Gen AI has reshaped how enterprises think about automation, customer experiences, and productivity. For many organizations, OpenAI was the platform that made this experimentation possible. When projects were small and stakes were low, sticking with familiar tools worked. As businesses begin to scale, the cracks start to show. Costs escalate, often unpredictably. Performance struggles to keep up with demand. Customizing solutions to fit unique challenges turns into a balancing act, and concerns over security and compliance become constant boardroom topics. That is why many enterprises have started thinking about how to migrate to Bedrock.    

These are the kind of issues that stall innovation and put critical projects at risk. The real question facing enterprise teams today isn’t whether Gen AI is deliver results, but whether their current stack can enable what the business needs next.  

How not to migrate to Bedrock?

Anyone who’s tried “lift and shift” migrations knows the pitfalls.  Before we tell you the right way to perform a migration from OpenAI to Bedrock, here are a few things that lead to a failed migration:

• Swapping OpenAI API calls for Bedrock’s seems simple, but it trades one vendor lock-in for another.

• Simply copying prompts from OpenAI to Bedrock mediated models ignores the unique conversational styles each model expects.

• Embeddings, that are critical to search and retrieval, can break or degrade, leading to silent quality issues.

• Manual quality checks miss subtle, but important, regressions in behavior or instruction-following.

• Big-bang deployments can create all-hands fire drills, with rollback only a hope away.

In these situations, you end up with a fragile brittle system that is more complex than before with unpredictable user outcomes. It is a smarter decision to migrate to Bedrock with the right engineering practices.

Migrate to Bedrock with GoML’s Unified Migration framework

At GoML, we believe migration should be strategic and future-proof. Here’s how our Unified Migration Framework, built for enterprise Gen AI at scale, makes it possible to migrate to Bedrock:

1. Unified model interface

Your application code calls our abstraction layer, not Bedrock APIs directly. This means you can switch to other models in the future (Anthropic, Cohere, Llama, you name it) with just a config update. You’re not trading one lock-in for another, you’re building flexibility.

2. Adaptive prompting engine

Plain “copy-paste” prompts won’t cut it. Our Adaptive Prompting Engine automatically transforms prompt structures and styles to match the best practices of each new model on Bedrock. That way, you maintain (or improve) performance, tone and reliability.

3. Seamless embeddings migration

We systematically re-index your data using a Bedrock-compatible model and build out a parallel vector store. This ensures your search and retrieval logic remains solid, or even improves, with zero silent failures.

4. Data-driven quality validation

We run your new Bedrock-powered system side-by-side against your old OpenAI system. Our ‘Evaluation Harness’ collects real-time metrics, if results aren’t perfect, the data helps us fine-tune prompts and fix issues before they hit production.

5. Zero downtime + phased rollout

We use “shadow mode” and canary releases to introduce changes gradually. You get a smooth, controlled rollout minus the big bangs and service interruptions, with user experience at top priority.

Best practices when you migrate to Bedrock

Here’s how the migration process unfolds, putting your goals whether it is continuity, speed and outcomes at the center so you can migrate to Bedrock:

Step 1: Discovery and qualification

The migration starts with full transparency. This phase is less about tools and more about clarity:

• Catalog every OpenAI integration, across business units, apps, and shadow IT.

• Assemble a migration taskforce: Align stakeholders who know the business value, data risks, compliance needs, and tech landscape.

• Map out why you’re migrating now. Typical triggers: ongoing operational pain, cost spikes, scaling issues, or upcoming audit requirements.

• Quantify what "success" means - Is it saving costs? Regulatory approval? Faster delivery to customers?

• Score risks, from user experience to critical workflow dependencies.

Output: A business-aligned migration charter, including clear app inventory, knowledge of true risk, and a shared, measurable success definition.

Step 2: Assessment and planning

Now, move into system-level analysis:

• Break down your existing tech stack by application, noting every OpenAI dependency.

• Gather data: latency, pricing, error rates and user stories, so that you can benchmark the “before”.

• Check compliance, privacy and security constraints. Identify where extra controls will be needed in Bedrock.

• Evaluate prompt complexity and embedding/vector usage. Will you simply swap endpoints, or do you need to adjust your data and business logic for Bedrock?

• Develop a plan and a detailed timeline, with budget and staffing estimates.

