Ollama Herd vs Olla

What is Olla?

Olla is a high-performance, low-overhead proxy and load balancer for self-hosted LLM infrastructure, written in Go and licensed Apache-2.0. It's built by TensorFoundry, an Australian AI-infrastructure company. Olla routes requests across a very broad set of natively-supported backends, Ollama, LM Studio, vLLM, vLLM-MLX, llama.cpp, SGLang, LMDeploy, Docker Model Runner, oMLX, Lemonade SDK, LiteLLM, and generic OpenAI-compatible endpoints, on Linux, macOS, and Windows, in under 50MB of RAM. It's a genuinely well-built project with good docs and an active community.

What is Ollama Herd?

Ollama Herd is an open-source smart multimodal AI router that turns multiple inference nodes across Apple Silicon and mixed hardware into one intelligent endpoint. It routes LLMs, embeddings, image generation, speech-to-text, and vision with a 8-signal scoring engine, mDNS auto-discovery, an 8-tab real-time dashboard, and OpenAI + Ollama + Anthropic Messages API compatibility. Two commands to set up, zero config files. pip install ollama-herd or brew install ollama-herd.

The Core Difference

Olla treats your machines as interchangeable inference endpoints with a health status, up or down, listed in a config file, and routes across the widest set of backend engines with production-proxy machinery. Herd treats your machines as workstations with conditions, is this Mac in a video call, running a heavy foreground app, thermally throttling, or being actively used right now, and does it already have the model warm, and routes with a 8-signal device-aware scoring engine.

On a rack of dedicated inference servers across mixed engines, Olla's model fits well. On a fleet of real Macs that people also use for work, Herd's model fits better. That's the honest distinction, and it's the whole comparison.

Where the Two Overlap

Being straight about what is not a difference:

• Anthropic Messages API / Claude Code. Both support it. Olla offers passthrough and automatic translation and documents Claude Code, OpenCode, and Crush. Herd handles it natively with no conversion, plus three-layer context management for long sessions. This is not a "only we have it" axis, both do.
• OpenAI-compatible proxying, health checks, retries, failover. Table stakes; both have them.
• Cross-platform. Both run on macOS, Linux, and Windows.
• Streaming-first, low overhead. Both. Olla is a compiled Go binary and notably lean.

Feature Comparison

Where Olla Wins

1. Backend breadth. 11+ natively-supported engines vs Herd's focused set. If your fleet mixes vLLM, SGLang, LMDeploy, llama.cpp, and Ollama, Olla routes across all of them today. This is Olla's decisive advantage.
2. Proxy-engine sophistication. Circuit breakers, connection pooling, and KV-cache-aware sticky sessions (pinning multi-turn conversations to the same backend to reuse the KV cache) are real production-proxy features.
3. Production hardening. Rate limiting, request size limits, configurable CORS, graceful shutdown, the operational essentials for a proxy in front of many clients.
4. Lean footprint. A single compiled Go binary running in under 50MB of RAM.
5. Maturity and momentum. A larger community and a company (TensorFoundry) behind it with active releases.

Where Ollama Herd Wins

1. Workstation-condition awareness. This is the core difference. Olla treats every endpoint as interchangeable and health-up-or-down. Herd knows when a Mac is in a video call (meeting detection), running a heavy foreground app, thermally throttling, or being actively used, and routes around it. On a fleet of real Macs that people also work on, this is the difference between a scheduler that helps and one that lands inference on the laptop you're presenting from.
2. mDNS zero-config discovery. Olla needs a config file listing every endpoint. Start herd-node on a machine and it joins the fleet in about 60 seconds, no config edit. For a fluid home-lab or small-team fleet where machines come and go, that's a real difference.
3. Learned weekly availability. A 168-slot behavioral model learns each device's usage patterns and de-prioritizes machines during their busy hours. Olla has no equivalent.
4. Model-lifecycle-aware scoring. Residency (is the model already warm here?), cold-start cost, and prewarming are part of Herd's placement decision, not just discovery.
5. Capability-aware multimodal routing. Herd treats LLM, embeddings, image gen, STT, and vision as distinct service categories, with a native fastembed path for zero-contention embeddings. Olla is an LLM-chat proxy.
6. Rich web dashboard. An 8-tab live dashboard over the whole fleet, overview, trends, model insights, per-tag analytics, benchmarks, health, recommendations, settings.
7. Appliance simplicity for the Apple case. pip install, two commands, no config file, LAN discovery.
8. Completely free, no commercial gate. Herd is MIT-licensed with no paid tier, no token, no upsell, and no commercial product behind it, what you install is the whole thing, forever. Olla is open source too (Apache-2.0), so both are free to run; the difference is that Olla is the open-source product of a company that also sells a separate enterprise platform. Both models are fine, but if you specifically want a pure community project with no commercial direction to answer to, that's Herd.

When to Choose

Bottom Line

Olla and Herd are the two projects closest to each other in this space, and Olla is the more mature proxy, broader backends, more production-proxy features, a bigger community. The honest positioning isn't "we're better." It's "we're built for a different fleet." Olla is built for infrastructure; Herd is built for workstations.

A group of Macs that people actually use for work are not interchangeable servers. Some are in meetings, some are running a build, some already hold the model warm, some are thermally throttling, some are temporarily busy. That reality, and scheduling around it, is what Herd is designed for, and it's the axis where a device-condition-aware scheduler beats a generic proxy.

Getting Started

If you already have Ollama or MLX running on your Macs, Herd discovers them automatically and starts routing in under two minutes.

pip install ollama-herd    # or: brew install ollama-herd
herd                       # start router
herd-node                  # on each device

FAQ

Is Ollama Herd a good alternative to Olla?

They optimize for different fleets. Olla is a broad multi-engine proxy that routes across 11+ inference backends and treats endpoints as interchangeable. Herd is an Apple-fleet-aware scheduler that routes around the condition of each Mac, thermal state, meetings, foreground apps, learned availability. Choose Herd when your machines are real workstations, not dedicated servers.

Does Olla support more backends than Ollama Herd?

Yes. Olla natively supports 11+ engines including vLLM, SGLang, LMDeploy, llama.cpp, and Docker Model Runner. Herd focuses on Ollama, MLX, and native fastembed. If broad engine coverage is your priority, Olla is the better fit, we won't pretend otherwise.

Do both support the Anthropic Messages API?

Yes, both do. Olla offers passthrough and translation. Herd handles it natively with no format conversion, plus three-layer context management for long Claude Code sessions on local models.

What does Ollama Herd do that Olla does not?

Herd models each machine as a workstation: thermal state, meeting detection, foreground-app activity, and a learned 168-slot weekly availability pattern, plus mDNS zero-config discovery and capability-aware multimodal routing. Olla treats endpoints as interchangeable health-up-or-down servers listed in a config file.

Is Ollama Herd free?

Yes. Ollama Herd is open-source under the MIT license, no paid tiers, no API keys, no subscriptions. Olla is also open-source, under Apache-2.0.

See Also

• Ollama Herd vs Ollama Proxies, the broader single-purpose-proxy category
• Ollama Herd vs LiteLLM, cloud API gateway (Olla itself recommends LiteLLM as the cloud bridge)
• Ollama Herd vs GPUStack, enterprise GPU cluster manager