Olla vs LiteLLM - Choosing an LLM Proxy

What LiteLLM is and what it's great at#

LiteLLM is one of the most widely used open-source projects in the LLM space, with over 100,000 GitHub stars and a large, active community. It ships as both a Python SDK and a proxy server.

The SDK gives you a single litellm.completion() call that translates to the right API format for whichever provider you're targeting. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex, Cohere, Mistral, local Ollama instances - over 100 providers are supported. You write your code once against the OpenAI-compatible interface and LiteLLM handles the translation layer behind the scenes.

The proxy server extends this into something you can deploy as a shared service. On top of routing, it adds:

• Virtual keys - issue per-team or per-user API keys without exposing your real provider credentials
• Budget and spend tracking - set monthly spend limits per key, per team, or globally; get alerted before you hit them
• Rate limiting - enforce per-key or per-model request limits
• Observability integrations - hooks for Langfuse, Helicone, Prometheus, Datadog, and others
• Fallbacks - if GPT-4o returns an error, automatically retry on Claude; if that fails, fall back to a local model

The Python ecosystem is where LiteLLM lives. If your team is already in Python, the SDK drops straight into existing code with minimal ceremony. The community is large enough that most edge cases are documented, and the integration list keeps growing.

What Olla is and what it's great at#

Olla is our open-source LLM proxy and load balancer (Apache 2.0, written in Go). The focus is different: Olla is built for teams running local and self-hosted inference backends, where overhead, latency, and resilience matter more than breadth of cloud provider support.

A few things that shape what Olla is:

Single binary, minimal footprint. Olla runs in roughly 50MB of RAM and starts in milliseconds. There is no Python runtime, no dependency chain to manage, no pip environment. You drop one binary on a server, point it at your backends, and it runs.

Sub-millisecond endpoint selection. Routing decisions use lock-free atomic stats. The overhead of the proxy itself is not noticeable in your latency numbers.

Local-first backend support. Olla supports Ollama, LM Studio, vLLM, SGLang, llama.cpp, LiteLLM, LMDeploy, vLLM-MLX, Docker Model Runner, and LemonadeSDK. See the guide to inference servers for a rundown of the backend options themselves.

Priority routing and circuit breakers. You declare which backends are preferred (the GPU box first, the CPU server as fallback). If a backend starts returning errors, the circuit breaker opens and traffic is rerouted before users notice. When the backend recovers, it comes back into rotation automatically.

Sticky sessions with KV-cache affinity. This is the feature that matters most for multi-turn conversations at any reasonable context length. Olla hashes the message prefix to route subsequent turns in a conversation to the same backend. That keeps the KV cache warm - which means turns three, four, and five don't pay the full context-processing cost of starting cold. On a 20,000-token conversation, this is the difference between fast and noticeably slow.

Dual proxy engines. Olla ships a simple Sherpa engine for straightforward use cases and a high-performance Olla engine for production load. Both expose the same config surface.

OpenAI and Anthropic compatible. Unified model discovery across all backends via a single /v1/models endpoint. Anthropic-compatible passthrough for applications that speak that API directly.

Feature comparison#

They are not mutually exclusive#

How to choose#

Pick LiteLLM if:

• You are routing across multiple cloud providers (OpenAI, Anthropic, Azure, Bedrock, Vertex, and others)
• Your team works in Python and wants SDK-level integration
• You need built-in virtual keys, per-team budgets, and spend tracking without adding another tool
• You want broad observability integrations out of the box
• Community size and breadth of third-party integrations matter to your team

Pick Olla if:

• You are running local or self-hosted backends (Ollama, vLLM, llama.cpp, LM Studio, or similar)
• You want a single binary with minimal runtime overhead - nothing to install, nothing to manage
• KV-cache-aware sticky routing matters to you (multi-turn conversations, long context lengths)
• You want priority-based routing and circuit breakers with declarative config
• You are not in Python and do not want to be

Pick both if:

• You have a mix of cloud and local backends and want the best tool handling each side

The enterprise angle#

If you are running inference for multiple teams or external customers and need proper multi-tenant controls - per-team key issuance, audit logs, rate limiting across tenants, detailed cost attribution - that is the gateway problem rather than the proxy problem. Alloy is our gateway tier that sits above the proxy layer and adds those controls.

Once you are past a handful of backends and into a fleet of inference nodes with scheduling and fleet health concerns, that is a different problem again - orchestration rather than routing. FoundryOS covers that end of the spectrum (currently in early access). Neither is necessary at the start; they are the rungs above the proxy on the same ladder.

Further reading#

• Olla on GitHub - open-source LLM proxy and load balancer (Apache 2.0)
• Olla documentation - configuration, backends, sticky sessions, routing
• LiteLLM on GitHub - unified LLM provider interface (MIT)
• LiteLLM documentation - SDK and proxy server guides
• What is an LLM proxy? - a primer on the concept before choosing an implementation
• Deploying LLMs on your own infrastructure - the wider guide to running self-hosted inference
• LLM inference servers compared - Ollama, vLLM, llama.cpp, LM Studio, and others