AI visibility report
Helicone ranks #10 in LLM Observability Evals & Gateways AI search.
Outside the top three on 25 of the 25 prompts buyers actually ask.
Braintrust is cited on 20 of those losses.
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Start free trial#10 among 11 vendors · still absent from 97.6% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
Google AI Mode
Perplexity
Bing CopilotNarrower footprint, stronger tone. Helicone ranks #10 on presence but #1 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
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
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.
Competitors on 5 platforms
Track this promptWhich LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?
Competitors on 5 platforms
Track this promptWhich LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?
Competitors on 5 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 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 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 launched in 2023 through Y Combinator's W23 batch. It enables AI engineers to log, monitor, debug, and analyze LLM applications via a one-line code change that routes traffic through Helicone's proxy. The platform combines a unified AI gateway—providing access to 100+ models with intelligent routing, automatic fallbacks, and response caching—with full-stack observability covering request tracing, cost and latency analytics, prompt versioning, session tracking, and evaluation scoring. Available as a managed cloud service or self-hosted via Docker or Helm, Helicone supports major providers (OpenAI, Anthropic, Azure, AWS Bedrock, Google Gemini) and frameworks (LangChain, LlamaIndex, Vercel AI SDK). In March 2026, Helicone was acquired by Mintlify and transitioned to maintenance mode.
Helicone is an open-source LLM observability platform and AI gateway that lets developers instrument their LLM applications with a single line of code. It captures all request and response data, provides dashboards for cost, latency, and quality metrics, and acts as a multi-provider gateway supporting 100+ models with caching, fallbacks, and rate limiting. The platform is self-hostable under the Apache 2.0 license and was used by over 16,000 organizations before being acquired by Mintlify in March 2026.
Key Facts
- Founded
- 2023
- HQ
- San Francisco, CA, USA
- Founders
- Justin Torre, Cole Gottdank, Scott Nguyen
- Employees
- 2-10
- Funding
- $1.5M
- Customers
- 16,000+ organizations
- Status
- Acquired by Mintlify (Mar 2026), maintenance mode
Target users
Key Capabilities10
- AI gateway with access to 100+ LLM models via a single OpenAI-compatible API endpoint
- One-line proxy integration by swapping the baseURL in OpenAI/Anthropic SDKs
- Real-time request logging with full prompt/response capture, latency, and token metrics
- Session and agent tracing for multi-step pipelines, chatbots, and agentic workflows
- Cost tracking and optimization including response caching and automatic fallbacks
- Prompt management with versioning, templates, and production deployment without code changes
- Evaluation scoring (Eval Scores) with dataset creation and playground for prompt experimentation
- Custom properties, user-level analytics, and HQL (Helicone Query Language) for request filtering
- Configurable rate limits, alerts, and webhook notifications
- Self-hosting support via Docker Compose and enterprise-grade Helm chart; SOC-2 Type II and GDPR compliant
Key Use Cases8
- Monitoring LLM API costs, latency, and token usage in production AI applications
- Debugging and replaying LLM requests, prompt chains, and agent sessions
- Multi-provider AI gateway routing with automatic failover and load balancing
- Prompt version management and regression testing before production deployment
- Fine-tuning data collection via curated request/response datasets
- Tracking per-user LLM spend and usage patterns for SaaS product analytics
- Enforcing rate limits and security guardrails on LLM-powered APIs
- Self-hosted LLM observability for data-sensitive or compliance-constrained environments
Helicone customer outcomes
386 hours saved via cached responses
Used Helicone's response caching to eliminate redundant LLM calls, reducing engineering overhead from duplicate requests.
2 days saved on request analysis
Leveraged Helicone's request inspection tools to accelerate debugging of LLM outputs, reducing time spent manually combing through request logs.
30% reduction in agent runtime saved
Used Helicone to detect a critical bug in production agent workflows, enabling rapid remediation and protecting agent runtime efficiency.
Recent Trend
How AI describes Helicone3
...y backend | Teams already standardized on Datadog | | Braintrust | Good | Managed analytics backend | Eval-heavy teams | | Helicone | Historically good | Analytics-oriented backend | Lightweight gateway/proxy use cases | #### 1\.
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance?
...tm_source=chatgpt.com) | ✅ | ✅ | ✅ Via OpenTelemetry / observability integrations | Best open-source/self-hosted | | Helicone | ✅ | ✅ | ✅ Gateway logging + fallback visibility | Best if observabil...
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch?
...--- | | Bifrost | ⭐⭐⭐⭐⭐ | ✅ | ✅ | Simplest startup choice | | LiteLLM | ⭐⭐⭐⭐ | ✅ | ✅ | Most mature/flexible | | Helicone | ⭐⭐⭐⭐⭐ | ✅ Excellent | ✅ | Observability-first | | Portkey | ⭐⭐⭐ | ⚠️ Mostly hosted control plane | ✅ Gateway | Tea...
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one?
Most cited sources4
- D1
Provider Routing - Helicone OSS LLM Observability
docs.helicone.ai·Documentation
- D1
Error Handling & Fallback - Helicone OSS LLM Observability
docs.helicone.ai·Documentation
- D1
Docker - Helicone OSS LLM Observability
docs.helicone.ai·Documentation
110 Ways to Use Your LLM Logs to Build Better AI Products
helicone.ai·Blog Post
Alternatives in LLM Observability Evals & Gateways6
Helicone positions itself as the developer-friendly, open-source alternative to LangSmith and proprietary LLM observability tools, differentiating on a one-line proxy-based integration, a combined AI gateway and observability offering, and transparent usage-based pricing with a generous free tier.
