AI visibility report
Braintrust ranks #1 in LLM Observability Evals & Gateways AI search.
Outside the top three on 9 of the 25 prompts buyers actually ask.
Langfuse is cited on 6 of those losses.
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Start free trialBest among 11 vendors · still absent from 68.8% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Perplexity
ChatGPT
Google AI Mode
Gemini Search
Bing CopilotLeader, with room to expand. Braintrust leads this category on presence and share of voice, but appears in only 31.2% 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 winning5
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?
Avg # 1.0 · 1 platform
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge.
Avg # 1.0 · 1 platform
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?
Avg # 2.0 · 2 platforms
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance?
Avg # 2.0 · 1 platform
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?
Avg # 2.0 · 3 platforms
Where Braintrust is losing5
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
Competitors on 4 platforms
Track this promptWhich LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?
Competitors on 4 platforms
Track this promptWhat LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams?
Competitors on 3 platforms
Track this promptWhich LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation?
Competitors on 3 platforms
Track this promptWhich LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?
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
Short answer: Platforms like Confident AI, Braintrust, Future AGI, Langfuse, and Promptfoo integrate directly with version control (Git-style branching, CI/CD hooks, regression gates) so prompt regressions can be tied back to specific commits or...
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?
Braintrust: Eval-driven development and prompt optimization workflows. Strong for engineering teams, but less focused on real-time production monitoring and non-technical collaboration.static.runit.aistatic.runit.ai.
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge.
Inside the LLM Call: GenAI Observability with OpenTelemetry | OpenTelemetry | | Braintrust | ✅ OTLP spans + quality scoring | Adds correctness/grounding checks | Evaluates LLM outputs against policies | Proprietary layer; requires adoption of Braint...
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?
Most cited sources8
68Best LLM tracing tools for multi-agent systems (2026 review) - Articles - Braintrust
braintrust.dev·Article
- 24
7 best prompt playgrounds for PMs in 2026 - Articles - Braintrust
braintrust.dev·Article
- 19
6 best LLM gateways for developers in 2026 - Articles - Braintrust
braintrust.dev·Article
- 12
Best AI Eval Tools for CI/CD Pipelines (2026 Review) - Articles - Braintrust
braintrust.dev·Listicle
- 10
Top 10 LLM observability tools: Complete guide for 2025 - Articles - Braintrust
braintrust.dev·Article
- 10
Best RAG observability tools (2026): monitor retrieval and generation in production - Articles - Braintrust
braintrust.dev·Article
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 | Bing Copilot | Google AI Mode | Perplexity | ChatGPT |
|---|---|---|---|---|---|
Capability3/5 cited (60%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | Your brand was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Developer Experience5/5 cited (100%) | |||||
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | Your brand was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | Your brand was cited |
Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Your brand and a competitor were cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | Your brand and a competitor were cited |
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge. | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited |
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited |
Integrations & Ecosystem3/5 cited (60%) | |||||
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited |
What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Performance & Reliability4/5 cited (80%) | |||||
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Your brand and a competitor were cited | A competitor was cited |
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | Your brand and a competitor were cited |
Setup & First Run5/5 cited (100%) | |||||
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | Your brand was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | A competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | Your brand was cited |
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited | Your brand was cited |
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Your brand and a competitor were cited | A competitor was cited |
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | Neither your brand nor a competitor was cited | Your brand was cited | Your brand was cited | Your brand and a competitor were cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 31.2% | 28.3% | 2.4% | 0.0% | 43.2% | #4.0 | +0.43 |
| 2 | LangChain | 20.8% | 13.7% | 3.2% | 0.0% | 51.2% | #4.4 | +0.45 |
| 3 | Langfuse | 16.8% | 13.3% | 4.8% | 3.2% | 56.8% | #2.8 | +0.48 |
| 4 | Confident AI | 16.0% | 17.3% | 0.0% | 0.0% | 12.8% | #5.7 | +0.42 |
| 5 | Galileo | 12.8% | 7.5% | 0.0% | 12.8% | 13.6% | #3.5 | +0.38 |
| 6 | Arize AI | 8.8% | 8.0% | 0.0% | 4.0% | 8.0% | #4.3 | +0.53 |
| 7 | Traceloop | 5.6% | 4.0% | 0.0% | 4.8% | 6.4% | #4.4 | +0.37 |
| 8 | Portkey | 4.0% | 2.7% | 0.8% | 0.0% | 18.4% | #3.0 | +0.26 |
| 9 | LiteLLM | 4.0% | 2.7% | 0.8% | 0.0% | 0.0% | #3.2 | +0.56 |
| 10 | Helicone | 1.6% | 0.9% | 0.8% | 0.8% | 24.8% | #5.0 | +0.45 |
| 11 | Patronus AI | 1.6% | 1.8% | 1.6% | 0.8% | 3.2% | #8.5 | +0.79 |
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