AI visibility report
AI visibility report for Helicone in AI/ML Infrastructure & LLM Tools.
Outside the top three on 17 of the 25 prompts buyers actually ask.
Braintrust is cited on 12 of those losses.
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Track Helicone across these prompts daily.
Start free trialStill absent from 98.7% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Perplexity
Bing Copilot
Google AI Mode
Gemini Search
GrokHow to read this. Helicone appears in 1.3% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Helicone is losing
Prompts where competitors are visible and Helicone is not.
These prompt-level losses are the first prompts to track and repair.
Where Helicone is winning
No clear strengths identified yet.
Where Helicone is losing5
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates?
Competitors on 3 platforms
Track this promptWhich LLM observability platforms support exporting trace data to BigQuery or Snowflake for custom analysis?
Competitors on 3 platforms
Track this promptWhich LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps?
Competitors on 3 platforms
Track this promptWhich LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side?
Competitors on 3 platforms
Track this promptWhich LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours?
Competitors on 3 platforms
Track this prompt
Track Helicone daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Helicone is an open-source AI gateway and LLM observability platform founded in 2023 as part of Y Combinator's W23 batch. It enables developers to monitor, route, debug, and optimize large language model applications through a one-line proxy integration—replacing the API base URL with Helicone's endpoint. The platform provides unified access to 100+ LLM providers, real-time cost and latency analytics, prompt versioning, semantic caching, agent session tracing, and configurable rate limiting. Helicone is licensed under Apache v2.0 and supports self-hosting via Docker or Helm. It is SOC 2 Type II and GDPR compliant. In March 2026, Helicone was acquired by Mintlify and transitioned to maintenance mode, having processed over 14.2 trillion tokens across 16,000 organizations.
Helicone is an open-source AI gateway and LLM observability platform that lets developers integrate in minutes by pointing their existing OpenAI SDK to Helicone's proxy URL. It combines a unified multi-provider gateway (100+ models, automatic fallbacks, semantic caching, rate limiting) with full-stack observability (request logs, cost tracking, latency metrics, agent session tracing, prompt versioning, and user analytics) in a single platform, deployable as SaaS or self-hosted.
Key Facts
- Founded
- 2023
- HQ
- San Francisco, CA, USA
- Founders
- Justin Torre, Cole Gottdank, Scott Nguyen
- Employees
- 10
- Funding
- ~$2M
- Customers
- 16,000 organizations
- Status
- Acquired by Mintlify (Mar 2026)
Target users
Key Capabilities10
- AI Gateway with unified access to 100+ LLM models via single API and zero-markup credits
- One-line proxy integration via baseURL change (no SDK rewrite required)
- Request logging with cost, latency, token usage, and time-to-first-token metrics
- Session and agent trace visualization for multi-step AI workflows
- Prompt versioning, testing, templates, and production deployment without code changes
- Semantic response caching to reduce API costs and latency
- Intelligent routing with automatic provider fallbacks and load balancing
- Custom rate limits, alerts, and real-time Slack/email notifications
- Self-hosting via Docker Compose or Helm with SOC 2 Type II and GDPR/HIPAA compliance
- HQL (Helicone Query Language) for custom request filtering and analytics
Key Use Cases8
- LLM cost monitoring and optimization for production AI applications
- Debugging and tracing multi-step AI agent workflows
- Prompt iteration and versioning across development and production
- Multi-provider LLM routing and failover for high-availability AI apps
- User-level usage and spend analytics for SaaS AI products
- Reducing LLM API spend via semantic response caching
- Compliance-friendly LLM observability in regulated industries (HIPAA, SOC 2)
- Unified LLM access management without managing multiple provider API keys
Helicone customer outcomes
386 hours saved via cached responses
Used Helicone's caching features to avoid redundant LLM calls in production workflows.
2 days saved on request analysis
Integrated Helicone to gain observability into LLM requests, reducing time spent manually combing through request logs.
30% reduction in agent runtime via early bug detection
Used Helicone monitoring to detect a critical bug in their AI agent pipeline during production.
Recent Trend
How AI describes Helicone3
Helicone : Known as the premier proxy-based solution for speed.
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?
Cost and Latency Issues: Helicone provides proxy-based observability, which is ideal for identifying which step caused cost spikes.
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?
Helicone (Easiest for Caching/Observability): A popular proxy-first tool that excels at automatic request logging, cost tracking, rate limiting, and caching with minimal configuration, offering both cloud-hosted and self-hosted options.
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates?
Most cited sources3
Alternatives in AI/ML Infrastructure & LLM Tools6
Helicone positioned itself as the fastest-to-integrate, open-source LLM observability and AI gateway platform, differentiating on one-line proxy setup (baseURL change only), zero-markup model access credits, built-in semantic caching, and a unified gateway plus observability product—contrasting with SDK-heavy competitors like LangSmith and Langfuse.
- It was notably the most-used LLM observability platform among Y Combinator companies.
- Following its acquisition by Mintlify in March 2026, the platform is in active maintenance mode.
