Switching Between Providers

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 Opus (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

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 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

1. Configure Multiple Providers

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

# Default: Opus (highest tier)
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

​Why Switch Providers?

​Avoid Rate Limits

​Optimize Costs

​Leverage Different Models

​Using —resume

​Basic Resume

​Resume with Different Tier

​Resume with Different Provider

​Resume with Custom Model

​Session Continuity

​Setting a Default Provider

​Setting Default with Custom Model

​Clearing the Default

​Overriding the Default

​Session Isolation

​Terminal Isolation

​Auto-Cleanup on Exit

​Process Safety

​Common Switching Patterns

​Pattern 1: Rate Limit Recovery

​Pattern 2: Cost Optimization

​Pattern 3: Model Experimentation

​Pattern 4: Development Workflow

​Provider-Specific Considerations

​Local Providers (Ollama, LM Studio)

​Cloud Providers (AWS, Vertex, Anthropic)

​Vercel AI Gateway

​Tips for Effective Switching

​1. Configure Multiple Providers

​2. Use Tier Flags for Cost Control

​3. Set Defaults for Common Workflows

​4. Monitor Usage and Costs

​5. Use Local for Development

​Troubleshooting

​Resume Not Working

​Provider Not Responding

​Model Not Available

​Next Steps

Provider Overview

Local Providers

Cloud Providers

Why Switch Providers?

Avoid Rate Limits

Optimize Costs

Leverage Different Models

Using —resume

Basic Resume

Resume with Different Tier

Resume with Different Provider

Resume with Custom Model

Session Continuity

Setting a Default Provider

Setting Default with Custom Model

Clearing the Default

Overriding the Default

Session Isolation

Terminal Isolation

Auto-Cleanup on Exit

Process Safety

Common Switching Patterns

Pattern 1: Rate Limit Recovery

Pattern 2: Cost Optimization

Pattern 3: Model Experimentation

Pattern 4: Development Workflow

Provider-Specific Considerations

Local Providers (Ollama, LM Studio)

Cloud Providers (AWS, Vertex, Anthropic)

Vercel AI Gateway

Tips for Effective Switching

1. Configure Multiple Providers

2. Use Tier Flags for Cost Control

3. Set Defaults for Common Workflows

4. Monitor Usage and Costs

5. Use Local for Development

Troubleshooting

Resume Not Working

Provider Not Responding

Model Not Available

Next Steps