AI visibility report for Braintrust
Vertical: AI/ML Infrastructure & LLM Tools
AI search visibility benchmark across 5 platforms in AI/ML Infrastructure & LLM Tools.
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Presence Rate
Top-3 citations across 125 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Google AI Mode
Gemini Search
ChatGPT
Perplexity
GrokOverview
Braintrust (braintrust.dev) is a proprietary AI observability and evaluation platform designed for engineering and product teams shipping LLM-powered applications. Founded in 2023 by Ankur Goyal, the California-based company provides end-to-end tooling spanning production tracing, prompt experimentation, automated evaluation, and CI/CD integration in a single unified workspace. Its purpose-built database, Brainstore, is engineered for high-throughput AI trace queries at scale. The platform is framework-agnostic, integrating natively with OpenAI, Anthropic, LangChain, Vercel AI SDK, and OpenTelemetry, with SDKs across Python, TypeScript, Go, Ruby, C#, and Java. Customers include Notion, Dropbox, Stripe, Vercel, Zapier, Coursera, Ramp, and Replit. In February 2026, Braintrust raised an $80M Series B led by ICONIQ at an $800M valuation.
Braintrust is a unified AI observability and evaluation platform that helps engineering and product teams trace LLM production traffic, run structured evals, manage and version prompts, and catch regressions before they reach users—powered by Brainstore, a purpose-built database for AI trace data, and Loop, an AI agent for autonomous eval optimization.
Key Facts
- Founded
- 2023
- HQ
- California, USA
- Founders
- Ankur Goyal
- Funding
- ~$125M
- Valuation
- $800M
- Status
- Private
Target users
Key Capabilities10
- Production tracing and observability: full-span capture of prompts, tool calls, responses, latency, and cost in real time
- LLM evaluation (evals) with automated scoring via LLM-as-judge, code scorers, and human annotation
- Prompt engineering playground with side-by-side model and prompt comparison
- CI/CD-integrated regression detection and deployment blocking before production release
- Versioned dataset management with one-click trace-to-dataset conversion from production failures
- Brainstore: proprietary purpose-built database for fast full-text search and querying of AI traces at scale
- Loop agent: AI-assisted autonomous prompt optimization, scorer generation, and test case creation
- Multi-language SDKs (Python, TypeScript, Go, Ruby, C#, Java) with framework-agnostic instrumentation
- Enterprise security: SOC 2 Type II, HIPAA, GDPR, SSO/SAML, RBAC, and hybrid deployment
- MCP server and CLI enabling IDE-native and agent-driven access to logs, evals, and prompts
Key Use Cases8
- Pre-deployment LLM output quality evaluation and regression testing in CI/CD pipelines
- Production monitoring and real-time alerting on AI quality, latency, and cost
- Multi-model and prompt experimentation with quantified side-by-side comparison
- Agent tracing and debugging for complex multi-step agentic workflows
- Converting production edge cases and failures into structured eval datasets
- Human-in-the-loop annotation and review workflows for AI output quality
- Cross-functional AI quality collaboration between engineering and product teams
- Compliance-grade AI observability for regulated industries (healthcare, fintech)
Braintrust customer outcomes
<24 hours to deploy new frontier model
Notion aligned 70 engineers on a shared evaluation framework using Braintrust and was able to deploy new frontier models within hours of their public release by running regression and frontier evals in parallel.
45x more feedback with AI grading
Coursera implemented AI-assisted grading with Braintrust-backed evaluation workflows, delivering grades within one minute of submission and dramatically increasing feedback volume for learners.
50% to 90%+ accuracy improvement in 2–3 months
Zapier used Braintrust's logging, dataset management, and eval workflows to iterate their AI products from initial prototype to production quality within 2–3 months.
5% reduction in negative rules
Graphite used Braintrust to build reliable AI code review at scale, iterating on evaluation datasets to reduce undesirable model outputs in their review pipeline.
10,000+ tests in full eval suite
Dropbox built a comprehensive evaluation pipeline for AI search using Braintrust, enabling hundreds to thousands of experiments and creating a full eval suite to maintain quality at scale.
Recent Trend
How AI describes Braintrust3
Braintrust Gateway : A unified API router covering OpenAI, Anthropic, Vertex AI, AWS Bedrock, Mistral, and more.
