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Olla vs Alternative Solutions

Quick Comparison Matrix

This guide helps you understand how Olla compares to other tools in the LLM infrastructure space. We believe in using the right tool for the job, and often these tools work better together than in competition.

Tool Primary Focus Best For Deployment Language
Olla Load balancing & failover for existing endpoints Self-hosted LLM reliability Single binary Go
LiteLLM API translation & provider abstraction Multi-cloud API unification Python package/server Python
GPUStack GPU cluster orchestration Deploying models across GPUs Platform Go
Ollama Local model serving Running models locally Single binary Go
LocalAI OpenAI-compatible local API Drop-in OpenAI replacement Container/binary Go
Text Generation WebUI Web interface for models Interactive model testing Python application Python
vLLM High-performance inference Production inference serving Python package Python/C++

What Makes Olla Different?

Olla focuses on a specific problem: making your existing LLM infrastructure reliable and manageable.

We don't try to:

  • Deploy models (that's GPUStack's job)
  • Translate APIs (that's LiteLLM's strength)
  • Serve models (that's Ollama/vLLM's purpose)

Instead, we excel at:

  • Intelligent failover - When your primary GPU dies, we instantly route to backups
  • Production resilience - Circuit breakers, health checks, connection pooling
  • Minimal overhead - <2ms latency, ~40MB memory
  • Simple deployment - Single binary, containerised, YAML config, no dependencies

Common Scenarios

"I have multiple machines running Ollama"

Perfect for Olla! Point Olla at all your Ollama instances and get automatic failover, load balancing, and unified access.

"I need to use OpenAI, Anthropic, and local models"

Use Olla + LiteLLM: LiteLLM handles the API translation, Olla provides resilience and routing between LiteLLM instances and local endpoints.

"I have a cluster of GPUs to manage"

Use GPUStack + Olla: GPUStack orchestrates model deployment across GPUs, Olla provides the reliable routing layer on top.

"I just want to run models locally"

Start with Ollama/LocalAI: These are model servers. Add Olla when you need failover or have multiple instances.

Complementary Architectures

Home Lab Setup

Applications
     ↓
   Olla (routing & failover)
     ↓
├── Ollama (main PC)
├── Ollama (Mac Studio)
└── LM Studio (laptop)

Enterprise Setup

Applications
     ↓
   Olla (load balancing)
     ↓
├── GPUStack Cluster (primary)
├── vLLM Servers (high-performance)
└── LiteLLM → Cloud APIs (overflow)

Hybrid Cloud Setup

Applications
     ↓
   Olla
     ↓
├── Local: Ollama/LM Studio
└── Cloud: LiteLLM → OpenAI/Anthropic

When NOT to Use Olla

Let's be honest about when Olla isn't the right choice:

  • Single endpoint only: If you'll only ever have one LLM endpoint, Olla adds unnecessary complexity
  • Need API translation: If your main need is converting between API formats, LiteLLM is purpose-built for this
  • GPU orchestration: If you need to deploy/manage models across GPUs, GPUStack or Kubernetes is what you want
  • Serverless/Lambda: Olla is designed for persistent infrastructure, not serverless

Philosophy

We built Olla to do one thing really well: make LLM infrastructure reliable. We're not trying to replace other tools - we want to make them work better together. The LLM ecosystem is complex enough without tools trying to do everything.

Detailed Comparisons

For in-depth comparisons with specific tools:

Questions?

If you're unsure whether Olla fits your use case, feel free to open a discussion on GitHub. We're happy to help you architect the right solution, even if it doesn't include Olla!