Olla
Free Apache-2.0 LLM proxy for unified self-hosted inference routing
If you self-host multiple LLM backends and want a free, fast proxy with smart routing, Olla is a top pick. It's 14.8x lighter on idle memory than LiteLLM and adds sub-millisecond overhead. But you handle Docker and YAML yourself, and there's no support SLA. Teams needing managed services should look elsewhere.
Verified 3d ago · liveness 72/100 · cite: rightaichoice.com/tools/olla
- Development teams self-hosting multiple LLM backends who need a single unified API
- Small businesses wanting a free, lightweight inference gateway without enterprise complexity
- Platform engineers building internal AI infrastructure with automatic failover and observability
- Researchers experimenting with local models across different engines
- Non-technical users who need a GUI or a fully managed service
- Enterprises requiring dedicated support SLAs or enterprise-grade features
- Users who primarily use cloud LLM providers like OpenAI or Anthropic API
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Skip Olla if you need a managed proxy service with a GUI and support, or if you only use cloud LLM APIs and don't self-host backends.
Olla is free (Apache-2.0) with no usage limits, making it unbeatable for teams self-hosting even at scale—unlike LiteLLM's paid tiers beyond 1M tokens/day. You pay in ops time (Docker, YAML, updates). For larger enterprises, FoundryOS (Q2 2026) will offer managed features.
In short
Olla — Free Apache-2.0 LLM proxy for unified self-hosted inference routing. Best for Development teams self-hosting multiple LLM backends who need a single unified API, Small businesses wanting a free, lightweight inference gateway without enterprise complexity, Platform engineers building internal AI infrastructure with automatic failover and observability. Free to use.
What's new in Olla
Checked 8 days agoAcross the latest 5 updates: 3 feature updates and 2 news mentions.
Olla v0.0.29 patch — Adds embedded read-only admin dashboard, refreshed model catalogue, native Prometheus metrics, circuit-breaker failover, and credential validation at startup.
v0.0.29 introduces an embedded read-only admin dashboard, native Prometheus metrics, circuit-breaker failover, and credential validation at startup, improving observability and reliability.
Olla v0.0.28 patch — Adds native oMLX support, Anthropic passthrough, per-endpoint auth for local backends, and opt-in CORS.
v0.0.28 adds native oMLX support for Apple Silicon, Anthropic passthrough with format translation, per-endpoint authentication, and opt-in CORS.
Olla vs LiteLLM - Choosing an LLM Proxy
Blog post comparing Olla (Go-based, local-first) and LiteLLM (Python provider hub), outlining trade-offs for proxy selection.
LLM Inference Servers Compared - vLLM, SGLang, llama.cpp and Ollama
Side-by-side comparison of four inference backends covering hardware needs, strengths, and usage contexts.
Olla v0.0.27 patch — Adds native LMDeploy backend support, sticky session fixes for KV cache alignment, and aimock test harness.
v0.0.27 adds native LMDeploy backend support, fixes sticky sessions for KV cache alignment, and includes an aimock test harness.
What people actually say about Olla — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
44 mentions across 4 sources (Hacker News, Bluesky, GitHub, Lemmy) · researched Jul 6, 2026.
- +Unified OpenAI-compatible API across nine inference backends.
- +Automatic model discovery and aggregation reduces manual configuration.
- +Supports priority, round-robin, least-connections, and weighted routing.
- +Automatic failover with circuit breakers and exponential backoff.
- +Tiny memory footprint (~20 MB idle) suitable for homelab hardware.
- −Almost no community feedback or real-world usage reports exist.
- −Name is easily confused with the unrelated Ollama project.
- −No managed cloud tier means users must handle all ops themselves.
- −Lacks enterprise SLAs and formal support channels.
- −Documentation and onboarding unclear due to sparse community additions.
- • Infrastructure costs for self-hosting (compute, storage, bandwidth)
- • Operational overhead for maintenance and updates
- • No official support; community help is uncertain
Viability Score
How well maintained and how widely used is Olla? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Unified OpenAI-compatible API across 9+ backends
- Load balancing: priority, round-robin, least-connections, weighted
- Automatic failover with circuit breakers and exponential backoff
- Health monitoring with configurable thresholds
- Rate limiting and request validation
- Anthropic passthrough with message format translation
- Per-endpoint authentication
- Sticky sessions for KV-cache alignment
- Model alias validation and aggregation
- Byte-preserving JSON rewrite
- Dual proxy engine: Sherpa and Olla
- Connection pooling and object pooling
- Structured logging and real-time metrics
- Embedded read-only admin dashboard
- Native Prometheus metrics
About Olla
Olla is a lightweight, open-source LLM proxy and load balancer for teams that self-host inference backends. It unifies Ollama, LM Studio, vLLM, SGLang, llama.cpp, oMLX, LMDeploy, and other OpenAI-compatible endpoints behind a single API, so you can stop juggling multiple URLs and model names. Built for small businesses, development teams, and growing startups, it handles intelligent load balancing, automatic failover, and health monitoring in a single Go binary that idles at around 20 MB and adds under 1 ms of proxy overhead. Key capabilities include a unified model registry with automatic aggregation across 8+ backends, multiple routing strategies (priority, round-robin, least-connections, weighted), and circuit breakers with exponential backoff that route around unhealthy nodes. Sticky sessions keep KV-cache alignment for faster cold turns. Recent releases add an embedded read-only admin dashboard, native Prometheus metrics, and Anthropic passthrough with format translation. Olla is free under Apache 2.0 and designed for local-first infrastructure. You deploy it yourself with Docker or Podman and configure it via YAML. It's not a managed service, and there's no GUI beyond the admin dashboard. If you need enterprise support or a fully managed cloud proxy, LiteLLM's paid tiers or FoundryOS (expected Q2 2026) are alternatives.
