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 session
- Preserves all context (files, code, decisions)
- Switches the provider seamlessly
- Continues 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
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 session
- No config files modified - all changes via environment variables
- Crash-safe - no cleanup needed if the session crashes
- Multiple 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