# Braintrust AI visibility in LLM Observability Evals & Gateways

Canonical: https://devtune.ai/verticals/llm-observability-evals-gateways/braintrust

[Website](https://www.braintrust.dev/)

Updated: 2026-10-23T02:39:47.872473+00:00
Prompts: 25
Runs: 5


## Platforms

- perplexity
- google-ai-mode
- bing-copilot-search
- google-ai
- chatgpt-search

Rank: 4
Total brands: 11
Measured responses: 125
Presence percent: 28.000000000000004
Share of voice percent: 15.919282511210762
Average position: 5.605633802816901
Docs presence percent: 10.4
Blog presence percent: 1.6
Brand mention percent: 53.6


## Profile

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.
Product summary: 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 capabilities

- 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



### Target users

- AI/ML engineers building and iterating on LLM-powered features
- Product managers overseeing AI product quality and release decisions
- Engineering leads at companies scaling AI applications to production
- Platform/infrastructure teams managing multi-model AI deployments
- Data scientists and evaluation specialists curating LLM benchmarks
- Enterprise teams requiring compliance, security, and auditability for AI outputs



### Key use cases

- 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

Integrations ecosystem: Braintrust natively supports 13+ AI frameworks including OpenTelemetry, Vercel AI SDK, OpenAI Agent SDK, LangChain, LangGraph, Google ADK, Mastra, Pydantic AI, AutoGen, and Instructor. SDKs are available for Python, TypeScript, Go, Ruby, and C#. Model providers covered include OpenAI, Anthropic, Google, and custom endpoints via the built-in AI Proxy. Developer tooling integrations include MCP server (for IDE-based log querying), Cursor, VS Code, Windsurf, Claude Code, Claude Desktop, and OpenAI Codex. The platform is listed on AWS Marketplace. A hybrid deployment option allows the Brainstore data plane to run within a customer's own cloud infrastructure for data residency and privacy requirements.
Pricing summary: 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.
Review summary: 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.
Competitive positioning: 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.
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.


### Source urls

- https://www.braintrust.dev/
- https://www.braintrust.dev/pricing
- https://www.braintrust.dev/blog/announcing-series-b
- https://www.braintrust.dev/docs
- https://www.braintrust.dev/customers/notion
- https://www.braintrust.dev/customers/coursera
- https://www.braintrust.dev/customers/dropbox
- https://siliconangle.com/2026/02/17/braintrust-lands-80m-series-b-funding-round-become-observability-layer-ai/
- https://a16z.com/announcement/investing-in-braintrust/
- https://venturebeat.com/ai/braintrust-data-wants-to-make-enterprise-ai-better-with-faster-evaluations
- https://www.g2.com/products/braintrust-2024-12-22/reviews
- https://checkthat.ai/brands/braintrust-2
- https://aws.amazon.com/marketplace/pp/prodview-ne7lhck4zaqdo
- https://tracxn.com/d/companies/braintrust/__oa36ci6gZZRhWulUAavDKOZLtVKKCXdoLz0NSTi-HOw

Reviewed at: 2026-05-06T17:53:55.569+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Notion | 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. | <24 hours to deploy a new frontier model |
| Coursera | Deployed AI-assisted grading via Braintrust evaluations, achieving 90% learner satisfaction and providing learners with dramatically more feedback per submission. | 45x more feedback with AI grading |
| Dropbox | 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. | 10,000+ tests in full eval suite |
| Zapier | 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. | 50% → 90%+ accuracy improvement |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| G2 | 4.5 | 5 | 159 | https://www.g2.com/products/braintrust-2024-12-22/reviews |



### Review themes



#### 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




### Company facts

Founded year: 2023
Hq: San Francisco, CA, USA


#### Founders

- Ankur Goyal

Employees range: 100-150
Total funding: $121M
Valuation: $800M
Arr: Not available
Customer count: Not available
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Arize AI | 43 | 125 | 34.4 | 5.953271028037383 |
| Langfuse | 38 | 125 | 30.4 | 5.378048780487805 |
| LangChain | 36 | 125 | 28.799999999999997 | 4.535211267605634 |
| Braintrust | 35 | 125 | 28.000000000000004 | 5.605633802816901 |
| LiteLLM | 23 | 125 | 18.4 | 6.06 |
| Portkey | 15 | 125 | 12 | 6.393939393939394 |
| Confident AI | 5 | 125 | 4 | 2.857142857142857 |
| Galileo | 4 | 125 | 3.2 | 5.222222222222222 |
| Helicone | 4 | 125 | 3.2 | 7.25 |
| Traceloop | 3 | 125 | 2.4 | 5 |
| Patronus AI | 1 | 125 | 0.8 | 6.25 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| perplexity | 7 | 28.000000000000004 |
| google-ai-mode | 11 | 44 |
| bing-copilot-search | 1 | 4 |
| google-ai | 8 | 32 |
| chatgpt-search | 8 | 32 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | 3 | 2.3333333333333335 |
| Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | 3 | 2.6666666666666665 |
| Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | 3 | 3 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | 5 |
| Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | 5 |
| Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | 4 |
| What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | 4 |
| Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | 3 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 3 |
| Developer Experience | 5 | 4 |
| Integrations & Ecosystem | 5 | 3 |
| Performance & Reliability | 5 | 3 |
| Setup & First Run | 5 | 2 |



## Prompt results

- Prompt text: I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?


