Olla
Olla is an open-source, Apache-2.0 Go LLM proxy that load-balances Ollama, vLLM, SGLang and MLX behind one OpenAI-compatible endpoint on
Olla earns a look when you already run two or more self-hosted engines and want routing that gets out of the way. The benchmark numbers are unusually specific for this category — 0.30 ms p50 overhead, zero drops across 20,223 failover requests — and v0.0.29's dashboard and Prometheus endpoint close the visibility gap that used to mean bolting on tooling. If your traffic is mostly hosted providers instead, this is the wrong tool.
Verified 5h ago · liveness 72/100 · cite: rightaichoice.com/tools/olla
- Platform engineers self-hosting two or more LLM engines who need one endpoint and automatic failover
- Startups running local inference without paying per-token managed-gateway fees
- Apple Silicon teams unifying MLX and GGUF model names behind one endpoint
- Operators who want Prometheus metrics and structured audit logs on their own inference nodes
- A single backend and one model — you add a hop and a YAML file for no routing benefit
- Teams whose inference traffic mostly goes to OpenAI, Bedrock or other hosted providers
- Non-technical users expecting a GUI to add endpoints and change routing live
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Skip Olla if all your inference traffic goes to hosted providers like OpenAI or Bedrock, or if you run a single engine on one box - in both cases a provider-hub proxy or a direct connection is the simpler shape.
Olla is Apache-2.0 open source, so the software cost is zero and you can run unlimited endpoints, models and requests on your own hardware. What you pay for instead is ops time: you run the container, write the YAML and carry the pager yourself, and you supply the inference boxes. Compare that against paid managed gateways that charge per token or per seat - Olla wins on cost wherever your compute is already local, and loses the moment you would rather somebody else ran it.
In short
Olla — Olla is an open-source, Apache-2.0 Go LLM proxy that load-balances Ollama, vLLM, SGLang and MLX behind one OpenAI-compatible endpoint on. Best for Platform engineers self-hosting two or more LLM engines who need one endpoint and automatic failover, Startups running local inference without paying per-token managed-gateway fees, Apple Silicon teams unifying MLX and GGUF model names behind one endpoint. Free to use.
What's new in Olla
Checked 5 days agoAcross the latest 5 updates: 3 feature updates, 1 changelog entry and 1 news mention.
Olla v0.0.29 adds embedded admin dashboard and Prometheus metrics
Adds a loopback-only read-only admin dashboard at /internal/ui/ with fleet status and model inventory, native Prometheus metrics at /internal/metrics, circuit-breaker failover, a refreshed 2026 model catalogue and a startup failure for credentials embedded in endpoint URLs.
Olla v0.0.28 adds native oMLX support on Apple Silicon
Brings native oMLX multi-model inference on Apple Silicon with Anthropic passthrough, per-endpoint authentication for local backends, opt-in CORS for browser clients and Docker Model Runner support.
What we found when we benchmarked Olla
TensorFoundry measured Olla's routing, latency, memory and failover on a Windows dev box, reporting 0.30 ms p50 proxy overhead, roughly 20 MB idle memory and zero dropped requests across 20,223 failover requests, including a head-to-head against LiteLLM.
Olla v0.0.27 adds native LMDeploy backend
Adds native LMDeploy backend support with a model converter wired into the factory and routes, plus sticky session fixes that improve KV cache alignment across multi-endpoint deployments.
Olla v0.0.26 fixes SSL failures from Host header handling
A bugfix release resolving SSL connection failures caused by incorrect Host header handling and making Docker image builds portable across arm and amd without goreleaser.
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.
