How Ollama Herd Compares

Quick Comparison

Detailed Comparisons

vs. Olla

Olla is a high-performance multi-engine proxy and load balancer for self-hosted LLMs, built by TensorFoundry. It routes across the widest set of backends — vLLM, SGLang, LMDeploy, llama.cpp, Docker Model Runner, Ollama, and more — with production-proxy machinery like circuit breakers and KV-cache sticky sessions. It's our most direct competitor and a genuinely well-built project.

The difference is what each schedules around. Olla treats machines as interchangeable inference endpoints with a health status, listed in a config file. Herd treats them as workstations with conditions — thermal state, meetings, foreground apps, learned weekly availability, warm models — discovered automatically via mDNS. Olla is the better generic proxy; Herd is the better Apple-fleet-aware scheduler.

Choose Olla when: Your fleet mixes many inference engines and you want the broadest backend coverage.

Choose Herd when: Your fleet is real Macs that people also use for work, and routing around each machine's condition matters.

Read the full Olla comparison →

vs. Single Ollama

Running one Ollama instance is the starting point. It works great — until you have more than one machine or more than one concurrent user.

When single Ollama is enough:

• You have one machine
• You run one model at a time
• You don't mind waiting in a queue

When you need Herd:

• You have 2+ machines and want them to work together
• Multiple tools hit Ollama simultaneously (agents, coding assistant, chat)
• You're tired of model thrashing (loading/unloading models to free memory)
• Your MacBook fans spin up during inference and you want requests routed elsewhere

vs. LM Studio's LM Link

LM Link is LM Studio's private multi-device feature — connect your LM Studio install to other machines over a Tailscale mesh and access local models remotely. End-to-end encrypted, free up to 2 users / 10 devices in preview.

LM Link connects your Macs to each other. Ollama Herd routes across your whole team's mixed fleet — Mac, Linux, Windows, any Ollama- or MLX-compatible node — with intelligent scoring, three-layer context management for long Claude Code sessions, per-tier model mapping, and a real admin dashboard. LM Link is connectivity; Ollama Herd is orchestration.

Choose LM Link when: You're all-in on LM Studio as your model runner and just need remote access from other Macs.

Choose Herd when: Your fleet is heterogeneous (mixed OSes, multiple runtimes) or you need scoring/routing/compaction/admin features beyond "connect these devices."

Read the full LM Link comparison →

vs. exo

exo splits a single large model across multiple devices using tensor parallelism. If one machine can't fit a 405B model, exo distributes the layers so they collectively run it.

exo and Herd solve different problems. exo answers "how do I run a model too big for one machine?" Herd answers "how do I route many requests to many models across many machines?" They're complementary — an exo cluster can register as a single Herd node.

Choose exo when: You need to run one model that's too large for any single device.

Choose Herd when: You have multiple devices that can each run their own models and you want intelligent routing across all of them.

vs. Apple's Distributed MLX

At WWDC26, Apple shipped first-party distributed inference for MLX (macOS 26.2+): RDMA over Thunderbolt 5, the open-source JACCL collective communication library, and MLX Distributed with tensor and pipeline parallelism. It runs frontier-scale models across Thunderbolt-connected Mac Studios — the same model-sharding problem exo solves, now from the platform vendor.

Same distinction as exo: Apple's stack dedicates a pure-Apple cluster to one huge model; Herd routes many models across a mixed fleet with 7-signal scoring. An MLX Distributed cluster can serve behind Herd as one very powerful node.

Choose MLX Distributed when: You have Thunderbolt 5-connected Macs and one model too big for any of them.

Choose Herd when: Your fleet is mixed hardware, your workload is multi-model or multimodal, or you want request-level routing intelligence.

vs. LiteLLM

LiteLLM is a cloud API gateway that provides a unified OpenAI-compatible interface to 100+ LLM providers (OpenAI, Anthropic, Bedrock, Azure, etc.).

Different layer entirely. LiteLLM routes between cloud providers. Herd routes between local devices. LiteLLM has no concept of thermal state, memory pressure, device health, or mDNS discovery. They work together naturally — Herd sits between LiteLLM and your local Ollama instances, giving LiteLLM a single "local" endpoint backed by an intelligent fleet.

Choose LiteLLM when: You need to route between cloud providers or want a unified API across OpenAI/Anthropic/etc.

Choose Herd when: You want your local devices to work together. Use both if you want local + cloud with intelligent routing at each layer.

vs. GPUStack

GPUStack is a GPU cluster manager for AI model deployment. It manages GPU resources across environments (on-prem, Kubernetes, cloud), auto-configures inference engines (vLLM, SGLang, TensorRT-LLM), and supports all GPU vendors.

GPUStack is more polished but more complex. It targets GPU cluster operators who want multi-engine support and enterprise features. Herd targets individuals and small teams who want zero-config fleet management with the Ollama they already use.

Choose GPUStack when: You're managing a GPU cluster with mixed vendors and need multi-engine support.

Choose Herd when: You have a few personal devices running Ollama and want them to work together in 60 seconds.

vs. DIY Scripts

Many people write their own routing scripts — round-robin across Ollama instances, manually checking which node has capacity, or just SSH-ing into whichever machine seems free.

