The golden era of AI deployment for most enterprises began with the most obvious choice for the time- OpenAI. While a viable second option was once absent, as of 2026 choosing Amazon Bedrock for enterprise workloads is a total no-brainer. Migrating your workloads from OpenAI to Amazon Bedrock unifies inference under a proven AWS security perimeter. An AWS partner like GoML can complete Amazon Bedrock migrations in 4 – 6 weeks. GoML has completed more than 12 OpenAI to Amazon Bedrock migrations already in 2026.
Partnering with the right Amazon Bedrock migration specialist like GoML ensures your team navigates complex embedding re-indexing, captures available AWS co-funding and completes a production-ready cutover in just a few weeks, depending on complexity. In addition, you can unlock significant token cost savings through multi-model routing.
In this article, we detail everything you need to evaluate, compare, and select the best AWS partner for your OpenAI to Amazon Bedrock migration.
Assessing the best migration for you
Before evaluating partners, engineers at your org must accurately diagnose the structural complexity of their existing OpenAI deployment. Migration timelines vary from a couple of weeks to nearly half a year - and that duration depends on application architecture, state dependencies and underlying retrieval pipelines.
Classifying your workload into the correct operational tier helps one decipher a realistic delivery timeline + milestones.
Which migration do you have?
It’s usually the case that most teams put themselves in category one, and then someone asks whether the app does retrieval... and it turns out to be the second. So, plan for a full-stack migration until you find out otherwise.
The short version. Amazon Bedrock now hosts OpenAI's frontier models alongside Claude, Nova and 100+ others, at OpenAI's own list prices, billed through AWS. That includes GPT-6 Astra, OpenAI's newest and most capable model. So, migrating no longer means giving up GPT. It means moving the control plane into your AWS account and then picking models as per task. Most of the cost saving comes from right-sizing models, context, and token engineering. With GoML, the API layer is itself plug-and-play through our AI Matic Model Gateway.
If you want a cost picture for your own workload before you talk to anyone, GoML's BedrockROI calculator will run your token volumes.
Understanding OpenAI and Amazon Bedrock
What is OpenAI?
OpenAI is a frontier model provider, famously known for ChatGPT. OpenAI may also, in a short while, become one of the world’s most valuable companies. OpenAI's catalogue covers reasoning, text, media generation, voice and vector search. Frontier reasoning runs on GPT-6 Astra, GPT-5.6, covering deep analysis, agentic execution and automated software development. High-volume text and chat go to GPT-5.6 Terra, GPT-5.6 Luna and GPT-5.4. Image and video come from GPT-Image-2.5 Sunburst, -Image-2.5 Flare, DALL·E 3, and Sora 2. Open AI voice models like GPT-Realtime-2.1, GPT-Audio-1.5, and Whisper are great for voice pipelines. Retrieval and indexing use text-embedding-3-large and text-embedding-3-small.
Introduction to Amazon Bedrock
Bedrock is Amazon's flagship AI platform. What AWS did for storage on the cloud, Bedrock does for generative AI on the cloud. It gives every company the foundation for enterprise-grade AI capability. It works as a unified inference control plane: over 100foundation models from 18 providers behind one API, so engineering teams aren't locked to a single model provider.
Bedrock puts frontier reasoning, high-throughput language models, image generation and retrieval in one architecture. Anthropic Claude versions and Amazon Nova 2 can handle complex agentic logic. Amazon Nova Micro and Meta Llama 3.3 maximise cost-performance for high-volume routing. Amazon Nova Canvas and Stability AI power visual synthesis. Amazon Titan V2 and Cohere Embed anchor semantic vector retrieval.
Amazon Bedrock natively serves OpenAI’s newest flagship engines, as of September 2026. That includesGPT-6 Astra and the GPT-5.6 family directly through AWS endpoints, allowing your applications to use run the exact same neural architectures inside your cloud account.
Note: Moving from OpenAI to Bedrock doesn't have to mean dropping OpenAI models. Often it means keeping them and changing how it runs and how you can retain model flexibility while consolidating billing and building a more robust AI architecture.
Generative AI and its applications
Most OpenAI to Amazon Bedrock migration projects involve areas where ChatGPT is good but better model choices exist on Bedrock. That include everyday, high-volume workloads like document extraction, customer support triage, internal search, claims and invoice processing, or automated agents that string multiple model calls together.
Apart from use cases, we've seen workloads with high request volumes, low latency requirements, and enterprise data security needs are candidates. That specific mix is why a system that works fine as a prototype quickly breaks down when scaled into production without proper platform controls.
Migrating from OpenAI to Amazon Bedrock
For any or transitioning enterprise AI from experimental prototypes to mission-critical AI infrastructure requires thinking about long-term scalability, governance, financial controls, and operational flexibility.
Reasons to consider migration
Reports have consistently cited that a majority of generative AI prototypes never reach production and often have performance or accuracy problems. Our own experience has been different. But it is also factual that a lot of companies don't have enterprise-grade security controls for their agents and AI workloads.
Four reasons come up again and again in scoping calls.