Reviews
Praised
- One-line integration simplicity
- Intuitive and polished dashboard UI
- Immediate cost and latency visibility
- Steady cadence of feature updates
- Effective caching for reducing API spend
- Ease of debugging LLM issues in production
- Strong open-source community and transparency
Criticized
- Short data retention on lower-tier plans
- Platform now in maintenance mode post-acquisition
- Limited advanced evaluation capabilities vs. specialized tools
- Very small independent review volume
Helicone has a sparse but positive public review profile. On G2 it holds a 4.5/5 from 2 reviews; on Product Hunt it carries a 5.0/5 from 13 reviews. Reviewers consistently praise its ease of setup, intuitive dashboard, and immediate cost/latency visibility. One attributed quote from QA Wolf's Senior Director of AI calls it 'probably the most impactful one-line change I've seen applied to our codebase.' No substantive public criticism appears in available reviews. The low review volume limits statistical confidence.
Pricing
Hobby (Free): 10,000 requests/month, 1 GB storage, 7-day retention, 1 seat.
- Pro
$79/month plus usage-based overages, unlimited seats, 1-month retention, alerts, HQL, and reports; 7-day free trial.
- Team
$799/month plus usage-based, 5 organizations, 3-month retention, SOC 2/HIPAA compliance, dedicated Slack support; 7-day free trial.
- Enterprise
custom pricing with SAML SSO, on-prem deployment, unlimited organizations, forever retention, and bulk discounts. Usage-based components cover additional requests beyond 10K/month and storage beyond 1 GB. Discounts available for startups under 2 years old with under $5M in funding (50% off first year), nonprofits, open-source projects ($100 credit), and students/educators (free).
Limitations
- Platform entered maintenance mode following Mintlify acquisition in March 2026—new feature development has ceased, with only security updates, new model additions, and bug fixes continuing.
- Data retention is limited to 7 days on the free tier and 1 month on Pro, requiring a Team or Enterprise plan for longer retention.
- Evaluation and scoring capabilities are less mature than specialized platforms such as Braintrust.
- G2 review volume is very low (2 reviews), limiting independent third-party validation.
- Funding and employee scale are small relative to enterprise-focused competitors.
- The proxy-based integration adds ~50–80ms latency per request.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Bing Copilot | Google AI Mode | ChatGPT | Perplexity | Gemini Search | Grok |
|---|---|---|---|---|---|---|
Capability0/5 cited (0%) | ||||||
Which AI observability tools are best at detecting prompt injection attempts and guardrail violations in production LLM apps? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What ML platforms handle dataset versioning alongside model versioning so you can reliably reproduce a training run from six months ago? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about? | 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 | A competitor was cited | Neither your brand nor a competitor was cited |
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? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience1/5 cited (20%) | ||||||
Which AI infrastructure platforms support running the same orchestration logic locally against a mock LLM before deploying to production? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
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? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Your brand was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure? | 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 | Neither your brand nor a competitor was cited |
Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Integrations & Ecosystem1/5 cited (20%) | ||||||
What AI infrastructure platforms handle multi-model setups well — letting you switch between LLM providers and open-source models without rewriting application code? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
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? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms support exporting trace data to BigQuery or Snowflake for custom analysis? | A competitor 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 | A competitor was cited | Neither your brand nor a competitor was cited |
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Performance & Reliability0/5 cited (0%) | ||||||
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production? | 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 | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which managed LLM inference platforms handle cold starts well — is there a way to keep a model warm without paying for idle GPU time? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM infrastructure platforms give the best cost-to-latency balance for a high-throughput app doing 10,000 requests per hour? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Setup & First Run0/5 cited (0%) | ||||||
What platforms can affordably serve a fine-tuned 7B parameter model with low latency for a production app without requiring a dedicated ML team? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week? | 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 | A competitor was cited | Neither your brand nor a competitor was cited |
What tools let you set up a RAG pipeline evaluation framework to measure retrieval quality and answer accuracy before going to production? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
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? | 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 | Neither your brand nor a competitor was cited | Neither your brand nor 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 | 13.3% | 38.2% | 0.0% | 0.7% | 16.7% | #4.0 | +0.45 |
| 2 | LangChain | 4.7% | 11.8% | 2.0% | 0.0% | 26.7% | #3.2 | +0.50 |
| 3 | MLflow | 4.7% | 15.8% | 0.0% | 0.0% | 14.0% | #4.0 | +0.56 |
| 4 | Langfuse | 4.7% | 18.4% | 1.3% | 1.3% | 16.7% | #5.6 | +0.46 |
| 5 | Weights & Biases | 2.0% | 3.9% | 0.7% | 0.0% | 14.7% | #4.0 | +0.50 |
| 6 | Fireworks AI | 1.3% | 2.6% | 0.7% | 0.7% | 5.3% | #1.0 | -0.08 |
| 7 | Comet ML | 1.3% | 2.6% | 0.0% | 0.0% | 2.0% | #2.5 | +0.20 |
| 8 | Modal | 1.3% | 2.6% | 0.0% | 1.3% | 0.0% | #3.0 | +0.25 |
| 9 | Helicone | 1.3% | 3.9% | 0.7% | 0.7% | 11.3% | #6.3 | +0.69 |
| 10 | Anyscale | 0.0% | 0.0% | 0.0% | 0.0% | 1.3% | — | — |
| 11 | LiteLLM | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 12 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 4.0% | — | — |
| 13 | Together AI | 0.0% | 0.0% | 0.0% | 0.0% | 8.7% | — | — |
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