DIY works until it doesn't. You'll spend more time maintaining the scripts than using them. No thermal awareness, no capacity learning, no auto-retry, no dashboard, no meeting detection. Every edge case becomes your problem.

Choose DIY when: You have very specific routing logic that no tool supports.

Choose Herd when: You want routing that handles the edge cases you haven't thought of yet.

Deep Dive Comparisons

Each comparison page covers feature tables, honest pros/cons, when to choose each tool, FAQs, and getting started guides.

• Ollama Herd vs Single Ollama — When to upgrade from one machine to a fleet
• Ollama Herd vs Cloud APIs — Local fleet vs per-token pricing (OpenAI, Anthropic, etc.)
• Ollama Herd vs Olla — Apple-fleet-aware scheduler vs broad multi-engine proxy
• Ollama Herd vs exo — Fleet routing vs model sharding (complementary)
• Ollama Herd vs Apple MLX Distributed — Fleet routing vs Apple's first-party model sharding (WWDC26)
• Ollama Herd vs LM Link — Fleet routing vs LM Studio's Tailscale remote access
• Ollama Herd vs GPUStack — Zero-config fleet vs GPU cluster manager
• Ollama Herd vs LiteLLM — Local fleet router vs cloud API gateway
• Ollama Herd vs LocalAI — Multi-device fleet vs single-machine server
• Ollama Herd vs vLLM — Apple Silicon fleet vs GPU serving engine
• Ollama Herd vs Open WebUI — Intelligent routing vs chat interface
• Ollama Herd vs Bifrost — Hardware-aware fleet vs adaptive load balancer
• Ollama Herd vs Envoy AI Gateway — Personal fleet vs enterprise K8s gateway
• Ollama Herd vs Docker Model Runner — Fleet routing vs container inference
• Ollama Herd vs DIY Scripts — 2-minute install vs months of scripting
• Ollama Herd vs Ollama Proxy Tools — Integrated fleet vs fragmented scripts

The Competitive Landscape

Dozens of projects will route local AI requests for you. Once you look closely, they sort into four buckets — and in the one bucket that overlaps with Herd, exactly one project comes close, and it's built for a different kind of fleet.

Request routers & proxies — the bucket Herd lives in

This is where the real competition is. Most entrants are thin: round-robin dispatchers, model-aware proxies, per-user fair-share queues — each solving one slice of fleet coordination. Tools like ollamaMQ, ollamafarm, and LoLLMs Hub cover a slice well; LiteLLM governs the cloud-gateway edge. The one project that competes with Herd on the whole problem is Olla — a genuinely strong, company-backed multi-engine proxy. It routes across more backend engines than Herd does. But it treats your machines as interchangeable servers in a config file. Herd is the only router that treats them as workstations with conditions — thermal state, meetings, foreground apps, learned weekly availability, warm models — discovered automatically with zero config. Olla is the better generic proxy; Herd is the better fleet-of-real-Macs scheduler.

Distributed-inference clusters — a different problem

exo, Apple's WWDC26 distributed MLX stack, and Distributed Llama shard one model across several machines so you can run models too big for any single device. That's model parallelism, not request routing — and it's complementary: a distributed cluster can register as a single node inside a Herd fleet. We don't compete with them; we route to them.

Single-machine servers — backends, not rivals

vLLM-MLX, vMLX, and the like make one Apple Silicon machine faster and smarter. They're the engines Herd routes to, not alternatives to Herd. The better they get, the better a Herd fleet gets.

Chat interfaces — a different layer

Open WebUI and friends are frontends. Point one at Herd and you get a beautiful chat UI backed by an intelligent fleet. Different layer, natural pairing.

What Makes Herd Unique

No other project combines all of these — and the combination is the point:

1. 7-signal intelligent scoring with learned latency data
2. Workstation-condition awareness — meeting detection + app fingerprinting, because laptops aren't servers
3. Adaptive capacity learning — a 168-slot model of your fleet's weekly rhythm
4. mDNS zero-config discovery — no endpoint list to maintain, truly two commands
5. Per node:model queue management with dynamic concurrency and auto-retry
6. Multimodal routing — LLM, embeddings, image gen, speech-to-text, and vision as distinct service types
7. OpenAI + Ollama + Anthropic Messages — drop-in for any client, including Claude Code
8. Real-time 8-tab dashboard with fleet overview, trends, health, and analytics
9. Completely free and open — MIT licensed, no paid tier, no token, no upsell, no accounts

Plenty of tools do one or two of these. Herd is the only one built around all of them at once — and the only one that starts from the premise that a group of Macs people actually use for work are not interchangeable servers. That's the fleet Herd was built for, and it owns it.

And it's genuinely free. Herd is MIT-licensed top to bottom, with no paid tier, no per-seat pricing, no token, no metering, and no commercial product it's steering you toward. What you pip install is the whole thing, forever. Every feature on this site, including the enterprise ones, is open source. Optional deployment and support help is available if you want it, but the software itself has no gate.