Data governance: Bedrock puts inference behind IAM, VPC endpoints, and your secure AWS account. No new vendor review, no new data processing agreement, and nothing stored or used for training by the model provider.
Billing: A consolidated AWS bill for inference, compute, and storage saves substantial time on billing and verification. You can draw from committed spend and EDPs. If you have a large enterprise discount program, that alone can pay for the project.
Model choice: One API, many models. As models continue evolving, flexibility in architecture is vital to ensuring you can change models fast. Bedrock, and GoML’s AI Matic Model Gateway on top of Bedrock, enables plug and play model changes.
Frontier access: Astra on Bedrock is the clearest example. You get OpenAI's (and other providers like Anthropic’s) newest model inside your existing trusted AWS environment, with Zero Data Retention available for eligible API customers.
Running OpenAI models on Bedrock at the same prices won't cut your per-token cost. But it does cut procurement friction and consolidates billing.
Real savings also accrue from the built-in ability to route inference calls to multiple best-fit models from different providers. In my experience, we’re long past the phase where you automatically plug in the newest frontier model for every call. The best AI systems today squeeze more out of every token and rely on multiple models within the same system for different purposes. Routing, classification, and extraction don't need a frontier model, and, for example, Amazon Nova is great at them and cost a fraction of frontier pricing.
GoML has seen up to 85% cost reduction on migrated workloads where model right-sizing was part of the scope.
Challenges in OpenAI Migration
If you were to point your code at Bedrock, run a basic test - everything can look like it’s ready to go. However, later you’ll notice that user prompts gradually start drifting, search results turn to junk and token limits choke responses. Here are the core technical hurdles every engineering team faces during a typical migration.
Embeddings: If your app does retrieval, your vector store was built with an OpenAI embedding model. If you change the embedding model without re-indexing, Search starts returning irrelevant results. The answers still read well, they're just wrong. The safe path is to re-index into a parallel vector store and check retrieval quality before you cut over.
Prompts: Models respond differently to conversational structure and instruction style. Rebuilding prompts from GPT to Claude or Nova (or even within models from the same provider) is a critical part of every migration.
Missing equivalents: OpenAI's Realtime API, the Assistants API with persistent threads, and DALL·E image generation don't map directly onto Bedrock. If your app depends on those, be ready to make engineering decisions.
API swaps: Replacing OpenAI calls with Bedrock calls line for line leaves you as locked in as before, with new failure modes to learn. The point of moving is flexibility, and you only get that with an abstraction layer like GoML’s AI Matic Model Gateway.
Regions and rollout status: Model availability differs by AWS region. New models also arrive in stages. Astra started with a limited set of organisations and expanded over following days, and enterprise access is off by default until an admin enables it for the workspace. Check what your account can actually call before you design around it.
Manual quality checks: Some good old-fashioned software advice. Test, test, test. I suggest a measured side-by-side comparison against the old system, using your own inputs.
Steps to Migrate from OpenAI to Bedrock
When moving production LLMs to AWS Bedrock, the main technical traps are breaking API compatibility, silently degrading response quality, and managing AWS IAM and quota limits under load. Having engineered and executed this migration across our own production workloads, we developed this three-phase blueprint around the AI Matic Model Gateway. It acts as an in-VPC proxy that translates payloads on the fly, allowing you to validate parity against live traffic before cutting over a single route.
Phase 1: Assessment and Target Mapping
The migration begins with an end-to-end audit of your current stack, mapping every prompt template, model reference, token budget, and latency ceiling to equivalent foundation models on Amazon Bedrock. You can retain OpenAI endpoints through Bedrock when direct behavioral parity is non-negotiable or re-route high-throughput tasks like classification and structured parsing to cost-efficient options like Amazon Nova or Meta Llama.
During this initial stage, your engineers isolate a golden dataset from production traces to establish hard baselines for P95 latency, output format adherence, error rates, and cost per million tokens. Concurrently, the AI Matic Model Gateway is provisioned directly inside your private AWS VPC, locking down IAM roles, service quotas, and Bedrock endpoint access before making any application-layer adjustments.
Phase 2: Translation and Routing Configuration
With the gateway deployed, engineering updates application endpoints to point directly to the private proxy, retaining your existing OpenAI SDK client code without rewrites. The AI Matic Model Gateway intercepts outgoing calls and performs real-time payload translation and prompt schema standardization into Bedrock-compliant formats, preserving tool definitions, temperature settings, and streaming chunks.
For architectures utilizing embedding pipelines, parallel vector stores are indexed and benchmarked alongside the current cluster to ensure retrieval-augmented generation (RAG) hit rates do not degrade. Concurrently, engineers wire up operational controls inside the gateway - enforcing fallback routes, token-bucket rate limits, Bedrock Guardrails, and telemetry sinks like OpenTelemetry and Langfuse.
Phase 3: Shadow Testing and Cutover
To eliminate cutover risks, live production requests are dual-routed via the AI Matic Model Gateway’s shadow-piping mode. The proxy asynchronously replicates inbound traffic to the target Bedrock models, scoring the outputs against your Phase 1 golden baseline while returning only the stable production responses to downstream consumers.
