Andi AIRun makes it easy to switch between providers mid-task, allowing you to work around rate limits, optimize costs, and leverage different models.
Why Switch Providers? Avoid Rate Limits Claude Pro has usage limits. When you hit a rate limit, switch to an API provider and continue immediately:
# Working with Claude Pro, hit rate limit ai # "Rate limit exceeded. Try again in 4 hours 23 minutes." # Immediately continue with AWS ai --aws --resume
Optimize Costs Switch to cheaper models for simple tasks:
# Use Haiku for quick edits (faster, cheaper) ai --aws --haiku --resume # Use Ollama for free local inference ai --ollama --resume
Leverage Different Models Switch to more powerful models for complex reasoning:
# Switch to Opus for complex refactoring ai --aws --opus --resume # Try a different model entirely ai --vercel --model xai/grok-code-fast-1 --resume
Using —resume The --resume flag lets you pick up a previous conversation exactly where you left off.
Basic Resume # Start with Claude Pro ai # Hit rate limit, switch to AWS ai --aws --resume
Resume with Different Tier # Working with Sonnet (default) ai --vertex # Switch to Haiku for speed ai --vertex --haiku --resume # Switch to Opus for complex reasoning ai --vertex --opus --resume
Resume with Different Provider # Start with AWS ai --aws # Switch to Vertex AI ai --vertex --resume # Switch to local Ollama (free!) ai --ollama --resume
Resume with Custom Model # Start with Claude Sonnet ai --vercel # Switch to xAI Grok ai --vercel --model xai/grok-code-fast-1 --resume
Session Continuity When you use --resume, Andi AIRun:
Loads the previous conversation from your most recent sessionPreserves all context (files, code, decisions)Switches the provider seamlesslyContinues the task without interruption
The conversation history is stored locally in ~/.ai-runner/sessions/, so resume works even after closing your terminal.
Setting a Default Provider Avoid typing the provider flag every time by setting a default:
# Set AWS Bedrock as default ai --aws --set-default # Now 'ai' uses AWS automatically ai ai --opus ai --haiku
Setting Default with Custom Model # Set Vercel with xAI Grok as default ai --vercel --model xai/grok-code-fast-1 --set-default # Now 'ai' uses xAI Grok automatically ai
Clearing the Default ai --clear-default # Now 'ai' uses Claude Pro (if logged in) or first configured provider ai
Overriding the Default # Set AWS as default ai --aws --set-default # Override for one session ai --vertex # Next session uses AWS again ai
Session Isolation All provider changes are session-scoped and automatically isolated :
Terminal Isolation # Terminal 1: Using LM Studio ai --lmstudio # Terminal 2: Using native Claude Pro (unaffected) claude # Terminal 3: Using AWS Bedrock ai --aws
Each terminal session is completely independent.
Auto-Cleanup on Exit ai --lmstudio # Session ends (Ctrl+C or naturally) # Original environment automatically restored # No stale state, no files modified
Process Safety
No global state - changes only affect the current terminal sessionNo config files modified - all changes via environment variablesCrash-safe - no cleanup needed if the session crashesMultiple sessions - run different providers simultaneously
Common Switching Patterns Pattern 1: Rate Limit Recovery # Hit rate limit ai # "Rate limit exceeded. Try again in 4 hours 23 minutes." # Option 1: Switch to API provider ai --aws --resume # Option 2: Switch to free local ai --ollama --resume # Option 3: Switch to different cloud ai --vertex --resume
Pattern 2: Cost Optimization # Start with powerful model for initial work ai --aws --opus # Switch to cheaper model for refinements ai --aws --haiku --resume # Switch to free local for final tweaks ai --ollama --resume
Pattern 3: Model Experimentation # Try Claude Sonnet first ai --apikey # Not satisfied? Try xAI Grok ai --vercel --model xai/grok-code-fast-1 --resume # Try OpenAI's coding model ai --vercel --model openai/gpt-5.2-codex --resume # Try local model ai --ollama --model qwen3-coder --resume
Pattern 4: Development Workflow # Planning phase: Use powerful model ai --aws --opus # Implementation: Use balanced model ai --aws --sonnet --resume # Testing/debugging: Use fast, cheap model ai --aws --haiku --resume # Refinement: Use free local ai --ollama --resume
Provider-Specific Considerations Local Providers (Ollama, LM Studio) Pros:
Free (no API costs)
No rate limits
Private (data stays local)
Fast (no network latency)
Cons:
Requires hardware (VRAM/RAM)
Model quality varies
Setup required
Best used for:
Cost-conscious development
Private/sensitive code
Frequent iterations
Learning and experimentation
Cloud Providers (AWS, Vertex, Anthropic) Pros:
Most powerful models
No hardware requirements
Always available
Latest model versions
Cons:
Pay per use
Rate limits (especially Claude Pro)
Network dependency
Data sent to provider
Best used for:
Complex reasoning
Large refactors
Production work
Critical tasks
Vercel AI Gateway Pros:
Access to 100+ models
Single API for all providers
Unified billing
Easy switching
Cons:
Pay per use
Network dependency
Rate limits vary by model
Best used for:
Multi-model workflows
Experimentation
Provider flexibility
Tips for Effective Switching Set up 2-3 providers in secrets.sh for maximum flexibility:
# Primary: Claude Pro (free tier) # Logged in with: claude login # Fallback 1: AWS Bedrock (pay-as-you-go) export AWS_PROFILE = "my-profile" export AWS_REGION = "us-west-2" # Fallback 2: Ollama (free local) # Just install and run: ollama serve
2. Use Tier Flags for Cost Control # Expensive: Opus for complex tasks ai --aws --opus complex-refactor.md # Balanced: Sonnet (default) for most work ai --aws task.md # Cheap: Haiku for simple edits ai --aws --haiku simple-fix.md
3. Set Defaults for Common Workflows # Set your most-used provider as default ai --aws --set-default # Clear when switching projects ai --clear-default
4. Monitor Usage and Costs Keep an eye on your API usage:
AWS: CloudWatch metrics
Google: Cloud Console
Anthropic: Console dashboard
Vercel: AI Gateway dashboard
5. Use Local for Development # Development: Use free local models ai --ollama # Production: Switch to cloud for reliability ai --aws --resume
Troubleshooting Resume Not Working # Check session history ls ~/.ai-runner/sessions/ # Resume last session explicitly ai --resume # Resume specific session ai --resume --session 2024-03-03-15-30-00
Provider Not Responding # Test provider configuration ai --aws --test # Switch to known-good provider ai --apikey --resume
Model Not Available # For local providers, pull/download first ollama pull qwen3-coder lms load openai/gpt-oss-20b # Then retry ai --ollama --model qwen3-coder --resume
Next Steps
Provider Overview Learn about the provider system
Local Providers Set up free local models
Cloud Providers Configure cloud APIs