Output: A full migration playbook , approved by tech, security and line-of-business leaders - with risks, dependencies and mitigation mapped out.

Step 3: Pilot and model evaluation

This is the proving ground, where many migrations are derailed if handled superficially:

• Establish a fully isolated Bedrock test environment with the same controls you use in production.

• Replay actual business-critical user flows, prompts and search queries from OpenAI into Bedrock equivalents.

• Measure quality (response accuracy, reliability), integration smoothness, and cost using both automated tools and real user validation.

• Refine prompts and fine-tune model selection, based on differences in how Bedrock models handle instructions, context, and output.

• If relevant, run embedding comparisons. Don’t let search or recommender quality slip through unnoticed.

Output: Evidence-based “green light” for continuing with documented cost, performance and quality metrics, plus lessons learned for rollout.

Step 4: Migration execution

Here’s where GoML’s philosophy stands out, as detailed in the migration framework presentation:

• Use a unified model adapter (not just a naive endpoint switch). This future-proofs your stack and makes future model swaps far easier.

• Activate “shadow mode” first: run OpenAI and Bedrock systems in parallel on real data, but only expose one output to customers.

• Roll out gradually with canary releases: start with a subset of users or business flows, then expand as confidence in Bedrock rises.

• Refactor embeddings, retrain models, and migrate data as needed, using runbooks for failover and not an “all or nothing” risk possibility.

• Set up real-time monitoring, error tracking, and instant rollback procedures.

Output: A robust, observable migration to Bedrock with real production traffic, validated quality and no break in service.

Step 5: Optimization and continuous improvement

Don’t treat migration as “done at launch.” From GoML’s framework best-in-class teams always:

• Continuously benchmark new models or Bedrock features.

• Re-optimize for cost, latency, or specialized model use cases.

• Automate reporting for compliance, SLA tracking, and usage auditing.

Output: An AI estate that’s cost-effective, reliable, fully observable, and ready to expand, all while aligning with long-term business and technical strategy.

Check out our previous blogs for more in-depth insights on why you must consider an OpenAI migration.

What's included when you migrate to Bedrock with GoML?

Choosing GoML as your partner means you benefit from our standardized, yet tailored process to migrate to Bedrock:

• Migration in as little as 2 weeks, complete with codebase updates, hosting on Amazon Bedrock, and optimized prompts.

• No hidden costs for migration. Our recent track record speaks for itself, with 12 successful migrations in the first half of 2025.

• Proven frameworks and automation deliver a Streamlit UI for demonstration and feedback, robust documentation for knowledge transfer and a data-driven approach you can verify every step of the way.

• Risk-free, value-driven results. Not only do you minimize disruption, but you also maximize your return on each dollar invested in Gen AI.

Why migrate to Bedrock now?

• Model freedom: Access top models (Anthropic, Llama, Cohere and many more) in a single, unified API.

• Cost and performance: Bedrock offers best-in-class pricing and performance, especially with innovations like Amazon Nova, which costs up to 68% less for certain workloads compared to GPT-4o.

• Enterprise security: Full compliance (SOC 2, HIPAA, FedRAMP), data privacy, and robust guardrails built-in.

• Easy integrations: Built to plug into your AWS-native tools, with managed infrastructure, streamlined operations, and full support from AWS and GoML teams.

If you want deeper comparative metrics, or breakdowns on model costs and performance, check out our blog on a detailed cost breakdown of switching to AWS.

GoML and OpenAI Migration SCA

GoML is an AWS Gen AI Competency Launch Partner, helping global enterprises modernize their AI systems safely and efficiently.  

We've just signed a Strategic Collaboration Agreement for Gen AI migration with AWS, which means your customers can switch to AWS with our Unified Migration Framework.  

With zero IT investment.  

Our fully funded migrations take cost off the table as a barrier to migration for your clients. As a Gen AI competency launch partner, we take these migrating customers from assessment to “production-ready” in two weeks. Customers like Mariana, Doppelio, and Ojje have gone live with successful deployments — including clinical note generation at scale, an enterprise-grade support agent, and interactive content creation.

What are the next steps to migrate to Bedrock?

Every enterprise’s journey will look a little different. The right partner can de-risk the process and help you unlock the value of modern Gen AI, without disruption, technical debt or hidden surprises.

Schedule an executive AI briefing and let’s plan your migration!