Open Source AI Gateway for Developers
An open source AI gateway provides a self-hosted solution for routing and managing AI model requests. It allows engineers to deploy, control, and customize AI workflows without relying on proprietary services.
What is an AI Gateway?
An AI gateway acts as a router between your application and various AI models or services. It enables flexible routing, fallback strategies, and integration with different model providers. An open source option offers transparency, customization, and control over the deployment.
Key Features of an Open Source AI Gateway
- Self-hosted deployment: Run the gateway on your infrastructure.
- Model provider integration: Connect to multiple providers like OpenAI, Anthropic, or custom endpoints.
- Routing flexibility: Implement rules based on workload, cost, or model capabilities.
- Open source code: Access, modify, and extend the platform.
- Security and compliance: Keep data within your environment.
How to Set Up an Open Source AI Gateway
Setting up involves choosing your deployment method, connecting model providers, and configuring routing rules.
Deployment Options
- Python SDK
- Docker image (
osr serve) - Hosted platform
Connecting Model Providers
Configure your provider endpoints and credentials in the platform dashboard or SDK.
# Example: configuring a provider
from adapters.openai_compat import OpenAICompatClient
client = OpenAICompatClient(api_key='your-api-key')
Defining Routing Rules
Set rules based on workload, model capabilities, or cost.
Comparing Open Source AI Gateway with Proprietary Solutions
| Feature | Open Source Gateway | Proprietary Gateway |
|---|---|---|
| Deployment | Self-hosted | Hosted |
| Customization |
