A Practical Guide to Migrating GPT Apps Across Azure, AWS, and GCP
TL;DR — Most LLM migrations are not caused by model performance. They happen because of data residency laws, enterprise deployment requirements, or cloud standardisation decisions. This guide helps you narrow the search space to the right replacement candidates — not replace real testing.
Why Companies Actually Migrate LLMs
Engineers rarely wake up and decide to migrate their AI stack. Most migrations are triggered by business constraints, not technical ones.
Common Migration Scenarios
1️⃣ Expanding into Regions With Data Residency Laws
Your product currently runs on Azure OpenAI (gpt-4o-mini), but a new region requires:
- EU sovereign cloud
- Local data processing
- Provider-specific compliance certifications
You may need to move to AWS Bedrock, Google Vertex AI, or Azure AI Foundry with open models — even though your application logic stays identical.
2️⃣ Enterprise Customers Want AI Inside Their Environment
This is extremely common in B2B SaaS. Enterprise customers often require:
- Private cloud deployment
- VPC-only access
- On-prem inference
- Sovereign cloud environments
Your API-based model suddenly needs to become Llama, Qwen, DeepSeek, or Mistral — running inside their infrastructure.
3️⃣ Corporate Cloud Standardisation
A classic scenario:
- AI team → Azure
- Platform team → AWS
Leadership decides: "All workloads must run on AWS."
Now your team must translate gpt-4o-mini into an AWS Bedrock equivalent — and the model catalog doesn't make that obvious.
The Problem: Model Names Don't Translate
Each vendor uses completely different naming schemes. There is no official cross-provider model map.
| Vendor | Entry Model | Mid Model | Reasoning Model |
|---|---|---|---|
| OpenAI | gpt-4o-mini |
gpt-4o |
o-series |
| Anthropic | Haiku | Sonnet | Opus |
| Flash | Pro | Pro reasoning | |
| Meta | Scout | Maverick | Large variants |
| Qwen | Small (7B–14B) | 72B–110B | 235B Thinking |
| DeepSeek | V3 (non-thinking) | V3 standard | R1 (reasoning) |
Common mistakes teams make
- ❌ Picking the cheapest model in the new catalog
- ❌ Picking the newest model by release date
- ❌ Picking the highest benchmark model regardless of tier
All three approaches can silently break production behaviour.
The Tier Model That Actually Works
Instead of comparing names, compare capability tiers. Every major provider follows the same five-tier structure.
| Tier | Typical Use Cases | Latency | Relative Cost |
|---|---|---|---|
| Mini / Flash / Small | Chatbots, RAG, classification | Fastest | Lowest |
| Standard / Mid | Assistants, summarisation, coding | Medium | Moderate |
| Reasoning / Pro | Agents, planning, complex Q&A | Slower | Higher |
| Frontier / Flagship | Research workloads, safety-critical | Slowest | Highest |
Once you know your current model's tier, finding candidates becomes systematic — not guesswork.
Tier 1 — Replacing gpt-4o-mini
Typical workloads: chat assistants, RAG pipelines, tool calling, lightweight coding
Candidates by cloud
| Cloud | Replacement Candidates |
|---|---|
| Azure AI Foundry | Llama 4 Scout, Qwen3-8B/14B, DeepSeek V3, Claude Haiku |
| AWS Bedrock | Claude Haiku, Mistral Small |
| GCP Vertex AI | Gemini Flash-Lite, Gemini Flash |
Behaviour differences at this tier
| Model | Strengths | Watch out for |
|---|---|---|
| Claude Haiku | Reliable, low hallucination rate | ~7× more expensive than gpt-4o-mini
|
| Gemini Flash | Extremely fast, 1M token context | GCP-only; not available on Azure |
| Llama 4 Scout | Open-weight, 10M token context, Azure-hosted | Not a pure reasoning-tuned model |
| DeepSeek V3 | Unusually strong reasoning (MMLU-Pro ~75.9, GPQA ~59.1) for this price tier | Direct API or Azure Foundry; no native AWS/GCP |
| Qwen3-8B/14B | Strong multilingual + math, Apache 2.0 | Smaller context than Gemini/Llama |
Tier 2 — Replacing gpt-4o
Typical workloads: document summarisation, coding assistance, enterprise chat assistants
Candidates by cloud
| Cloud | Replacement Candidates |
|---|---|
| Azure AI Foundry | Claude Sonnet, Llama 4 Maverick, DeepSeek V3 |
| AWS Bedrock | Claude Sonnet, Mistral Medium |
| GCP Vertex AI | Gemini Flash, Gemini 2.5 Pro |
Benchmark reference (reasoning quality)
| Model | MMLU | MMLU-Pro | GPQA-Diamond | Notes |
|---|---|---|---|---|
| Claude Sonnet 4.x | ~88+ | Strong | Competitive | Best SWE-bench coding score at this tier |
| Llama 4 Maverick | ~85+ | ~80.5 | ~69.8 | Beats GPT-4o on Meta's coding benchmarks |
| DeepSeek V3 | 88.5 | 75.9–81.2 | 59.1–68.4 | Frontier-class at mid-tier pricing |
| Gemini Flash (GCP) | Strong | Competitive | ~78% SWE-bench | GCP-only; fastest in this tier |
DeepSeek V3 on Azure often outperforms
gpt-4oon raw reasoning benchmarks at significantly lower cost. Treat it as a tier upgrade, not just a replacement.
