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Pick 2–5 published tools. Values come from our evidence-checked profiles — an em-dash means unknown, never “no”. Ranking rows reflect the current published methodology for the comparison context.
LiteLLM vs Helicone
Context: LLM Gateways & Model Routing Platforms · 25 rows
| Attribute | LiteLLMBerriAI | HeliconeHelicone |
|---|---|---|
| Identity · Taxonomy | ||
| Documentation | https://docs.litellm.ai/docs/ | — |
| Tags | llm-gateway, proxy, open-source, routing | llm-observability, llm-gateway, open-source, monitoring |
| Platform · Deployment | ||
| Delivery model | self-hosted | SaaS, self-hosted |
| Pricing · Licensing | ||
| Free plan | true | true |
| Free trial | true | true |
| License model | open-source | open-source |
| Pricing model | free, custom | free, subscription, usage |
| Source repository | https://github.com/BerriAI/litellm | https://github.com/Helicone/helicone |
| SPDX license IDs | MIT | Apache-2.0 |
| Starting price | — | 79 |
| Trial length | 30 | 7 |
| Features · Product · Fit | ||
| Core capabilities | Unified OpenAI-compatible API to 140+ LLM providers, Virtual keys with per-key, team, and org budgets, Auto routing, load balancing, and caching, Guardrails including PII masking and prompt injection protection, Usage analytics, spend tracking, and chargeback, Unified access for LLMs, agents, and MCP servers | LLM request logging and tracing, AI gateway with caching, rate limits, and fallbacks, Prompt management and datasets, HQL querying and user analytics, Alerts and reports, Evaluation and experiments |
| Api · Integrations · Ecosystem | ||
| API available | true | true |
| CLI | true | — |
| Enterprise SSO | OIDC | SAML |
| Security · Privacy · Compliance | ||
| Security certifications | — | SOC 2, HIPAA |
| Support · Docs · Services | ||
| Support channels | community, enterprise support | community, email |
| Audience · Use · Case | ||
| Best for | Platform and engineering teams that want to self-host one OpenAI-compatible gateway in front of many LLM providers, with budgets, routing, and guardrails. | AI engineering teams that want open-source LLM observability with an optional hosted gateway for logging, caching, and rate limiting. |
| Company-size fit | mid-market, enterprise | individual, SMB, mid-market, enterprise |
| Primary use cases | Central LLM gateway for AI applications, Spend tracking and chargeback across teams, Model routing and load balancing | LLM observability and debugging, Cost and latency monitoring, Gateway routing for AI applications |
| Skill level | advanced | intermediate |
| Lifecycle · Versioning | ||
| Changelog/release notes | https://docs.litellm.ai/release_notes | — |
| Current version | v1.100.0 | — |
| Maintenance status | active | active |
| Vendor · Maintainer | ||
| Vendor type | commercial | commercial |
“—” means unknown (we never treat missing evidence as a negative). Cells reflect each value's current evidence state; superseded or stale values are excluded. Ranking rows are editorial assessments under the cited methodology version — see the LLM Gateways & Model Routing Platforms category for definitions.