Snapshot
Continue to About ↓- What it does
- Helicone is an AI gateway and LLM observability platform that sits between your application and model providers, logging, costing, and tracing requests after a one-line base-URL change.
More detail
- It also routes requests across multiple providers with a single key and offers automatic fallbacks.
- Official pages currently state the company is 'no longer part of Mintlify' with further details pending.
- Best for
- Engineering teams running production LLM features who want request-level tracing, cost tracking, and a unified gateway across multiple model providers.
More detail
- Also suits teams that prefer an open-source, self-hostable option.
- Pricing
- Hobby is free with 10,000 requests per month, 1 GB storage, 1 seat, 1 organization, and 7-day retention.
More detail
- Pro is $79 per month with unlimited seats, alerts, HQL, 1-month retention, and 1,000 logs/min, plus usage-based charges beyond included amounts.
- Team is $799 per month with 5 organizations, SOC-2
- Main limitation
- Non-technical teams needing a no-code setup, and buyers who require a vendor with a settled, clearly disclosed corporate status.
More detail
- Teams that need deep evaluation tooling may find it limited compared with dedicated evaluation platforms.
- Record
- Living and monitored
- Reviewed
- Evidence
- Public web research
- Machine access
- Structured profile ↗
About
- Helicone is an AI gateway and LLM observability platform that sits between your application and model providers, logging, costing, and tracing requests after a one-line base-URL change.
- It also routes requests across multiple providers with a single key and offers automatic fallbacks.
- Official pages currently state the company is 'no longer part of Mintlify' with further details pending.
What it helps with
- Provider outages and rate limits can break AI features without automatic fallback or routing across providers.
- Non-deterministic LLM outputs are hard to debug without request-level traces, sessions, and user context.
- LLM spending is hard to predict or attribute without per-request cost tracking, caching, and usage analytics.
- Managing separate API keys and limits across many model providers creates operational overhead.
- Prompt iteration tied to code deploys slows experimentation and version control.
- Multi-step agent workflows are difficult to inspect end to end without session and trace tooling.
Capabilities
A practical view of what is available and where limitations apply.
| Offering | Available | Detail |
|---|---|---|
| One-line integration | Yes | Documented as a base-URL swap that begins logging requests without broader code changes. |
| AI gateway with multiple providers | Yes | A single key routes to a broad set of models across documented integrations such as OpenAI, Anthropic, Azure, and OpenRouter. |
| Automatic fallbacks and routing | Yes | Official documentation describes automatic fallbacks across providers. |
| Request, session, and user tracing | Yes | Official site lists requests, segments, sessions, and users in the dashboard. |
| HQL query language | Limited | Available on Pro and above; not on the free Hobby tier. |
| Prompt management and playground | Yes | Official pricing and repository reference prompts, scores, datasets, and a playground. |
| Evaluation tooling | Limited | Helicone's own comparison table lists evaluation as limited versus dedicated evaluation platforms. |
| Self-hosting | Yes | Open-source Apache 2.0 project with self-hosting supported. |
| SAML SSO | Limited | Listed only on the Enterprise plan, not on Pro or Team. |
| SOC-2 and HIPAA compliance | Limited | Listed from the Team plan upward; not included on Hobby or Pro. |
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Evidence
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- Verification
- Scribble researched these facts from the linked public sources.
- The owner has not manually confirmed every field.
- Last reviewed
- Primary source
- helicone.ai
Technical profile details
Record ID
df50dd3c-c2bc-4fa3-bd9e-b25a3a6ae98dCanonical URLhttps://profiles.scribble.network/listing/helicone-ai-df50dd3c