- The platform self-describes as the most-used LLM observability platform among YC companies and explicitly competes on open-source flexibility, provider breadth (100+ models via a single API), and an intuitive UI versus more complex enterprise competitors such as Arize AI.
- Gateway features (caching, fallbacks, rate limiting, multi-provider routing) are bundled natively rather than treated as a separate product, which differentiates Helicone from pure-observability peers like Langfuse and Traceloop.
Reviews
Praised
- One-line integration simplicity
- Intuitive and clean UI dashboard
- Responsive, developer-community-driven team
- Real-time request visibility and debugging
- Effective cost and token usage tracking
- Open-source flexibility and self-hosting option
- Consistent feature rollout cadence
- Fast onboarding with no credit card required
Criticized
- Slow scan/upload performance (single G2 reviewer)
- Now in maintenance mode post-acquisition (no new major features)
- Advanced compliance and SSO gated to expensive tiers
- Very limited public review volume reduces signal confidence
Helicone has a small but consistently positive public review footprint. On G2 it holds a 4.5/5 score from 2 reviews. On Product Hunt it achieved #1 Product of the Day and draws praise for its intuitive UI, rapid integration, and responsive team. Developer sentiment highlights simplicity—the one-line setup and clean dashboard are frequently cited strengths. Criticism is sparse; one G2 reviewer noted slow upload scan performance. Community reviews emphasize the team's developer-community engagement and fast response to feature requests. No Gartner Peer Insights or Capterra scores are publicly verifiable.
Pricing
Free Hobby tier: 10,000 requests/month, 1 seat, 1 organization, 7-day data retention, 1 GB storage.
- Pro
$79/month (plus usage-based overages), unlimited seats, 1-month retention, HQL, alerts, reports, 1,000 logs/min ingestion.
- Team
$799/month (plus usage-based overages), 5 organizations, SOC-2 and HIPAA compliance, dedicated Slack channel, 3-month retention, 15,000 logs/min ingestion.
- Enterprise
custom pricing, on-prem deployment, SAML SSO, unlimited data retention, custom MSA. Usage-based pricing applies to requests and storage beyond included amounts. Discounts available for startups (<2 years old, <$5M funding: 50% off first year), non-profits, open-source projects ($100 credit), and students (free).
Limitations
- As of March 2026, Helicone entered maintenance mode following its acquisition by Mintlify, meaning no new major features are planned—only security updates, new model additions, and bug fixes.
- The free Hobby tier caps data retention at 7 days and ingestion at 10 logs/minute.
- Pro tier limits retention to 1 month.
- The G2 review base is very small (2 reviews), making structured user sentiment analysis unreliable.
- One G2 reviewer noted slow performance during file upload/scan operations.
- Advanced compliance features (HIPAA, SOC-2 Type II, SAML SSO) are gated to Team and Enterprise tiers.
- Native evaluation depth is lighter than dedicated eval platforms such as Braintrust or Galileo.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Google AI Mode | Perplexity | ChatGPT | Bing Copilot |
|---|---|---|---|---|---|
Capability1/5 cited (20%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was 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 | Neither your brand nor a competitor was cited |
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | A competitor was cited | A competitor was cited | A competitor was cited | 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? | 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 gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | Neither your brand nor a competitor was cited | 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 |
Developer Experience0/5 cited (0%) | |||||
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 | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was 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 | A competitor was cited |
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor 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 | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Integrations & Ecosystem1/5 cited (20%) | |||||
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 | Neither your brand nor 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 | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
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 | 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? | A competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was 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 | Neither your brand nor a competitor was cited |
Performance & Reliability0/5 cited (0%) | |||||
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
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 | 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 LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor 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 | A competitor was cited | A competitor was 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 | Neither your brand nor a competitor was cited |
Setup & First Run1/5 cited (20%) | |||||
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 | Neither your brand nor a competitor was 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? | 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 fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | Neither your brand nor a competitor was cited | 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 |
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 | A competitor was cited | A competitor was cited | A competitor was 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 | 32.0% | 21.8% | 7.2% | 0.0% | 44.0% | #3.3 | +0.51 |
| 2 | LangChain | 26.4% | 18.8% | 8.8% | 0.0% | 48.0% | #3.3 | +0.42 |
| 3 | Langfuse | 21.6% | 14.3% | 8.0% | 4.0% | 55.2% | #3.1 | +0.59 |
| 4 | Confident AI | 18.4% | 14.7% | 0.0% | 0.0% | 15.2% | #4.9 | +0.43 |
| 5 | Arize AI | 16.0% | 10.9% | 0.0% | 4.8% | 15.2% | #4.4 | +0.51 |
| 6 | Galileo | 12.0% | 6.8% | 0.0% | 12.0% | 10.4% | #2.8 | +0.40 |
| 7 | LiteLLM | 5.6% | 4.1% | 3.2% | 0.0% | 0.0% | #3.4 | +0.52 |
| 8 | Traceloop | 4.0% | 3.8% | 0.0% | 2.4% | 6.4% | #3.6 | +0.25 |
| 9 | Portkey | 4.0% | 3.4% | 0.8% | 0.0% | 21.6% | #5.3 | +0.45 |
| 10 | Helicone | 2.4% | 1.5% | 1.6% | 0.8% | 25.6% | #5.8 | +0.83 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.8% | — | — |
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