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.

Overview¶

Olla and 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	❌	✅ Extensive
Provider auth	❌	✅
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)¶

# Olla config with native LiteLLM support
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¶

# Use Olla to manage local + LiteLLM for cloud
endpoints:
  - name: local-3090
    url: http://localhost:11434
    priority: 1
  - name: litellm-cloud
    url: http://localhost:4000  # LiteLLM with OpenAI/Anthropic
    priority: 10  # Only use when local is down

Enterprise Multi-Region¶

# Olla provides geographic routing
endpoints:
  - name: sydney-litellm
    url: http://syd-litellm:8000
    priority: 1
  - name: melbourne-litellm
    url: http://mel-litellm:8000
    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¶

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

Adding LiteLLM to Olla Setup¶

Deploy LiteLLM instance
Configure cloud providers in LiteLLM
Add LiteLLM as endpoint in Olla
Set appropriate priority

Common Questions¶

Q: Can Olla do API translation like LiteLLM? A: No, Olla focuses on routing and reliability. Use LiteLLM for API translation.

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.