Behind the Verdict
Olla occupies a specific niche: local-first AI teams that want a unified API without paying for a managed proxy. We'd reach for it when you have more than two inference backends and don't want to write your own routing layer. The performance numbers are impressive - under 1 ms overhead, ~20 MB idle memory, and 10,200 req/s throughput - and they come from a real benchmark, not a spec sheet. The dual proxy engine (Sherpa for lightweight, full Olla) gives you flexibility, though most will just use Olla. Circuit breakers with exponential backoff mean failed backends don't take down your whole stack, and sticky sessions help with KV-cache alignment - a real win for multi-turn conversations. Where it bites: setup requires Docker and YAML. If you're not comfortable with terminal commands, this isn't for you. There's no GUI beyond the admin dashboard, and no managed hosting. The project is young (200+ GitHub stars, v0.0.29), so you're betting on community momentum. Compared to LiteLLM, which is Python-based and supports hundreds of providers, Olla is Go-based and lighter. LiteLLM wins on provider breadth and mature cloud options; Olla wins on speed and simplicity. If you only use cloud APIs like OpenAI, you don't need a proxy at all. For enterprise SLAs, wait for FoundryOS or use LiteLLM paid tiers. In practice, we'd use Olla for internal tools, dev environments, or small production setups where you control the infrastructure. It's free forever under Apache 2.0, so there's no vendor lock-in. But don't expect enterprise support - and if your team grows, plan your migration path.
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Real-world workflow fit
Concrete scenarios for the personas Olla actually fits — and what changes day-one when you adopt it.
Setting up a unified API for multiple inference backends (Ollama, vLLM, SGLang) in Docker
Outcome: Deploys Olla in minutes, configures backends via YAML, and gets a single OpenAI-compatible endpoint with automatic failover and monitoring.
Benchmarking latency difference between llama.cpp and vLLM on same hardware
Outcome: Uses Olla's metrics to compare response times, adjusts routing weights to favour faster backend, and shares findings.
Integrating Anthropic-format API calls into internal tooling while using local models
Outcome: Enables Anthropic passthrough, Olla translates messages to local model format, internal tools work unchanged.
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 a proxy and load balancer, not an inference engine—you need separate backends like Ollama or vLLM.
- Deployment requires Docker and YAML configuration; there is no GUI.
- The project is under active development, so you may encounter breaking changes in early versions.
- Community support is primarily through GitHub issues.
as of 2026-08-25
Verification history
We have re-verified Olla 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published Olla 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
Ideal for
Development teams and small businesses self-hosting LLM backends who need a free, lightweight proxy without enterprise budgets.
What this tier adds
Free and open source (Apache-2.0) with full feature access—no paid tiers, no usage limits.
Where the pricing makes sense
The company stage and team size where Olla's pricing actually pencils out — and where peers do it cheaper.
Olla is free (Apache-2.0) with no usage limits, making it unbeatable for teams self-hosting even at scale—unlike LiteLLM's paid tiers beyond 1M tokens/day. You pay in ops time (Docker, YAML, updates). For larger enterprises, FoundryOS (Q2 2026) will offer managed features.
Setup time & first value
How long it actually takes to get something useful out of Olla — broken out by persona, not the marketing-page minute.
For a developer familiar with Docker, first value in under 10 minutes: pull image, write a minimal YAML, start container. Non-technical users may need an hour to learn Docker and networking basics.
Switching to or from Olla
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LiteLLM: point your client to Olla's endpoint; YAML config similar, so most routing rules translate easily.
- ↗To FoundryOS: if you outgrow Olla, FoundryOS (Q2 2026) will offer enterprise features; migration likely via standard OpenAI-compatible endpoints.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Featured Head-to-Head Comparisons
Olla vs Spider Cloud
Spider Cloud and Olla serve completely different needs: Spider Cloud is a high-performance web scraping API tailored for RAG pipelines and AI agents, with powerful AI extraction and Browser AI commands. Olla is an open-source LLM proxy and load balancer for managing multiple inference backends. Choose based on whether you need web data extraction (Spider Cloud) or unified LLM routing (Olla).
Olla vs Temporal Ai
Temporal AI and Olla serve fundamentally different needs: Temporal is for building reliable, long-running workflows and AI agents that survive failures, while Olla is a lightweight LLM proxy for routing requests across multiple self-hosted backends. Choose Temporal if you want mission-critical orchestration with durability and visibility; choose Olla if you need a free, open-source gateway to unify local LLMs. They are complementary, not directly competitive.
Olla vs Voyage Ai
Voyage AI is for enterprises needing high-accuracy, domain-specific embeddings for RAG, while Olla is a free open-source proxy for teams self-hosting multiple LLM backends. Choose Voyage if you need specialized models and compliance; choose Olla if you need a lightweight, cost-effective gateway.
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