Why Your AI Agent Should Never Depend on a Single LLM Provider
Never Depend on a Single LLM Provider
If your AI agent uses one LLM provider and that provider has an outage, your agent is dead. Not degraded - completely non-functional. This is a hard dependency that most teams do not plan for until it breaks.
The Real Cost of Single-Provider Lock-In
LLM providers have outages. Rate limits change without notice. Pricing increases happen quarterly. Models get deprecated. When any of these hit and you have no fallback, your options are to wait or scramble.
The pattern repeats across the industry:
- Provider goes down for 4 hours
- Every agent built on that provider stops
- Teams realize they have zero fallback path
- Emergency migration starts under pressure
Building Multi-Provider Resilience
The fix is straightforward but requires planning from day one:
- Abstract the LLM call behind a simple interface - model name, prompt in, response out
- Maintain configs for at least two providers that can handle your workload
- Implement automatic fallback with a circuit breaker pattern
- Test the fallback path regularly, not just when production breaks
This does not mean running two providers simultaneously. It means having a tested backup that activates automatically.
What to Watch For
Different providers have different strengths. Your primary might handle complex reasoning well while your fallback is better at simple tasks. Design your agent to degrade gracefully - maybe the fallback handles 80% of requests correctly instead of 98%.
That 80% is infinitely better than 0% during an outage.
The Practical Minimum
At minimum, keep one cloud provider and one local model as fallback. Tools like Ollama make it possible to run capable models locally with zero API dependency. The local model might be slower or less capable, but it keeps your agent running.
Fazm is an open source macOS AI agent. Open source on GitHub.