Francisco. Built specifically for teams shipping LLM-powered products into production, it unifies trace logging, automated evaluations, prompt management, and dataset curation in a single workflow. The platform's core architecture is built on Brainstore, a purpose-built database designed for AI-trace workloads. Its 'Instrument → Observe → Annotate → Evaluate → Deploy' lifecycle lets engineering and product teams capture every prompt and tool call in production, score outputs using LLM-as-a-judge or custom code, convert failures into test cases with one click, and gate releases with CI/CD-integrated evals. Customers include Notion, Dropbox, Replit, Coursera, Cloudflare, Ramp, Stripe, Zapier, and Vercel. In February 2026 Braintrust raised an $80M Series B led by ICONIQ at an $800M valuation.
Braintrust is an end-to-end AI observability and evaluation platform that connects production trace logging with structured evaluation workflows in a single developer-centric product. It captures every LLM call, tool invocation, and agent reasoning step as hierarchical spans; scores outputs using LLM-as-a-judge, heuristic, or human annotation; manages versioned prompts; and enables teams to build regression datasets directly from production failures. Its Loop AI agent automates prompt optimization and dataset generation based on trace data, while Brainstore—a purpose-built database for AI logs—powers high-speed full-text search and querying across millions of traces. Braintrust is framework-agnostic, supports 13+ native integrations, and offers enterprise security including SOC 2 Type II, HIPAA compliance, and hybrid deployment.
Key Facts
- Founded
- 2023
- HQ
- San Francisco, CA, USA
- Founders
- Ankur Goyal
- Employees
- 100-150
- Funding
- $121M
- Valuation
- $800M
- Status
- Private
Target users
Key Capabilities10
- Production trace logging with full span capture (prompts, tool calls, latency, cost)
- Offline and online LLM evaluation (LLM-as-a-judge, code-based, and human scorers)
- Prompt management with versioning, playground, and side-by-side comparison
- Brainstore: purpose-built AI-trace database with claimed 80x faster query performance
- Loop AI agent for automated prompt optimization and dataset generation
- Trace-to-dataset one-click conversion for regression testing from production failures
- CI/CD integration for automated eval gating on pull requests
- Human annotation and review workflows with customizable trace views
- AI gateway / proxy supporting 100+ models with routing and cost tracking
- Enterprise security: SOC 2 Type II, HIPAA, RBAC, SAML SSO, hybrid deployment
Key Use Cases8
- Catching LLM regressions before production deployment via CI/CD-gated evals
- Monitoring production AI for hallucinations, drift, and quality degradation
- Prompt engineering and model comparison across providers
- Building and curating evaluation datasets from real production traces
- Scaling eval workflows across cross-functional engineering and product teams
- Deploying new frontier models rapidly with automated regression testing
- Debugging complex multi-step agentic workflows via hierarchical trace inspection
- Compliance and safety evaluation for enterprise AI deployments
Braintrust customer outcomes
<24 hours to deploy a new frontier model
Aligned 70 engineers on a unified evaluation framework using Braintrust, enabling the AI team to deploy new frontier models within hours of release rather than weeks.
45x more feedback with AI grading
Deployed AI-assisted grading via Braintrust evaluations, achieving 90% learner satisfaction and providing learners with dramatically more feedback per submission.
10,000+ tests in full eval suite
Built a multi-tier evaluation pipeline for Dropbox Dash AI search, graduating from spreadsheets to a comprehensive system with pre-merge smoke tests, a full post-merge suite, and real-time online LLM-as-a-judge scoring in production.
50% → 90%+ accuracy improvement
Used Braintrust to move from ad-hoc hallucination detection to a systematic dataset-driven evaluation framework, dramatically improving AI accuracy across millions of automated tasks per month.
Recent Trend
How AI describes Braintrust3
* Braintrust ---------- * Deployment: Self-hosted / private cloud options * Strengths: * Experiment tracking for prompts + agents * Human + LLM judge workflows * Regression testing for prompt...
What AI eval platforms support on-premise or VPC deployment for regulated industries?
...----------------------- These are the closest thing to “end-to-end RLHF platforms” (trace → label → compare → feed back into training/evals): ### Braintrust Strongest “developer-native” option for structured human evaluation inside CI-style workflows.
