Ollama Herd vs Ollama Proxy/Routing Tools

What are Ollama Proxy and Routing Tools?

A growing ecosystem of open-source tools addresses pieces of the local-AI routing problem. Some are lean round-robin dispatchers; some add model-aware routing or per-user fair-share queues; some manage model loading on a single machine. Each solves one slice of fleet coordination. Very few attempt the whole problem, full routing intelligence, multimodal support, capacity learning, and observability, and of those that do, only one is a serious peer to Herd.

The One Serious Peer: Olla

Olla (built by TensorFoundry) is the standout of this field and the closest thing to a direct competitor Herd has. It's a high-performance Go proxy supporting more backend engines than Herd, vLLM, SGLang, LMDeploy, llama.cpp, Docker Model Runner, Ollama, and more, with production-proxy machinery like circuit breakers and KV-cache sticky sessions. If your fleet is a rack of dedicated, interchangeable inference servers across many engines, Olla is excellent.

The difference is philosophical. Olla treats machines as endpoints with a health status, listed in a config file. Herd treats them as workstations with conditions, is this Mac in a meeting, thermally throttling, being actively used, does it already have the model warm, discovered automatically via mDNS. Olla is the better generic proxy; Herd is the better scheduler for a fleet of real Macs that people also work on. See the full Ollama Herd vs Olla comparison →

What is Ollama Herd?

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

The Players

ollama_proxy_server (~200 stars), Python proxy that sits in front of multiple Ollama instances. Provides API key authentication and model-aware routing. Routes requests to whichever backend has the requested model loaded. Simple, functional, limited.

ollama_load_balancer (~50 stars), Rust-based parallel request dispatcher. Round-robins requests across Ollama instances. Fast, minimal, no intelligence. Think nginx for Ollama.

llama-swap (~2.7K stars), Model loading/unloading orchestrator for a single Ollama instance. Automatically swaps models in and out of GPU memory based on incoming requests. Solves the "I have 10 models but only enough VRAM for 2" problem on one machine.

SOLLOL (~4 stars), The most ambitious of the small tools. Context-aware scoring, priority queues, auto-discovery, and a dashboard. Closest to Herd's vision but far less mature, with minimal community validation.

OLOL (~23 stars), Ollama inference cluster tool. Groups Ollama instances and distributes requests. Basic clustering without scoring intelligence.

Hive (~17 stars), Task queue for Ollama. Queues inference requests and dispatches them to available backends. More of a job scheduler than a router.

OllamaFlow, Label-based routing layer, now archived (its successor is a heavier multi-tenant control plane, Conductor). Included here for lineage.

The current-generation slice tools, a newer wave has more traction: ollamaMQ (per-user fair-share queues for shared labs), ollamafarm (multi-Ollama management with offline detection), and LoLLMs Hub (a broader secure orchestration platform). Each is strong at its slice. None combine device-condition scoring, learned availability, multimodal routing, and mDNS discovery the way Herd does, and for the whole-problem comparison, Olla remains the one serious peer.

Feature Comparison

What Each Does Well

• ollama_proxy_server, Solves a real problem simply: API key gating for shared Ollama access. If you need auth in front of multiple instances, it works. Good for teams sharing a few machines where access control matters more than routing intelligence.
• ollama_load_balancer, Pure speed. Written in Rust, minimal overhead, just dispatches requests round-robin. If your Ollama instances are identical and you want the thinnest possible proxy, this is it.
• llama-swap, Clever single-machine optimization. Automatically loads/unloads models based on demand, which is genuinely useful when you have more models than VRAM. Different problem than fleet routing, but complementary.
• SOLLOL, The most conceptually similar to Herd. Attempts scoring, queuing, discovery, and dashboard in one package. Shows that someone else sees the same problem space. Limited by tiny community and early maturity.
• OLOL, Basic clustering that works. If you just need "spread requests across 3 machines" without any intelligence, it gets the job done.
• Hive, Task queue model is interesting for batch workloads. If you're processing 1,000 documents and want to queue them across backends, Hive's approach makes sense.
• OllamaFlow, Label-based routing is useful for heterogeneous fleets where you want to manually direct certain models to certain hardware. Static but explicit.