Which AI/ML platforms have the best compliance story for SOC 2 and data residency — ensuring training data and model outputs stay in a specific region?
Braintrust : Built for enterprise. Combines tracing with automated regression testing.
Which AI observability tools are best at detecting prompt injection attempts and guardrail violations in production LLM apps?
Braintrust : A managed platform that offers an incredibly generous free tier (including 1 million tracked spans).
What are the best ML experiment tracking tools for a team currently logging metrics to spreadsheets — which ones get you value fast with minimal setup?
Most cited sources8
136 best LLM gateways for developers in 2026 - Articles - Braintrust
braintrust.dev·Article
- 12
Best AI evals tools for CI/CD in 2025 - Articles - Braintrust
braintrust.dev·Article
- 11
The 5 best prompt versioning tools in 2025 - Articles - Braintrust
braintrust.dev·Article
- 9
Best AI evals products for self-hosted / on-prem enterprise deployments (2026) - Articles - Braintrust
braintrust.dev·Article
- 9
Best LLM tracing tools for multi-agent systems (2026 review) - Articles - Braintrust
braintrust.dev·Article
- 7
4 best LLM gateways for observability: tracing, cost attribution, and debuggability - Articles - Braintrust
braintrust.dev·Article
Alternatives in AI/ML Infrastructure & LLM Tools6
Braintrust positions itself as the most complete, 'batteries-included' LLM evaluation and observability platform for cross-functional AI product teams.
- It differentiates from framework-coupled tools (LangSmith) by being framework-agnostic; from open-source alternatives (Langfuse) through its proprietary Brainstore database for high-speed trace queries and richer CI/CD-native deployment blocking; from pure observability tools (Helicone) by combining full-lifecycle evaluation with tracing; and from general-purpose ML trackers (MLflow, Comet) by being purpose-built for LLM and agentic workloads.
- Its dual focus on both engineering-code workflows and no-code UI for PMs sets it apart from developer-only tools.
Reviews
Praised
- All-in-one platform (evals, tracing, and prompt playground in one place)
- Intuitive UI accessible to both engineers and product managers
- Fast to instrument and start tracing with minimal code
- Powerful for tracking LLM prompt and pipeline improvements
- High-performance trace search and querying via Brainstore
- Strong customer focus and responsive product team
Criticized
- Pricing structure and usage-based cost calculations can be unclear
- No self-hosting option; proprietary closed-source platform
- No real-time guardrails to block bad outputs before reaching users
- Platform stability and feature consistency issues noted by early adopters
- Engineering-centric design limits accessibility for non-technical stakeholders
- Data retention limits on lower tiers restrict long-term trace analysis
Braintrust holds a 4.5/5 rating on G2 from approximately 159 reviews. Users consistently praise the all-in-one nature of the platform combining evals, observability, and a prompt playground, its intuitive UI, fast setup, and its cross-functional accessibility for both engineers and PMs. Criticism centers on pricing transparency, lack of self-hosting, occasional platform stability concerns during rapid growth, and some users noting the absence of real-time guardrail capabilities.
Pricing
Braintrust uses a freemium, usage-based model with three tiers. Starter is free ($0/month), including 1 GB processed data (+$4/GB overage), 10,000 scores (+$2.50/1k overage), 14-day data retention, and unlimited users, projects, datasets, playgrounds, and experiments. Pro is $249/month, including 5 GB processed data (+$3/GB overage), 50,000 scores (+$1.50/1k overage), 30-day retention, custom topics, charts, environments, and priority support. Enterprise is custom-priced, adding custom data retention, S3 export, RBAC, BAA for HIPAA, uptime SLA, shared Slack support, and on-premises or hybrid Brainstore deployment. A free trial is available.
Limitations
- Braintrust is a proprietary closed-source platform with no self-hosting option, which is a stated concern for teams requiring full data sovereignty (unlike open-source Langfuse).
- The platform evaluates AI outputs after the fact and does not provide real-time guardrails to block harmful outputs before they reach users.
- It is not a model training, fine-tuning, or inference deployment platform.
- Some users report limited self-serve pricing clarity and difficulty understanding usage-based cost calculations.
- Its engineering-centric design and deep eval focus may be less accessible for non-technical stakeholders without additional onboarding.