Which LLM platforms have the best workflows for human annotation and labeling of model outputs?
\[1\] | | Braintrust | Excellent evals | Limited native guardrails | No | Strong evaluation system, but runtime enforcement usually requires another layer.
Which evaluation platforms let me convert development-time evals into production guardrails automatically?
Most cited sources8
20Best AI evals products for self-hosted / on-prem enterprise deployments (2026) - Articles - Braintrust
braintrust.dev·Article
- 18
Best LLM tracing tools for multi-agent systems (2026 review) - Articles - Braintrust
braintrust.dev·Article
- 14
7 best tools for debugging AI agents in production (2026) - Articles - Braintrust
braintrust.dev·Article
- 9
AI observability tools: A buyer's guide to monitoring AI agents in production (2026) - Articles - Braintrust
braintrust.dev·Article
- 6
Best RAG observability tools (2026): monitor retrieval and generation in production - Articles - Braintrust
braintrust.dev·Article
- 5
Evaluate systematically - Braintrust
braintrust.dev·Documentation
Alternatives in LLM Observability Evals & Gateways6
Braintrust positions itself as the unified 'quality layer' for production AI, differentiating from point solutions by tightly coupling observability and evals in a single workflow atop Brainstore, its purpose-built AI-trace database.
- It emphasizes first-class JavaScript/TypeScript support alongside Python, end-to-end lifecycle coverage from prompt experimentation through production monitoring, and enterprise-grade security (SOC 2 Type II, HIPAA, RBAC, hybrid deployment).
- Key differentiators include Brainstore's claimed 80x faster trace search versus traditional databases, the Loop AI eval agent for automated prompt optimization, and a 'trace-to-dataset' one-click workflow that competitors typically require manual steps to replicate.
- Braintrust targets teams that want a fully managed, deeply integrated platform rather than open-source self-hosted tooling.
Reviews
Praised
- All-in-one evals and observability in one workflow
- Fast and intuitive UI
- Trace-to-dataset one-click conversion
- Strong CI/CD eval integration
- Cross-team collaboration for PMs and engineers
- Brainstore performance on large trace datasets
- Responsive to customer product feedback
- Quick time-to-value for initial setup
Criticized
- Short data retention on Starter and Pro tiers
- Customer support response times
- No open-source or self-hosted option
- Occasional platform stability and bug issues
- Enterprise pricing opacity
- Learning curve for advanced custom scorers
Public reception is positive among developer and AI engineering audiences. On G2, the platform holds a 4.5/5 rating from approximately 159 verified reviews. Users consistently praise the all-in-one combination of evals, observability, and prompt tooling; the intuitive and fast UI; the trace-to-dataset workflow; and the platform's value for cross-functional collaboration between engineers and product managers. Recurring criticisms include customer support responsiveness, short data retention windows on lower tiers, the absence of a self-hosted option, and occasional platform stability issues as the product continues to mature rapidly.
Pricing
Braintrust offers three tiers. Starter is free and includes 1 GB processed data per month, 10,000 scores, 14-day retention, and unlimited users, projects, datasets, playgrounds, and experiments; overages are $4/GB and $2.50 per 1,000 scores. Pro is $249/month with 5 GB processed data, 50,000 scores, 30-day retention, custom topics, custom charts, environments, and priority support; overages at $3/GB and $1.50 per 1,000 scores. Enterprise offers custom pricing with custom data retention and export, RBAC, custom security agreements (BAA, DPA, uptime SLA), shared Slack support, and on-premises or hosted Brainstore deployment for high-volume or privacy-sensitive workloads.
Limitations
- Short data retention windows on lower tiers (14 days on Starter, 30 days on Pro) require Enterprise for custom policies.
- No open-source or self-hosted option for cost-sensitive teams, unlike Langfuse or Arize Phoenix.
- G2 reviewers flag customer support response times and inconsistency as a recurring concern.
- Some users report occasional platform stability issues and bugs as the product matures.
- Enterprise pricing is custom/opaque with no self-serve access to higher-tier features.
- The platform's depth can introduce a learning curve for teams new to structured evals.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation3/5 cited (60%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing1/5 cited (20%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness3/5 cited (60%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run2/5 cited (40%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging4/5 cited (80%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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