Olla
Open-source LLM proxy & load balancer for self-hosted AI inference.
For teams self-hosting multiple LLM backends, Olla delivers a lightweight, fast, and reliable proxy with sub-millisecond overhead and failover. It's free and open-source, but lacks enterprise support and a GUI.
- Development teams self-hosting multiple LLM backends needing a unified API
- Small businesses seeking a cost-effective, lightweight inference gateway
- Platform engineers building internal AI infrastructure with failover
- Researchers experimenting with various local models and engines
- Non-technical users needing a GUI or fully managed service
- Enterprises requiring dedicated support SLAs and enterprise features
- Users needing integrations with cloud LLM providers (OpenAI, Anthropic API)
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
In short
Olla — Open-source LLM proxy & load balancer for self-hosted AI inference. Best for Development teams self-hosting multiple LLM backends needing a unified API, Small businesses seeking a cost-effective, lightweight inference gateway, Platform engineers building internal AI infrastructure with failover. Free to use.
What's new in Olla
Checked 11 days agoAcross the latest 10 updates: 4 feature updates and 6 news mentions.
Under the Hood of MLX: How a Model Runs on a Mac
Details MLX's lazy evaluation, graph fusion with mx.compile, unified memory, and quantized matmul kernels on Apple Silicon.
The Bandwidth Wall: A Roofline for Local LLMs
Explains memory bandwidth as the binding constraint for token generation on Macs, using the roofline model and M-series data.
How MLX Runs LLMs on Apple Silicon
Overview of Apple's MLX framework, unified memory, and comparison to llama.cpp and Ollama.
Run MLX Behind Olla on Your Mac
Guide to using Olla as a unified endpoint for oMLX, GGUF, and Anthropic passthrough.
Olla v0.0.28 released with native oMLX support and Anthropic passthrough
Adds native oMLX for fast multi-model inference on Apple Silicon, per-endpoint authentication, and opt-in CORS.
What we found when we benchmarked Olla
Presents Olla routing, latency, and failover benchmarks against LiteLLM on Windows.
Olla vs LiteLLM - Choosing an LLM Proxy
Compares Olla (Go-based, local-first) with LiteLLM (Python provider hub) for LLM proxy use cases.
What is an LLM Proxy?
Explains the role of an LLM proxy in front of inference backends and why teams use one.
Olla v0.0.27 adds native LMDeploy support
Integrates LMDeploy backend and fixes sticky sessions.
Olla v0.0.26 fixes SSL Host header handling
Bugfix release addressing SSL connection issues due to Host header mis-handling.
Viability Score
How likely is Olla to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Unified OpenAI-compatible API across multiple LLM backends
- Model discovery and aggregation from 9+ inference engines
- Priority-based, round-robin, least-connections, weighted load balancing
- Automatic failover and recovery with circuit breakers and exponential backoff
- Health monitoring with configurable thresholds and real-time metrics
- Rate limiting and request validation for security
- Anthropic passthrough with message format translation
- Per-endpoint authentication for local backends
- Sticky sessions for consistent KV-cache routing
- Model alias validation and byte-preserving JSON rewrite
- Dual proxy engine: Sherpa (lightweight) and Olla (full-featured)
- Connection pooling and aggressive object pooling for low latency
- Comprehensive audit logging and structured logging
- Native support for oMLX and LMDeploy backends
- Deploy via Docker with single command
About Olla
Olla is a high-performance, open-source LLM proxy and load balancer that unifies multiple inference backends—including Ollama, LM Studio, vLLM, SGLang, llama.cpp, LMDeploy, Docker Model Runner, and oMLX—behind a single OpenAI-compatible API. Built for small businesses, development teams, and growing organizations, it automatically discovers models across backends and routes requests with intelligent load balancing, automatic failover, and sub-millisecond overhead. Key features include priority-based, round-robin, least-connections, and weighted routing; circuit breakers with exponential backoff; per-endpoint connection pooling; rate limiting; request validation; and comprehensive audit logging. Olla also supports Anthropic passthrough with message format translation, per-endpoint authentication, sticky sessions, and model alias validation. Deployed via Docker in minutes, it has a tiny memory footprint (~20 MB idle) and has achieved over 20K container pulls. As an Apache 2.0 open-source project, Olla offers a free, self-hosted alternative to heavier proxies like LiteLLM, though it lacks a fully managed cloud offering and enterprise SLAs.
Behind the Verdict
Olla is a straightforward, no-frills open-source proxy for teams that want to unify multiple local LLM backends without paying for a managed service. We'd reach for this when we have Ollama, vLLM, and llama.cpp instances scattered across dev boxes and just need a single endpoint with failover and load balancing. The performance numbers are solid: sub-millisecond overhead, ~20 MB idle memory, and the ability to handle 10,000+ req/s through the proxy alone. The recent additions of oMLX, LMDeploy, and sticky sessions make it more versatile for Apple Silicon users and long-context workflows. Where it bites: there's no GUI, so configuration is purely via YAML and environment variables. That's fine for DevOps teams but a dealbreaker for less technical users. Compared to LiteLLM, Olla is dramatically lighter (14.8x less memory idle) and faster, but LiteLLM has a broader ecosystem of cloud provider integrations and a managed cloud option. If you're all-in on self-hosted backends and don't need cloud provider proxies, Olla is the leaner choice. In practice, we've seen it work reliably in CI/CD pipelines and small production setups. The lack of enterprise support and SLAs means you're on your own for uptime, but the circuit breakers and health checks handle common failure modes well.
Researching Olla? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Use Cases
- Unify Ollama, vLLM, and llama.cpp behind a single OpenAI-compatible endpoint for development teams
- Automatically failover between local and remote inference backends to maintain high availability
- Route Anthropic-format requests to local models using built-in message format translation
- Monitor and rate-limit access to self-hosted LLM infrastructure for internal tools
- Benchmark and compare latency across different backends using Olla's health metrics
- Deploy a lightweight proxy in a Docker container to manage multiple models with minimal overhead
Limitations
- Olla is focused on proxying and load balancing; it does not provide its own inference engine.
- Configuration is YAML-based and may require intermediate knowledge of networking and container deployment.
- The project is under active development, so some features (e.g., sticky sessions) are still maturing.
- Community support is limited to GitHub issues.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Integrations
Resources & Guides
Official links
Featured Head-to-Head Comparisons
Popular in Developer Infrastructure
Temporal AI
Durable execution platform for building reliable AI agents and workflows.
Spider Cloud
Fast web crawling, scraping & search API for AI agents
Frequently Asked Questions
Categories
Best-of guides
Topics
Used Olla? Help shape our editorial sentiment research.