---
title: Olla vs LiteLLM - Comparison Guide for LLM Infrastructure
description: Compare Olla and LiteLLM for your AI infrastructure. Learn when to use each tool and how to combine them for optimal performance.
keywords: olla vs litellm, litellm comparison, llm proxy comparison, ai gateway, api translation, load balancing
---

# Olla vs LiteLLM

> **Native Integration Available!** 🎉  
> Olla now includes native LiteLLM support through a dedicated profile. This means you can use LiteLLM as a backend provider just like Ollama or LM Studio, with full health checking, load balancing and failover capabilities. See [LiteLLM Integration](../integrations/backend/litellm.md).

## Overview

[Olla](https://github.com/thushan/olla) and [LiteLLM](https://github.com/BerriAI/litellm) solve different problems in the LLM infrastructure stack. **Olla now provides native LiteLLM support**, making them perfect companions rather than competitors.

## Core Differences

### Primary Purpose

**Olla**: Infrastructure-level proxy focused on reliability and load balancing

- Makes existing endpoints highly available
- Provides failover and circuit breakers
- Optimised for self-hosted infrastructure

**LiteLLM**: API translation and abstraction layer

- Converts between different LLM API formats
- Provides unified interface to 100+ providers
- Handles authentication and rate limiting for cloud providers

### Architecture

**Olla (with native LiteLLM support)**:
```
Application → Olla → Multiple Backends
                ├── Ollama instance 1
                ├── Ollama instance 2
                ├── LM Studio instance
                └── LiteLLM gateway → Cloud Providers
                                  ├── OpenAI API
                                  ├── Anthropic API
                                  └── 100+ other providers
```

**LiteLLM (standalone)**:
```
Application → LiteLLM → Provider APIs
                    ├── OpenAI API
                    ├── Anthropic API
                    └── Cohere API
```

## Feature Comparison

| Feature | Olla | LiteLLM |
|---------|------|---------|
| **Routing & Load Balancing** | | |
| Priority-based routing | ✅ Sophisticated | ⚠️ Basic fallbacks |
| Round-robin | ✅ | ❌ |
| Least connections | ✅ | ❌ |
| Circuit breakers | ✅ | ❌ |
| Health monitoring | ✅ Continuous | ⚠️ On-request |
| **API Management** | | |
| API translation | ⚠️ Anthropic only | ✅ Extensive (100+ providers) |
| Provider auth | ✅ Outbound (v0.0.28+) | ✅ |
| Cost tracking | ❌ | ✅ |
| Rate limit handling | ✅ Internal | ✅ Provider-aware |
| **Performance** | | |
| Latency overhead | <2ms | 10-50ms |
| Memory usage | ~40MB | ~200MB+ |
| Streaming support | ✅ Optimised | ✅ |
| Connection pooling | ✅ Per-endpoint | ⚠️ Global |
| **Deployment** | | |
| Single binary | ✅ | ❌ |
| No dependencies | ✅ | ❌ (Python) |
| Container required | ❌ | Optional |

## When to Use Each

### Use Olla When:

- Managing multiple self-hosted LLM instances
- Need high availability for local infrastructure
- Require sophisticated load balancing
- Want minimal latency overhead
- Running on resource-constrained hardware

### Use LiteLLM When:

- Integrating multiple cloud providers
- Need API format translation
- Want cost tracking and budgets
- Require provider-specific features
- Building provider-agnostic applications

## Using Them Together (Native Integration)

With Olla's **native LiteLLM support**, integration is seamless:

### Native Integration (Recommended)
```yaml
# Olla config with native LiteLLM support
discovery:
  static:
    endpoints:
      # Local models (high priority)
      - name: local-ollama
        url: http://localhost:11434
        type: ollama
        priority: 100

      # LiteLLM gateway (native support)
      - name: litellm-gateway
        url: http://localhost:4000
        type: litellm  # Native LiteLLM profile
        priority: 75
        model_url: /v1/models
        health_check_url: /health
```

**Benefits**:

- Native profile for optimal integration
- Automatic model discovery from LiteLLM
- Health monitoring and circuit breakers for cloud providers
- Unified endpoint for local AND cloud models
- Intelligent routing based on model availability

### Option 2: Side-by-Side
```
Applications
     ├── Olla → Local Models (Ollama, LM Studio)
     └── LiteLLM → Cloud Providers (OpenAI, Anthropic)
```

**Benefits**:

- Clear separation of concerns
- Optimised paths for each use case
- Simpler troubleshooting

## Real-World Examples

### Home Lab with Cloud Fallback
```yaml
# Use Olla to manage local + LiteLLM for cloud
discovery:
  static:
    endpoints:
      - name: local-3090
        url: http://localhost:11434
        type: ollama
        priority: 1
      - name: litellm-cloud
        url: http://localhost:4000  # LiteLLM with OpenAI/Anthropic
        type: litellm
        priority: 10  # Only use when local is down
```

### Enterprise Multi-Region
```yaml
# Olla provides geographic routing
discovery:
  static:
    endpoints:
      - name: sydney-litellm
        url: http://syd-litellm:8000
        type: litellm
        priority: 1
      - name: melbourne-litellm
        url: http://mel-litellm:8000
        type: litellm
        priority: 2
```

## Performance Considerations

### Latency Impact

- **Olla alone**: <2ms overhead
- **LiteLLM alone**: 10-50ms overhead
- **Olla + LiteLLM**: ~12-52ms total overhead

### Resource Usage

- **Olla**: ~40MB RAM, minimal CPU
- **LiteLLM**: 200MB+ RAM, higher CPU usage
- **Both**: ~250MB RAM total

## Migration Patterns

### From LiteLLM to Olla + LiteLLM

1. Deploy Olla in front of existing LiteLLM
2. Add local endpoints to Olla config
3. Update applications to point to Olla
4. Monitor and tune load balancing

### Adding LiteLLM to Olla Setup

1. Deploy LiteLLM instance
2. Configure cloud providers in LiteLLM
3. Add LiteLLM as endpoint in Olla
4. Set appropriate priority

## Common Questions

**Q: Can Olla do API translation like LiteLLM?**
A: Olla supports Anthropic Messages API translation (Anthropic ↔ OpenAI format) for local backends. For broad multi-provider API translation across 100+ cloud providers, use LiteLLM.

**Q: Can LiteLLM do failover like Olla?**
A: LiteLLM has basic fallbacks, but lacks Olla's health monitoring, circuit breakers, and sophisticated load balancing.

**Q: Which is faster?**
A: Olla adds <2ms latency. LiteLLM adds 10-50ms due to API translation. For local models, use Olla directly.

**Q: Can I use both in production?**
A: Absolutely! Many production deployments use Olla for infrastructure reliability and LiteLLM for cloud provider access.

## Conclusion

Olla and LiteLLM are complementary tools:

- **Olla** excels at infrastructure reliability and load balancing
- **LiteLLM** excels at API abstraction and cloud provider management

Choose based on your primary need, or better yet, use both for a robust, flexible LLM infrastructure.