by Vitaly NikitinAI models are not created equal. Some are better at code generation. Others excel at analysis. Some are faster, some are cheaper, and all of them occasionally have outages.
Betting your entire development pipeline on a single AI provider is a strategic risk. That’s why Forge was built from day one with multi-provider support.
The Problem with Single-Provider AI
When your pipeline depends on one provider:
- Outages block your entire workflow — when the API is down, development stops
- You’re locked into pricing changes — no leverage when there’s no alternative
- You miss model improvements — new breakthroughs happen across providers, not just one
- Different tasks need different models — a code review agent has different needs than a requirements analysis agent
How Forge Routes AI Requests
Forge’s AI Gateway handles routing across five providers:
- Anthropic (Claude)
- OpenAI (GPT)
- Google (Gemini)
- Azure AI
- AWS Bedrock
Smart Provider Selection
You define priority order. Forge tracks health metrics and response times per provider, then adjusts weights automatically. If a provider starts returning errors or slowing down, traffic shifts to the next in line.
Automatic Failover
When a request fails, Forge retries on the next provider in the priority chain. The agent doesn’t know or care which provider handled its request — it just gets a response.
Worker Pinning
For cases where consistency matters more than resilience (e.g., you want the same model handling an entire pipeline run), Forge supports pinning a worker to a specific provider. Session affinity ensures all turns in a session go to the same model.
Cost Optimization
Different agents have different needs. A PM agent analyzing requirements can often use a faster, cheaper model. A Developer agent writing production code benefits from the most capable model available. Forge lets you configure provider preferences per agent role.
The Result
A development pipeline that doesn’t stop when one provider has issues. Access to the best model for each task. And the flexibility to adapt as the AI landscape evolves.
This is infrastructure thinking applied to AI — and it’s how we believe AI-powered development tools should be built.