LiteLLM routes between cloud APIs. Ollama Herd routes between local devices. They solve fundamentally different problems — and work best together for hybrid cloud/local setups.
LiteLLM (~38K GitHub stars) is an open-source Python SDK and proxy server built by BerriAI. It lets you call 100+ LLM providers (OpenAI, Anthropic, Bedrock, Azure, Vertex, Cohere, and more) through a single OpenAI-compatible interface. LiteLLM handles provider abstraction, API key management, rate limiting, spend tracking, and team governance, making it the de facto standard for cloud LLM API routing.
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 7-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 key distinction: LiteLLM routes between cloud API providers. Ollama Herd routes between local physical devices. They solve fundamentally different problems and are more complementary than competitive.
| Feature | LiteLLM | Ollama Herd |
|---|---|---|
| Primary function | Cloud LLM API gateway | Local device fleet router |
| Supported providers | 100+ cloud APIs | Ollama instances on local network |
| Model types | LLMs (text/chat) | LLMs, embeddings, image gen, STT |
| API compatibility | OpenAI format | OpenAI + Ollama format |
| Discovery | Manual provider config | mDNS auto-discovery (zero config) |
| Routing intelligence | Load balancing, failover, routing by cost/latency | 7-signal scoring (VRAM, thermal, queue depth, memory, model affinity, capacity, latency) |
| Hardware awareness | None (cloud abstraction) | GPU memory, thermal state, meeting detection |
| Cost tracking | Per-token spend tracking across providers | Free (local inference, no API costs) |
| API key management | Virtual keys, budgets, rotation | Not applicable (no API keys needed) |
| Team management | SSO, RBAC, per-team budgets | Single-user / small-team fleet |
| Guardrails | Content filtering, PII masking | None (local inference, you own the data) |
| Logging/observability | Request logging, Prometheus, custom callbacks | 8-tab dashboard, real-time fleet metrics |
| Caching | Semantic caching | Dynamic context optimization |
| Failover | Automatic provider fallback | Automatic device fallback with re-scoring |
| Deployment | Docker, pip, hosted proxy | pip, Homebrew, runs on any Mac |
| Language | Python | Python |
| Cloud dependency | Required (routes to cloud APIs) | None (fully local) |
| Data sovereignty | Data leaves your network | Data never leaves your network |
| Test suite | Community tested | 480+ tests, 17 health checks |
LiteLLM and Ollama Herd are not competitors — they operate at different layers:
They can work together in two ways:
| Scenario | Choose |
|---|---|
| Need GPT-4, Claude, Gemini behind one API | LiteLLM |
| Need to track cloud API spend across teams | LiteLLM |
| Have multiple Macs and want to use them all | Ollama Herd |
| Data cannot leave your network | Ollama Herd |
| Want zero inference costs | Ollama Herd |
| Enterprise team with budget governance needs | LiteLLM |
| Personal/small-team local AI setup | Ollama Herd |
| Need multimodal routing (image gen, STT) on local hardware | Ollama Herd |
| Want both cloud and local behind one endpoint | LiteLLM + Ollama Herd together |
The comparison between LiteLLM and Ollama Herd is mostly a category error. LiteLLM is a cloud API gateway; Herd is a local fleet router. They overlap only in the narrow sense that both route AI requests to backends.
The real question is not "which one?" but "do I need cloud routing, local routing, or both?" For teams with Apple Silicon hardware that want private, free, hardware-aware inference routing, Herd does something LiteLLM fundamentally cannot. For teams that need 100+ cloud providers behind one endpoint, LiteLLM does something Herd has no interest in doing.
The best setup for many teams is both: Herd for local, LiteLLM for cloud, with Herd registered as a LiteLLM backend for seamless hybrid routing.
You can try Ollama Herd alongside LiteLLM without changing your existing cloud setup. Install Herd, point your local apps at it for private inference, and register the Herd endpoint as a LiteLLM backend for hybrid routing.
pip install ollama-herd
herd # start router
herd-node # on each deviceThey are complementary rather than competitive. LiteLLM excels at routing between cloud API providers with cost tracking and team governance. Ollama Herd excels at routing between local devices with hardware-aware scoring. If your goal is private, zero-cost local inference across Apple Silicon, Herd is the right tool.
Yes, and this is the recommended setup for teams that need both cloud and local AI. Register your Ollama Herd endpoint as a custom provider in LiteLLM. Your apps get one gateway that routes to cloud APIs for frontier models and to your local fleet for private or cost-sensitive work.
Herd is purpose-built for local inference routing with 7-signal hardware-aware scoring, mDNS auto-discovery, and multimodal support. LiteLLM can route to local Ollama instances, but it has no awareness of GPU memory, thermal state, or device capabilities. For local fleet routing, Herd makes significantly better decisions.
No. Ollama Herd routes to local Ollama instances on your network. There are no API keys, no provider configuration, and no cloud accounts needed. Everything runs on hardware you own.
Yes. Ollama Herd is open-source under the MIT license. No paid tiers, no API keys, no subscriptions.