Tier 3 — Replacing Reasoning Models (gpt-4.1 / gpt-5 / o-series)
Typical workloads: agent systems, research workflows, complex multi-step reasoning
Candidates by cloud
| Cloud | Replacement Candidates |
|---|---|
| Azure AI Foundry | Claude Opus, DeepSeek R1, Qwen3-235B Thinking (via Foundry) |
| AWS Bedrock | Claude Opus |
| GCP Vertex AI | Gemini 2.5 Pro, Gemini 3.1 Pro |
Reasoning benchmark reference (HLE + advanced)
| Model | HLE Score | MMLU-Pro | GPQA-Diamond | AIME-25 |
|---|---|---|---|---|
| Claude Opus 4.x | Top-tier (Anthropic reports #1 on HLE leaderboard) | ~90+ | Strong | Strong |
| Qwen3-235B-A22B Thinking | ~18% (one of few published open-weight HLE scores) | ~84.4% | ~81% | ~92% |
| DeepSeek R1 | Not widely published | ~81.2 | ~68.4 | Strong |
| Gemini 2.5 / 3.1 Pro | Competitive | Strong | Strong | Strong |
Qwen3-235B-A22B Thinking is currently one of the few open-weight models with a published Humanity's Last Exam score (~18%) — putting it in the same conversation as frontier closed models for reasoning-heavy tasks.
Architecture Pattern: Make LLMs Replaceable
The biggest mistake teams make is hard-coding a model into their architecture.
❌ The fragile pattern
Application → GPT-4o-mini (hardcoded)
Any migration requires touching application logic, service config, and prompt templates.
✅ The replaceable pattern
Benefits:
- Vendor independence — swap providers via config
- Easier model upgrades without app rewrites
- Enables cost-optimised routing
Cost Optimisation: Intelligent Request Routing
Many production AI systems at scale route requests by task complexity rather than using one model for everything.
This pattern can reduce LLM costs by 60–90% in workloads with a mix of simple and complex requests.
Prompt Regression Testing — Non-Negotiable
Before committing to any model swap, run prompt regression tests on your real production prompts.
# Simple regression harness
test_prompts = load_production_samples(n=200)
results = []
for prompt in test_prompts:
output_a = old_model.invoke(prompt)
output_b = new_model.invoke(prompt)
results.append({
"prompt": prompt,
"score_a": evaluate(output_a), # correctness, format, hallucination
"score_b": evaluate(output_b),
"regression": score_b < score_a * 0.95
})
regressions = [r for r in results if r["regression"]]
print(f"Regression rate: {len(regressions)/len(results)*100:.1f}%")
Check for:
- Correctness — does the answer change?
- Format compliance — does it still follow your output structure?
- Hallucination rate — does it fabricate facts?
- Latency — does it still meet your SLA?
⚠️ Important Disclaimer
The mappings in this guide are a starting point, not guaranteed drop-in replacements.
Models in the same tier can have meaningfully different behaviour on:
- Your specific domain vocabulary
- Your prompt style
- Edge cases in your data
Every migration must include:
- Prompt regression testing on real data
- Human evaluation of sampled outputs
- Shadow traffic validation (run both models in parallel, compare outputs)
- Gradual rollout (5% → 25% → 100%)
Recommended Migration Workflow
Do not skip shadow traffic. It catches subtle regressions that prompt tests miss.
📋 LLM Migration Cheat Sheet
Mini Tier (gpt-4o-mini equivalent)
- Azure → Llama 4 Scout, Qwen3-8B/14B, DeepSeek V3, Claude Haiku
- AWS → Claude Haiku, Mistral Small
- GCP → Gemini Flash-Lite, Gemini Flash
Standard Tier (gpt-4o equivalent)
- Azure → Claude Sonnet, Llama 4 Maverick, DeepSeek V3
- AWS → Claude Sonnet, Mistral Medium
- GCP → Gemini Flash, Gemini 2.5 Pro
Reasoning Tier (o-series / gpt-5 equivalent)
- Azure → Claude Opus, DeepSeek R1, Qwen3-235B Thinking
- AWS → Claude Opus
- GCP → Gemini 2.5 Pro, Gemini 3.1 Pro
Final Takeaway
The LLM ecosystem is evolving too fast to depend on a single provider.
Design your systems so that:
GPT → Claude → Gemini → DeepSeek
…is a configuration change, not a system rewrite.
When that happens, migrations become boring infrastructure work.
And boring infrastructure is exactly what you want in production.
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Tags: #ai #llm #cloud #azure #systemdesign
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