Where They All Fall Short

• No fleet intelligence. Round-robin and model-matching are not routing intelligence. None of these tools (except SOLLOL's early attempt) consider GPU memory pressure, thermal state, historical latency, or device capability when making routing decisions.
• No capacity learning. They treat all backends as static. Herd learns that your Mac Studio handles 70B models well and your MacBook Air should stick to 7B. These tools route blind.
• No multimodal routing. Every tool in this list handles LLM inference only. None route embeddings, image generation, or speech-to-text. Herd routes all five model types.
• No dynamic context optimization. They pass through context windows as-is. Herd adjusts context sizes based on available device memory, preventing OOM failures and maximizing throughput.
• No comprehensive dashboard. SOLLOL has a basic dashboard. The rest are CLI-only or have minimal status endpoints. Herd's 8-tab dashboard provides fleet health, routing decisions, model distribution, device status, benchmark results, and more.
• Minimal testing. Most have few or no tests. Herd has 1200+ tests and 30+ health checks. For infrastructure that routes your AI workloads, test coverage matters.
• No smart benchmark. None measure actual device inference capability. Herd's smart benchmark tests real throughput on each device, feeding results into routing decisions.

The "DIY Alternative"

In theory, you could wire together ollama_proxy_server for auth + model-aware routing, ollama_load_balancer for throughput distribution, and llama-swap on each node for model memory management. This gives you auth, distribution, and model swapping. But you still lack:

• Intelligent scoring (thermal, memory pressure, latency history)
• Auto-discovery (manual config for every instance)
• Multimodal routing (LLM only)
• Capacity learning (static assumptions about each backend)
• Unified dashboard (three separate tools, three separate monitoring stories)
• Dynamic context optimization
• Health checks and test coverage across the integration points

And you're maintaining three tools from three different authors with three different update cycles and no guarantee they play well together. Herd is the integrated answer. One install, zero config, all the intelligence.

When to Choose a Proxy Tool Instead

• ollama_proxy_server, You need API key authentication and Herd doesn't provide it (yet). Auth gating is a genuine gap in Herd.
• ollama_load_balancer, You want the absolute thinnest proxy with minimal latency overhead and don't care about routing intelligence. Rust is fast.
• llama-swap, You have one machine with limited VRAM and many models. This is complementary to Herd, not competitive, Herd routes between machines, llama-swap manages models within one.
• OllamaFlow, You want explicit manual control over which models go where, with human-defined labels rather than automatic scoring.

When to Choose Ollama Herd

• You have multiple devices and want intelligent routing across all of them
• You want zero-config setup, mDNS discovery, capacity learning, dynamic optimization
• You need multimodal routing beyond just LLMs
• You want a dashboard to see what your fleet is doing
• You need production-grade reliability with real test coverage
• You want routing that gets smarter over time as it learns your fleet

Bottom Line

The existence of 7+ Ollama routing tools validates that the problem is real. People with multiple Ollama instances need a way to coordinate them. But the fragmented, single-feature nature of these tools shows that nobody has built the complete solution, except Herd.

None of these tools are competitive threats individually. They're validation signals. The fragmented landscape confirms the need; Herd is the complete answer.

Getting Started

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

Frequently Asked Questions

Can I use llama-swap alongside Ollama Herd?

Yes. They solve different problems. llama-swap manages model loading and unloading on a single machine with limited VRAM. Herd routes requests across multiple machines. You can run llama-swap on individual nodes for model memory management while Herd handles fleet-level routing.

Does Ollama Herd support API key authentication?

Not yet. This is a genuine gap compared to ollama_proxy_server. If API key gating is your primary requirement for shared access, ollama_proxy_server handles that today. Herd is designed for trusted local networks.

Why not wire together multiple proxy tools?

You could combine ollama_proxy_server for auth, ollama_load_balancer for dispatch, and llama-swap for model management. But you still lack intelligent scoring, auto-discovery, multimodal routing, capacity learning, and a unified dashboard. You are also maintaining three tools from three authors with no integration guarantees.

How does Herd compare to SOLLOL?

SOLLOL is conceptually the closest to Herd, attempting scored routing, priority queues, and auto-discovery. However, it has minimal community validation (~4 stars), limited test coverage, and no multimodal support. Herd has 1200+ tests, 30+ health checks, and routes 5 model types.

Is ollama_load_balancer faster than Herd because it is written in Rust?

The proxy layer overhead is minimal in both. ollama_load_balancer adds the least possible latency for pure round-robin dispatch, but the routing decision itself (1–2ms) is negligible compared to actual inference time (seconds). Herd's scoring intelligence more than compensates by sending requests to the node that will complete fastest.

See Also

• Ollama Herd vs Single Ollama, The simplest starting point before any routing is needed
• Ollama Herd vs DIY Scripts, Building your own routing from scratch vs using Herd
• Ollama Herd vs Cloud APIs, Fleet inference economics compared to cloud API pricing