Mistral AI Migration Deep Dive

Table of Contents

• Overview
• Prerequisites
• Instructions
• Output
• Error Handling
• Examples
• Resources

Overview

Comprehensive guide for migrating to Mistral AI from other providers (OpenAI, Anthropic) or performing major version upgrades. Covers assessment, adapter pattern, feature-flag rollout, model mapping, validation testing, and rollback.

Prerequisites

• Current system documentation
• Mistral AI SDK installed
• Feature flag infrastructure
• Rollback strategy tested

Migration Types

Type	Complexity	Duration	Risk
Fresh install	Low	Days	Low
OpenAI to Mistral	Medium	Weeks	Medium
Multi-provider	Medium	Weeks	Medium
Full replatform	High	Months	High

Instructions

Step 1: Pre-Migration Assessment

Audit current AI code: find all files with AI imports, count integration points (chat completions, embeddings, function calling, streaming). Detect current provider and estimate effort (>10 points = high, >3 = medium, else low).

Step 2: Create Provider-Agnostic Adapter

Define AIAdapter interface with chat(), chatStream(), and embed() methods. Use Message, ChatOptions, and ChatResponse types to abstract away provider differences.

Step 3: Implement OpenAI Adapter

Build OpenAIAdapter implementing AIAdapter that wraps openai SDK. Maps OpenAI-specific fields (prompt_tokens, completion_tokens) to normalized types.

Step 4: Implement Mistral Adapter

Build MistralAdapter implementing AIAdapter that wraps @mistralai/mistralai SDK. Maps Mistral-specific fields (promptTokens, completionTokens) to normalized types.

Step 5: Feature Flag Migration

Create createAIAdapter() factory using MISTRAL_ROLLOUT_PERCENT env var. Random percentage check routes traffic to Mistral or OpenAI adapter.

Step 6: Gradual Rollout

Phase rollout: 0% (validation) -> 5% (canary) -> 25% -> 50% -> 100%. Monitor errors and latency at each phase for 24-48 hours before advancing.

Step 7: Model Mapping

Map models: gpt-3.5-turbo -> mistral-small-latest, gpt-4/gpt-4-turbo -> mistral-large-latest, text-embedding-ada-002 -> mistral-embed (1024 dimensions).

Step 8: Validation Testing

Run A/B comparison tests with identical prompts at temperature=0. Verify both providers return non-empty content. Log results for manual quality review.

Step 9: Rollback Plan

Set MISTRAL_ROLLOUT_PERCENT=0 to immediately route all traffic back to OpenAI. Verify health and notify team.

Output

• Migration assessment with effort estimation
• Provider-agnostic adapter interface
• OpenAI and Mistral adapter implementations
• Feature-flag controlled gradual rollout
• Model mapping configuration
• A/B validation test suite
• Rollback procedure

Error Handling

Issue	Cause	Solution
Different output format	API differences	Normalize in adapter
Missing feature	Not supported by Mistral	Implement fallback
Performance difference	Model characteristics	Adjust timeouts
Cost increase	Token differences	Monitor and optimize

Examples

Quick A/B Comparison

const [openaiResponse, mistralResponse] = await Promise.all([
  openaiAdapter.chat(messages, { temperature: 0 }),
  mistralAdapter.chat(messages, { temperature: 0 }),
]);
console.log('OpenAI tokens:', openaiResponse.usage);
console.log('Mistral tokens:', mistralResponse.usage);

See detailed implementation for advanced patterns.

Resources

• Mistral AI Documentation
• Strangler Fig Pattern
• Feature Flags Best Practices