Gen AI adoption got its big start with OpenAI, and for a while, most teams stuck with what was familiar. However, as AI workloads hit production and costs started to climb, many organizations began asking tough questions: Are we overpaying for the convenience? And most of all, what really happens when you make the move from OpenAI to smarter solutions like AWS?  

As teams go deeper into Gen AI, they’re starting to find stronger, more focused partners on the other side.  

Why consider a move from OpenAI to AWS?

Most teams start the gen AI journey hoping for speed and innovation. Things work great in the prototype stage. Although, problems show up as soon as the workload scales:

• Costs rise without warning, especially with usage-based models.

• Performance hits a ceiling, with slow response times as data flows increase.

• Customization is limited, making it tough to tailor solutions for unique business needs.

• Security and compliance concerns linger, especially as data moves outside the organization’s core infrastructure.

This intensifies when your budget owner asks for a detailed ROI breakdown, or when IT wants more control over governance. It becomes clear that the status quo may not scale much longer.

What is the real cost difference between OpenAI and AWS Nova Pro?

Migration is primarily about the bottom line, not just vendor choice. Here are some interesting details for you to consider:

• AWS Nova Pro costs up to 68% less than GPT-4o for the same 20 million token workload.

• You get more than double the context window with Nova Pro (up to 300,000 tokens per request compared to 128,000 on OpenAI’s top-tier model).

• Token output speed (measured in tokens per second) is nearly twice as fast with Nova Pro, which matters for real-time applications and customer experiences.

Simply put, if you’re running high-volume use cases, these cost and performance differences add up. Teams moving to AWS report being able to scale their gen AI work further without the unpredictable billing that comes with many usage-based models.

What are the enterprise benefits of moving from OpenAI to AWS?

A lot of the feedback from recent OpenAI to AWS migrations comes back to flexibility and choice. AWS offers a broad menu of foundation models through a single API. Teams can test-drive Anthropic, Llama, Cohere and others, switching easily as needed. You aren’t forced into a single model or approach.

This “model-agnostic” setup is especially important when experimenting or when your use case changes rapidly. This helps when you’re working with Amazon Bedrock as teams are running AI, data and applications in the same secure environment as the rest of their AWS infrastructure, keeping maintenance (and surprises) to a minimum.

Which is better for security and compliance: OpenAI or AWS?

Enterprise buyers rarely migrate for cost alone. Security and data governance drive as many decisions as billing, especially where AI intersects with sensitive or regulated information.

AWS systems offer full enterprise compliance out of the box. Everything from SOC 2, HIPAA eligibility, FedRAMP and more. Additionally, your data isn’t stored or used to retrain the underlying foundation models. Built-in guardrails and granular controls keep security teams happy. This means less red tape and faster deployment for business stakeholders.

How does GoML simplify your OpenAI to AWS migration while staying secure?

Enterprises considering a shift from OpenAI to AWS in general have a few goals in mind: lowering costs, gaining more control and securing sensitive data. However, while AWS provides essential tools and options, moving complex AI workloads isn’t a plug-and-play process. That’s where a partner with real-world migration expertise can make the difference. Here’s how GoML helps you make the move with confidence:

Migration planning that fits your business

Every organization’s infrastructure is unique. GoML starts with a technical and business assessment, mapping out how your current OpenAI-based workloads translate to AWS’s platform, which includes Bedrock, Nova Pro and other integrated foundation models. You get a plan that fits your timeline and technical profile, as opposed to a generic template.

Hands-on technical delivery

Successful migrations depend on technical depth, especially with advanced gen AI use cases. GoML manages all aspects, right from setting up endpoints, mapping data flows, supporting fine-tuning to ensuring minimal downtime, all while meeting your security and compliance requirements.

Security and compliance

With GoML, your migration process aligns fully with enterprise compliance standards (SOC2, HIPAA, FedRAMP, and more). We work within your governance framework, ensuring data location, retention and privacy policies are never compromised.

Ongoing optimization

‍Migration isn’t a one-off event. After go-live, GoML provides continuous support, helping you tune models, troubleshoot issues and adapt as AWS’s AI offerings evolve. This helps you maintain performance and cost advantages without extra overhead on your team.

What does OpenAI to AWS migration really take?  

Not all migrations are created equal, and AWS recognizes that. The process is broken down into a few flexible paths, depending on your complexity and goals:

• Simple API endpoint switching (1–4 weeks): If you’re using simple hosted endpoints, you can typically move workloads to AWS with minor technical tweaks. No deep engineering required.

• Advanced workload migration (1–3 months): Heavier lifts, custom model import, fine-tuning, and proprietary application logic. All these aspects require more time but add immense value in control and customization.

• Full-stack application migration (4–6 months): For organizations using things like Retrieval Augmented Generation (RAG), AI agents or unique compliance requirements, the journey is more involved but still clearly mapped out and supported each step of the way.

“We’ve worked with every major model out there. The one that wins long-term is the one that gets cost, scale, security and compliance right,” says Rishabh Sood, Founder, GoML.

The underlying migration roadmap by GoML isn’t complicated, but it is thorough:  

1. Discovery and qualification: Identify blockers, pain points  

2. Assessment and planning: Born-in Gen AI consultancy companies like GoML ensure comprehensive assessment matched to your business needs

3. Evaluation and proof: Every model is benchmarked in real-world scenarios, comparing costs and performance on your own terms.  

4. Migration execution: Move your workloads, validate outcomes, and transition commercial/legal agreements.

5. Ongoing collaboration: After deployment, having support for further performance tweaking and refinement

Here is a detailed breakdown on when you should consider an OpenAI migration.

What do teams say after migrating from OpenAI to AWS?

Let’s look at recent migration stories from teams who worked with GoML:

Mariana: Transforming clinical note generation

Mariana, a leader in healthcare automation, migrated its clinical note generation workflows from OpenAI to AWS Bedrock with GoML’s help. Here’s what changed:

• 50% improvement in the accuracy of structured clinical notes.

• 95% reduction in manual validation effort, thanks to automated schema checks.

• 80% prompt consistency using predefined templates across specialties.

• Fully automated prompt chaining, strong compliance posture and enterprise-ready monitoring. All this was delivered in just four weeks.

Takeaways included greater accuracy, less manual work and stronger controls in a highly regulated field. Read the full case study on how GoML transformed clinical note generation for Mariana.

Doppelio: Enterprise AI chat agent at scale

Doppelio delivers enterprise test automation. After migrating their multi-tenant chat agent from OpenAI to AWS, they saw:

• 67% improvement in information extraction from structured/unstructured docs.

• 99.95% uptime even with 1,000+ concurrent sessions, solving scale issues from the old setup.

• 50% higher accuracy on domain-specific responses.

• Better latency, tighter data segregation, and seamless cost optimization.

Takeaways included smarter, faster and much more reliable security for every tenant. Read the full case study on how GoML enabled enterprise AI chat agents at scale for Doppelio.

How to decide on your next move?

Re-evaluating your gen AI infrastructure is more than just a cost decision, it's a strategic one which could make or break your margins. The right move depends on your use cases, data sensitivity, scalability needs and long-term flexibility. For many enterprises, the shift from a single-model API like OpenAI to a multi-model, AWS-native architecture unlocks significant gains like lower TCO, better control, and infrastructure built to scale with the business.

If migration is on your roadmap, now is the right time. This is because the tooling, funding options, and support ecosystem have matured meaningfully. At GoML, we’ve helped leading teams navigate this transition with speed, clarity, and impact.

Reach out for an executive AI briefing before you migrate.