Average across the 4 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Unified OpenAI-compatible API across self-hosted backends on a single port (40114)
- Automatic model discovery and aggregation into one unified model registry
- Model aliasing with byte-preserving JSON rewrite
- Priority-based, round-robin, least-connections and weighted load balancing
- Circuit-breaker health monitoring with exponential backoff and gradual recovery
- Sticky session routing that keeps follow-up turns on the node holding the KV cache
- Anthropic passthrough with message-format translation
- Native oMLX multi-model inference support on Apple Silicon
- Per-endpoint authentication for local backends that ship without auth
- Embedded loopback-only read-only admin dashboard at /internal/ui/
- Native Prometheus metrics endpoint at /internal/metrics
- Built-in rate limiting and request validation
- Structured audit logging with millisecond-precision request traces
- Dual proxy engines: lightweight Sherpa and full-featured Olla
- Format translation across backend API dialects (v0.0.29, Aug 2026)
About Olla
Olla is a self-hosted LLM proxy and load balancer for teams running their own inference hardware. Point it at Ollama, LM Studio, vLLM, vLLM-MLX, SGLang, llama.cpp, LMDeploy, oMLX, LiteLLM, Lemonade, Docker Model Runner or any OpenAI-compatible endpoint, and it aggregates them into one address at :40114 with a unified model registry, aliases and per-model availability state. The vendor lists 9+ supported backends plus a generic openai-compatible catch-all. Routing is the substance, not the marketing. Priority-based, round-robin, least-connections and weighted strategies cover uniform and tiered setups; circuit breakers with exponential backoff catch dead nodes and reintroduce healed ones gradually. Sticky sessions keep follow-up turns pinned to the node holding the KV cache, which TensorFoundry measured at 7.6x faster cold turns versus round-robin at 32k context on turn two. Olla's own benchmarks from a June 2026 Windows dev box report ~0.30 ms p50 proxy overhead, roughly 20 MB idle memory (42-57 MB under load), a 14.4 MB static Go binary, a 10,200 req/s routing ceiling and zero dropped requests across 20,223 failover requests. Version 0.0.29 (10 August 2026) added an embedded loopback-only admin dashboard at /internal/ui/, native Prometheus metrics at /internal/metrics and a refreshed 2026 model catalogue covering GPT-OSS, GLM, Kimi, Granite and Nemotron. It is deliberately small to operate: one docker run, a YAML config, built-in rate limiting, request validation and structured audit logging with millisecond-precision traces. Under Apache-2.0 it costs nothing. Positioning-wise this is a local-fleet router, not a provider hub — if most of your traffic goes to OpenAI, Bedrock or Anthropic, a multi-provider gateway is the better shape.
Behind the Verdict
The decision here is straightforward: how many inference boxes do you actually run? With one backend and one model, Olla adds a hop and a YAML file for no routing benefit. With two or more, the case gets real fast — automatic failover, sticky KV-cache routing and a single :40114 endpoint your Open WebUI, Cursor or Cline clients point at once. We'd reach for this on a fleet of mixed engines. Apple Silicon teams juggling MLX and GGUF models get native oMLX support, and the unified model registry with aliases means your app calls one model name regardless of which node answers. Watch the operational shape before committing. The admin dashboard is read-only and loopback-only, so adding endpoints and changing routing still happens in YAML — a restart-and-reconfigure workflow, not a live control plane. Non-technical operators will bounce off that. Budget for config discipline as your fleet grows. How does it compare to LiteLLM? Think of LiteLLM as a provider hub with a Python runtime, Olla as a lean Go router for your own hardware. The 14.8x idle-memory difference on the same mock backend is Olla's pitch, and it holds for teams who care about a proxy that idles near 20 MB. The tradeoff: LiteLLM's provider catalog and Python ecosystem are broader. If your spend is mostly hosted APIs, LiteLLM wins. If your spend is GPUs you already own, Olla is the tighter fit. What it is not: a GPU scheduler, a multi-tenant platform or a billing layer. Buyers needing those should look elsewhere — TensorFoundry points enterprise-scale work at FoundryOS. In practice Olla sits between inference engines and app clients, handling rate limiting, request validation and audit logging so you don't build that glue yourself. The Apache-2.0 license removes procurement friction. You own the Docker
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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.
You run Ollama on a workstation, vLLM on a GPU box and llama.cpp on a spare server. You deploy Olla with docker run -p 40114:40114, list all three in the YAML config, and point Open WebUI at http://localhost:40114/olla/openai/v1.
Outcome: One endpoint and one model list for the whole fleet; you get /internal/metrics for Prometheus and /internal/ui/ for fleet status without writing a sidecar.
You have oMLX serving MLX models and llama.cpp serving GGUF, with different model names for the same weights. You enable model aliasing in Olla so both resolve under one name, and turn on sticky sessions for long-context chats.
Outcome: Clients like Cursor and Cline stop needing per-backend config, and follow-up turns stay on the node holding the KV cache instead of being re-routed to a cold one.
One of your two backends crashes mid-conversation. Olla's circuit breaker trips on it, requests retry against the healthy endpoint with exponential backoff, and the broken node is gradually reintroduced once health checks pass.
