LiteLLM — Open-source AI gateway to access 100+ LLMs with OpenAI format. Best for Platform teams managing LLM access for multiple developers across providers, Companies that want to avoid vendor lock-in by using multiple LLM providers, Organizations needing granular cost tracking and budget enforcement per team/project. Free to start; paid plans from $5/mo.
Platform teams managing LLM access for multiple developers across providersCompanies that want to avoid vendor lock-in by using multiple LLM providersOrganizations needing granular cost tracking and budget enforcement per team/projectTeams migrating from OpenAI to other providers without rewriting codeEnterprises requiring self-hosted or on-prem LLM gateway with audit logs
Not ideal for
Small teams using only one LLM provider with no need for multi-provider abstractionProjects that require proprietary non-OpenAI-compatible APIsTeams that prefer a fully managed SaaS without self-hosting optionsOrganizations that need instant real-time streaming without any latency overhead
A must-try for platform teams needing a single gateway to manage multiple LLM providers. It's production-proven at scale (1B+ requests) and eliminates the headache of provider-specific SDKs. Open-source core is free, making it low-risk to adopt.
Last verified: June 2026
Behind the Verdict
LiteLLM is an excellent choice for platform teams that need to give developers self-serve access to multiple LLM providers without managing provider-specific authentication, rate limits, or cost tracking. Its OpenAI-compatible API means minimal code changes for existing OpenAI users. The open-source version is feature-rich, supporting 100+ providers, spend tracking, and basic rate limiting. Where it really shines is in production environments: Netflix and Lemonade use it to provide day-zero access to new models. However, if you only need one provider (e.g., just OpenAI), LiteLLM might be overkill. The main alternative is Portkey, but LiteLLM's open-source nature and Y Combinator backing give it an edge for self-hosters. One caveat: the enterprise tier's pricing isn't public, so budget-conscious teams need to contact sales. For most teams, the open-source version will suffice.
Skip LiteLLM if Skip LiteLLM if you only need a single LLM provider and don't require centralized spend tracking, fallbacks, or multi-provider routing.
How likely is LiteLLM to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
80
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About LiteLLM
LiteLLM is an open-source AI gateway that simplifies model access, spend tracking, and fallbacks across 100+ large language models. Built for platform teams, it provides a unified OpenAI-compatible interface to manage LLM access for developers across providers like OpenAI, Azure, Google Gemini, AWS Bedrock, and Anthropic. Key features include automatic spend tracking with attribution to keys/users/teams, budget and rate limit controls, tag-based cost tracking, and load balancing with RPM/TPM limits. It also supports logging to s3/GCS, prompt formatting for Hugging Face models, and integrations with observability tools like Langfuse, Arize Phoenix, and OpenTelemetry. LiteLLM is available as a free open-source version or as a managed cloud/self-hosted enterprise offering with additional features like JWT auth, SSO, and audit logs. Compared to building custom multi-provider adapters, LiteLLM saves months of development time, as evidenced by production use at Netflix and Lemonade.
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Key Features
100+ LLM provider integrations
OpenAI-compatible API format
Automatic spend tracking per key/user/team
Budget and rate limit controls
Load balancing with RPM/TPM limits
LLM fallbacks for resilience
Tag-based cost attribution
Log spend to s3, GCS, etc.
Prompt formatting for Hugging Face models
Virtual keys management
LLM guardrails
Observability logging (Langfuse, OpenTelemetry)
Real-world workflow fit
Concrete scenarios for the personas LiteLLM actually fits — and what changes day-one when you adopt it.
Platform engineer at a mid-size startup
You need to give 5 development teams access to OpenAI, Azure, and Anthropic while tracking costs per team.
Outcome: Deploy LiteLLM proxy with virtual keys for each team, set monthly budgets, and get per-team spend reports in S3 in under an hour.
ML engineer at an enterprise
Your app relies on GPT-4 and you want automatic fallback to Claude if OpenAI goes down.
Outcome: Configure fallback models in the proxy YAML—during an outage, requests automatically route to Anthropic with zero code changes.
Infrastructure lead at a large organization
You need SSO, audit logs, and guardrails for compliance while giving 100+ developers model access.
Outcome: Use Enterprise tier with JWT/SSO integration, enable audit logging, and set guardrails per team—all managed from the admin UI.
Use Cases
Centralize LLM access with virtual API keys for every team in the organization.
Add automatic fallback from OpenAI to Azure OpenAI during outages with zero code changes.
The proxy adds a network hop, increasing latency for every request. Configuration YAML can grow complex for large orgs with many routing rules. New provider-specific features (e.g., beta response formats) may lag behind direct API usage. A recent SQL injection vulnerability (CVE-2024-XXXX) required urgent patching — teams must stay current. Prompt caching misconfiguration can lead to unexpected cost spikes (e.g., a reported $38K AWS Bedrock bill). Enterprise pricing is not public and requires a sales call.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published LiteLLM tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo (MIT license)
Ideal for
Platform teams and startups that can self-host and need full control over 100+ model integrations, cost tracking, and fallbacks without recurring fees.
What this tier adds
Starting tier: free, MIT-licensed, self-hosted SDK and proxy with virtual keys, budgets, and observability logging but without enterprise auth, audit logs, or support.
Enterprise
From $5K/year
Ideal for
Large organizations and regulated industries that require SSO, JWT authentication, audit logs, custom SLAs, and priority support for multi-team LLM access.
What this tier adds
Adds cloud or self-hosted deployment with JWT/SSO auth, audit logs, custom SLAs, guardrails, admin UI, and priority support over the Open Source tier.
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Enterprise pricing starts at $5K/year and requires a sales call—no self-serve checkout.
•Overage costs if self-hosted infrastructure (compute, storage) exceeds plan expectations.
•Prompt caching misconfiguration can unexpectedly spike costs (e.g., $38K on AWS Bedrock).
Where the pricing makes sense
The company stage and team size where LiteLLM's pricing actually pencils out — and where peers do it cheaper.
The open-source tier ($0/mo) is ideal for startups and small teams that can self-host. The Enterprise tier (from $5K/year) targets larger organizations needing SSO, audit logs, and support. Compared to managed alternatives like Portkey ($49+/mo) or Helicone ($20+/mo), LiteLLM's open-source version offers more control but requires DevOps effort. For single-provider teams, direct API usage may be cheaper.
Setup time & first value
How long it actually takes to get something useful out of LiteLLM — broken out by persona, not the marketing-page minute.
Platform engineers can get the proxy running in under 15 minutes with the Docker quickstart (docker run litellm). Customizing models, teams, and budgets takes 1-2 hours. Enterprise setup with SSO, audit logs, and guardrails may take a few days to configure and test.
Switching to or from LiteLLM
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From direct API wrappers: Point your existing OpenAI client to the proxy endpoint—no code changes needed.
→From single-provider setups: Add new provider models in config.yaml and use pass-through endpoints for zero-migration.
Migrating out
↗To a managed gateway (Portkey, Helicone): Export spend logs and reconfigure routing rules on the new platform.
↗To direct API usage: Update client URLs to each provider's base URL and remove proxy headers.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•Added guardrails and admin UI in recent releases (2024-2025).
•Prompt caching misconfiguration vulnerability disclosed—advised to validate settings.
•SQL injection vulnerability (CVE-2024-XXXX) patched—users urged to upgrade.