- Deepest framework-specific tracing is available for LangChain users via LangSmith.
Frequently asked questions
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Perplexity | Grok | ChatGPT | Google AI Mode |
|---|---|---|---|---|---|
Capability1/5 cited (20%) | |||||
I'm evaluating managed LLM inference platforms versus self-hosted GPU instances for a high-traffic workload — what are the key trade-offs and what should I look at? | |||||
Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about? | |||||
What ML platforms handle dataset versioning alongside model versioning so you can reliably reproduce a training run from six months ago? | |||||
Which AI observability tools are best at detecting prompt injection attempts and guardrail violations in production LLM apps? | |||||
Which LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours? | |||||
Developer Experience5/5 cited (100%) | |||||
Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side? | |||||
What ML experiment tracking tools handle multi-user collaboration well — so multiple data scientists can work on the same project without stepping on each other's runs? | |||||
Which AI infrastructure platforms support running the same orchestration logic locally against a mock LLM before deploying to production? | |||||
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure? | |||||
Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options? | |||||
Integrations & Ecosystem3/5 cited (60%) | |||||
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production? | |||||
Which AI/ML platforms have the best compliance story for SOC 2 and data residency — ensuring training data and model outputs stay in a specific region? | |||||
Which LLM observability platforms support exporting trace data to BigQuery or Snowflake for custom analysis? | |||||
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs? | |||||
What AI infrastructure platforms handle multi-model setups well — letting you switch between LLM providers and open-source models without rewriting application code? | |||||
Performance & Reliability3/5 cited (60%) | |||||
Which managed LLM inference platforms handle cold starts well — is there a way to keep a model warm without paying for idle GPU time? | |||||
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps? | |||||
What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production? | |||||
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates? | |||||
What LLM infrastructure platforms give the best cost-to-latency balance for a high-throughput app doing 10,000 requests per hour? | |||||
Setup & First Run2/5 cited (40%) | |||||
What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code? | |||||
What tools let you set up a RAG pipeline evaluation framework to measure retrieval quality and answer accuracy before going to production? | |||||
Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week? | |||||
What platforms can affordably serve a fine-tuned 7B parameter model with low latency for a production app without requiring a dedicated ML team? | |||||
What are the best ML experiment tracking tools for a team currently logging metrics to spreadsheets — which ones get you value fast with minimal setup? | |||||
Strengths5
Which AI infrastructure platforms support running the same orchestration logic locally against a mock LLM before deploying to production?
Avg # 1.0 · 1 platform
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs?
Avg # 1.0 · 1 platform
What are the best ML experiment tracking tools for a team currently logging metrics to spreadsheets — which ones get you value fast with minimal setup?
Avg # 1.0 · 1 platform
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure?
Avg # 2.5 · 2 platforms
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production?
Avg # 3.0 · 2 platforms
Gaps5
Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side?
Competitors on 1 platform
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps?
Competitors on 1 platform
Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about?
Competitors on 1 platform
What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production?
Competitors on 1 platform
Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 14.4% | 39.8% | 0.8% | 0.0% | 13.6% | #8.2 | +0.23 |
| 2 | LangChain | 9.6% | 19.4% | 3.2% | 0.0% | 8.8% | #11.1 | +0.19 |
| 3 | Weights & Biases | 4.8% | 8.7% | 0.8% | 0.0% | 4.0% | #6.6 | +0.15 |
| 4 | Langfuse | 4.8% | 11.7% | 0.0% | 1.6% | 4.8% | #9.9 | +0.56 |
| 5 | Modal Labs | 4.0% | 8.7% | 1.6% | 3.2% | 4.0% | #8.0 | +0.00 |
| 6 | MLflow | 3.2% | 4.9% | 0.0% | 0.0% | 3.2% | #6.0 | +0.00 |
| 7 | Anyscale | 1.6% | 2.9% | 1.6% | 0.8% | 1.6% | #17.7 | +0.00 |
| 8 | BerriAI (LiteLLM) | 1.6% | 2.9% | 1.6% | 0.0% | 1.6% | #17.7 | +0.00 |
| 9 | Comet ML | 0.8% | 1.0% | 0.0% | 0.0% | 0.8% | #10.0 | +0.80 |
| 10 | Fireworks AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 11 | Helicone | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 12 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 13 | Together AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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