Outcome: Chat keeps working through the outage; TensorFoundry's published failover benchmark recorded zero dropped requests across 20,223 requests with a backend killed mid-run.
Use Cases
- Unify Ollama, vLLM and llama.cpp behind one OpenAI-compatible endpoint on port 40114
- Fail over automatically between local backends so a crashed node does not take chat down
- Route Anthropic-format requests to local models using built-in message format translation
- Monitor and rate-limit access to self-hosted LLM infrastructure with /internal/metrics
- Keep follow-up chat turns on the node holding the KV cache with sticky sessions at long context
- Run oMLX and GGUF models behind a single endpoint on Apple Silicon hardware
Models Under the Hood
as of 2026-10-15
Limitations
- Olla is a proxy and load balancer, not an inference engine - you still need separate backends such as Ollama, LM Studio, vLLM, SGLang, MLX or llama.cpp running somewhere.
- Deployment is container-based (docker run) and configuration is YAML, so there is no point-and-click setup path, and the /internal/ui/ dashboard added in v0.0.29 is read-only.
- It is an early-stage project at v0.0.29 as of August 2026, with the pre-1.0 versioning that implies and support primarily through GitHub.
- Anthropic passthrough translates message formats; it does not promise every Claude-shaped client feature behaves identically on every backend.
- The v0.0.29 release note is explicit that configs embedding credentials in endpoint URLs now fail startup until the credentials are moved out, so older config files may need editing before upgrade.
as of 2026-10-12
Verification history
We have re-verified Olla 11 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-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
Showing the 6 most recent of 11 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.
Apache 2.0 (Open Source)
$0
Ideal for
Platform engineers and small teams running two or more self-hosted inference engines who want one endpoint, failover and metrics without paying per-token gateway fees
What this tier adds
Starting tier: full LLM proxy and load balancer under Apache 2.0 with no feature gates - unified API on port 40114, all four routing strategies, circuit breakers, sticky sessions, /internal/ui/ dashboard and /internal/metrics
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 Apache-2.0 open source, so the software cost is zero and you can run unlimited endpoints, models and requests on your own hardware. What you pay for instead is ops time: you run the container, write the YAML and carry the pager yourself, and you supply the inference boxes. Compare that against paid managed gateways that charge per token or per seat - Olla wins on cost wherever your compute is already local, and loses the moment you would rather somebody else ran it.
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.
Container-based setup: a platform engineer with an existing backend can typically go from docker run to a unified model list in under half an hour, since the sensible-defaults config from v0.0.23 means a lean YAML file covers most deployments. Count on noticeably longer if you are adding per-endpoint auth, Anthropic passthrough, alias mappings or Prometheus scraping, and budget an extra pass if
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 a direct Ollama or vLLM connection: point your client at http://<host>:40114/olla/openai/v1 instead of the engine's own port - the request shape is unchanged.
- →From LiteLLM: keep the same OpenAI-compatible client config and list your local endpoints in Olla's YAML instead of LiteLLM's config when your backends are local rather than hosted providers.
- →From hand-rolled nginx or HAProxy rules in front of inference engines: replace the upstream block and health checks with Olla's YAML endpoint list and built-in circuit breakers.
- ↗To LiteLLM: move your endpoint list into LiteLLM's config if most of your traffic shifts to hosted providers like OpenAI or Bedrock, where a provider hub fits better.
- ↗To a managed gateway: if you would rather not run the container or carry the pager, the same OpenAI-compatible client config can be repointed at a hosted gateway.
- ↗To direct engine connections: with only one backend and one model, drop the proxy hop and point clients straight at Ollama, vLLM or llama.cpp.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Olla”, and we withheld 6: 6 could not be judged, because “Olla” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Olla.
Official links
Tools that pair well with Olla
Common stack mates teams adopt alongside Olla, with the specific reason each pairing earns its keep.
9router
Open-source AI routing gateway that fronts 60+ providers behind one OpenAI-compatible endpoint at localhost:20128/v1.
OmniRoute
OmniRoute is a self-hosted open-source AI router that sends every request through one OpenAI-compatible endpoint at localhost:20128/v1.
Gateway
Portkey's AI Gateway routes, caches, and governs 1600+ LLMs behind one OpenAI-compatible API endpoint.
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 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.
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.
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Frequently Asked Questions
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