#### Brand position by platform

Perplexity: 1
Google-ai-mode: 4
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: 2



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 4 |
| Confident AI | 8 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Arize AI | 7 |



##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Langfuse | 10 |


- Prompt text: 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.


#### Brand position by platform

Perplexity: 1
Google-ai-mode: 3
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |
| LangChain | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |



##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |
| Arize AI | 6 |


- Prompt text: What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: 7
Bing-copilot-search: Not available
Google-ai: 2
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Braintrust | 7 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |


- Prompt text: Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: 8
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |
| Arize AI | 6 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 4 |
| Portkey | 7 |
| Braintrust | 8 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |


- Prompt text: Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: 4
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Langfuse | 3 |
| LangChain | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Traceloop | 3 |
| Braintrust | 4 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |
| Arize AI | 4 |
| Langfuse | 6 |


- Prompt text: What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: 3
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| LangChain | 3 |
| Arize AI | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 3 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 3 |
| Galileo | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |
| Arize AI | 5 |


- Prompt text: Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: 3
Bing-copilot-search: Not available
Google-ai: 1
Chatgpt-search: 4



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Langfuse | 3 |
| LangChain | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 3 |
| LangChain | 4 |
| Arize AI | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |
| Galileo | 14 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 2 |
| Langfuse | 3 |
| Braintrust | 4 |


- Prompt text: Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?


#### Brand position by platform

Perplexity: 7
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 2 |
| Langfuse | 4 |
| Braintrust | 7 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 2 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |
| LangChain | 10 |


- Prompt text: Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| Helicone | 3 |
| LiteLLM | 6 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 2 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 14 |


- Prompt text: I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |
| Portkey | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 7 |


- Prompt text: Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 4 |



##### Google-ai-mode





##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Arize AI | 2 |



##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 4 |


- Prompt text: What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Portkey | 4 |
| LiteLLM | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Portkey | 4 |
| LiteLLM | 11 |


- Prompt text: Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong?


#### Brand position by platform

Perplexity: 3
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: 1
Chatgpt-search: 6



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 3 |
| Langfuse | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 3 |
| Langfuse | 6 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 4 |
| Braintrust | 6 |


- Prompt text: What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?


#### Brand position by platform

Perplexity: 3
Google-ai-mode: 3
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 3 |
| Arize AI | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 3 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 1 |


- Prompt text: Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 6 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 7 |


- Prompt text: What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |
| Portkey | 6 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 7 |


- Prompt text: Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics?


#### Brand position by platform

Perplexity: 2
Google-ai-mode: 1
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: 6



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Arize AI | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Patronus AI | 3 |
| Braintrust | 6 |
| LangChain | 7 |
| Arize AI | 10 |


- Prompt text: Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Galileo | 1 |
| Arize AI | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |


- Prompt text: Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: 1
Chatgpt-search: 9



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 4 |
| Langfuse | 7 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 2 |
| Langfuse | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 2 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 3 |
| Arize AI | 4 |
| LangChain | 6 |
| Braintrust | 9 |


- Prompt text: What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: 10
Bing-copilot-search: Not available
Google-ai: 1
Chatgpt-search: 6



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 3 |
| Langfuse | 6 |
| Braintrust | 10 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 4 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |
| Arize AI | 4 |
| Braintrust | 6 |


- Prompt text: What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Langfuse | 4 |



##### Google-ai-mode





##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |
| LangChain | 5 |
| Traceloop | 7 |
| Langfuse | 9 |


- Prompt text: Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: 5
Bing-copilot-search: 2
Google-ai: 1
Chatgpt-search: 9



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Helicone | 4 |
| Braintrust | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Galileo | 1 |
| Braintrust | 2 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 4 |
| LangChain | 8 |
| Braintrust | 9 |


- Prompt text: Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| Helicone | 3 |
| LiteLLM | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| Traceloop | 2 |
| LiteLLM | 4 |



##### Bing-copilot-search





##### Google-ai





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Helicone | 1 |
| LiteLLM | 2 |
| Portkey | 10 |
| Langfuse | 12 |


- Prompt text: Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?


#### Brand position by platform

Perplexity: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 2 |
| Arize AI | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 2 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Confident AI | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |


- Prompt text: Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?


#### Brand position by platform

Perplexity: 3
Google-ai-mode: 3
Bing-copilot-search: Not available
Google-ai: 3
Chatgpt-search: 1



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 3 |
| Arize AI | 5 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| LangChain | 4 |
| Langfuse | 5 |





## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://www.braintrust.dev/articles/best-llm-tracing-tools-2026 | Best LLM tracing tools for multi-agent systems (2026 review) - Articles - Braintrust | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | article | 20 | 20 |
| https://www.braintrust.dev/learn/tracing/v0 | AI tracing \| AI tracing tool \| LLM logging \| Braintrust | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | article | 9 | 9 |
| https://www.braintrust.dev/docs/evaluate/playgrounds | Iterate in playgrounds - Braintrust | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | listicle | 7 | 7 |
| https://www.braintrust.dev/articles/opentelemetry-llm-tracing-guide | OpenTelemetry for LLM tracing: a guide to instrumenting agents and ... | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | article | 7 | 7 |
| https://www.braintrust.dev/articles/best-rag-observability-tools-2026 | Best RAG observability tools (2026): monitor retrieval and generation in production - Articles - Braintrust | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | article | 6 | 6 |
| https://www.braintrust.dev/articles/best-human-in-the-loop-llm-evaluation-platforms-2026 | 8 best human-in-the-loop LLM evaluation platforms in 2026 - Articles - Braintrust | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | article | 6 | 6 |
| https://www.braintrust.dev/articles/best-llm-monitoring-tools-2026 | Best LLM monitoring tools in 2026 (tested & reviewed) - Articles - Braintrust | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | listicle | 6 | 6 |
| https://www.braintrust.dev/articles/llm-tracing-guide | LLM tracing: The complete guide - Articles - Braintrust | braintrust.dev | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/99f4d22aae36ac5538a48347be679b0fcbcbd743.png | commercial | article | 5 | 5 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | bing-copilot-search | The strongest options are Confident AI, Langfuse, LangSmith, Promptfoo, and Braintrust. ## 🔑 Key Platforms with Version Control Integration Platform\| Version Control Features\| CI/CD & Regression Gates\| Best Fit Use Case ---\|---\|---\|--- Confid... |
| 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. | bing-copilot-search | For collaborative LLM observability without SQL, the strongest options are Confident AI, LangSmith, and Braintrust — each designed so engineers and PMs can jointly review trace quality with intuitive workflows. ## Key Platforms for Team Collaboratio... |
| Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | bing-copilot-search | ...ad adoption Opik\| OTel ingestion path among many\| Yes\| Apache‑2.0\| Cheapest managed option, broad integrations Braintrust\| Accepts OTel traces, eval‑focused\| Limited self‑host\| Proprietary\| When evals gate releases LangSmith\| Accepts... |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| perplexity | LiteLLM | [1] 2. LiteLLM — easiest if you want a fuller admin workflow. The Docker Compose quickstart brings up the gateway and Postgres, then offers an Admin UI at `http://localhost:4000/ui` for adding providers and models, creating keys, and testing requests. |
| perplexity | Portkey | [2][1] Caveat on Portkey: Its open-source gateway is easy to launch with `npx`, but the self-hosting materials surfaced here describe enterprise or hybrid deployment paths rather than a similarly simple self-hosted UI quickstart—so I wouldn’t put i... |
| google-ai-mode | LiteLLM | Quickstart \| liteLLM[Routerly – open source self-hosted LLM gateway. |
| bing-copilot-search | LiteLLM | The simplest self‑hosted LLM gateways with a working UI on day one are LiteLLM (with its built‑in Admin UI or paired with Open WebUI) and Bifrost. Both can be containerized quickly, give you a browser interface immediately, and are designed for teams... |
| google-ai | LiteLLM | The two standout solutions with the simplest self-hosted setups and fully functioning UIs on day one are LiteLLM and Portkey . ### 1\. |
| chatgpt-search | LiteLLM | ## My shortlist ## 1. LiteLLM Proxy Best default for startups Best balance of setup simplicity and gateway features LiteLLM has a built-in Admin UI, an OpenAI-compatible endpoint, provider configuration, virtual keys, spend tracking, budgets, retri... |
| perplexity | Langfuse | Langfuse — scheduled exports of trace data as Parquet or JSONL to your own S3, GCS, or Azure storage, from which you can load it into a warehouse. |
| google-ai-mode | Langfuse | LangSmith, Langfuse, and Datadog LLM Observability are primary LLM tracing platforms that support exporting or syncing trace data into cloud data warehouses (such as BigQuery, Snowflake, and ClickHouse) so analysts can run custom evaluation queries a... |
| google-ai-mode | LangChain | langchain.com/langsmith/data-export-downstream)Export trace data to BigQuery - Docs by LangChain Other platforms like Arize Phoenix (via OpenTelemetry) allow direct integration into your... |
| bing-copilot-search | Arize AI | ...on\| Focused on BigQuery; may need engineering effort to set up pipelines Data Fabric: Querying Agent Traces in BigQuery - Arize AI Langfuse\| CSV export; can integrate with wa... |
| chatgpt-search | Langfuse | If your goal is to let analysts join LLM traces and evaluation results with product events, customer cohorts, conversions, and retention metrics in a warehouse, Langfuse is one of the clearest fits. |
| perplexity | Portkey | Portkey is the clearest match: it supports automatic retries and multi-provider fallback, and documents tracing all attempts in a fallback chain under the same Trace ID. |



## Trend

Visibility delta: 4.0000000000000036
Avg position delta: -0.48365191146881337
Citation count delta: 15