This provides empirical validation of response fidelity, token consumption, and tail latencies under real traffic spikes. Once Bedrock responses match or exceed your accuracy thresholds, traffic is transitioned entirely via a centralized toggle in the gateway dashboard—cutting over with zero application downtime. Post-cutover focus shifts to operational cost optimization, specifically tuning Bedrock prompt caching and Provisioned Throughput units (PTUs).
Choosing the Right AWS Partner for Migration
Evaluating a migration partner comes down to finding teams with authentic delivery mechanisms rather than generic cloud credentials. The checklist below outlines the technical baselines to look for, along with the practical red flags to avoid.
Benefits of working with an AWS Partner
You're mostly buying time and avoiding serious rework, which also implies huge cost savings. A partner who has done this before has experience tackling regional availability, inference residency, quotas, embeddings mismatches, and latency bottlenecks that only appear at p95. On a first migration, most of the schedule risk is on problems that feel obvious the second time you see them.
There's also a funding angle your internal team can't use. AWS runs co-investment programs for qualified Amazon Bedrock migration partners and a partner-led migration can draw on them to offset part of the cost. Ask about it directly, as the right partner can enable this for you.
Top AWS Partners for OpenAI Migration
The right partner brings an automated cutover framework, handles embedding re-indexing without data loss, and secures AWS co-funding before work begins. Here is how the top AWS partners with dedicated OpenAI-to-Bedrock migration offerings:
1. GoML (NY, USA)
GoML is an AWS Generative AI, Machine Learning, and Healthcare Competency partner with a multi-year Gen AI Migration and Modernization Strategic Collaboration Agreement (SCA) for Bedrock migrations.
To date, GoML has migrated over 15 enterprises and more than $20M in production generative AI workloads to Amazon Bedrock, typically completing cutovers within two to four weeks. These migrations run on GoML’s AI Matic platform using the AI Matic Model Gateway - an in-account proxy that translates payloads, normalizes prompts, and manages shadow testing so teams can switch providers without downtime or code rewrites.
"AWS gives you exceptional building blocks for AI. We specialize in turning them into something extremely meaningful with real-world context and a steady hand". - Rishabh Sood, Founder, GoML
2. Loka
Loka has the most developed OpenAI migration practice outside GoML on this list, and describes itself as a global OpenAI on Bedrock launch partner with 500+ generative AI and agentic launches since 2023.
They run two offerings. The OpenAI Migration Assessment takes four to six weeks and benchmarks OpenAI against Bedrock and SageMaker on cost and performance, ending in a comparison, a roadmap, and a go or no-go decision guide.
The Fast Track then swaps your application to the Bedrock OpenAI client, points it at the right Region and model, and validates behaviour against your OpenAI baseline, applying IAM, KMS, CloudTrail, and VPC isolation.
AWS covers the full assessment cost and some migration costs for qualified customers, which is confirmed on a discovery call.
3. Avahi
Avahi is an AWS Premier Partner with a productised OpenAI to Bedrock migration service listed on AWS Marketplace, covering discovery, planning, prompt and model conversion, testing, and knowledge transfer. Their AI Comparison Tool benchmarks your actual prompts and volumes across OpenAI, Claude, Nova, and other Bedrock models before they quote anything, which is the right way round. They also run the same playbook for Gemini to Bedrock.
4. Caylent
Caylent is an AWS Premier Tier Services Partner with the Generative AI Competency, and was named 2024 AWS GenAI Industry Solution Partner of the Year. They have real migration evidence rather than a brochure: on one engagement where the customer doubted Bedrock could match their OpenAI solution, Caylent built a test harness comparing legacy inputs and outputs against Bedrock outputs and demonstrated 100% parity. Delivery runs through their "Catalyst" framework.
5. Others worth knowing
Cybage lists a migration service on AWS Marketplace aimed at OpenAI API and LangChain applications moving to Bedrock, Titan, and Claude models through SageMaker endpoints. Avkalan.ai and Ingeno both offer assessment-led migration services, with Ingeno running a structured six-week assessment. AllCloud has published some OpenAI to Bedrock migration work with customers globally.
Using Amazon Bedrock for Generative AI Workloads
Overview of AWS Bedrock Features
Performance benchmarking vs Amazon Bedrock
Public benchmarks offer broad directional signals, but practical decisions require testing against your specific workload. Standard scores evaluate generic tasks with synthetic prompts and vendor results reflect vendor-specific harnesses. Treat launch-day claims as baseline hypotheses to validate internally.
An actionable benchmark reports five metrics per candidate model:
- Accuracy: Scored directly against an internal, labelled golden dataset.
- Cost: Calculated per thousand requests using your actual input and output token distributions.
- Latency: Evaluated at p50 and p95 percentiles under realistic production concurrency.
- Tool-Calling Reliability: Verified across multi-step execution flows and schema adherence.
- Task Completion Cost: Measured across entire agentic workflows rather than raw token rates.
This is mainly because advanced models frequently solve problems in fewer total steps and tokens, evaluating end-to-end task cost provides a more reliable metric than comparing isolated per-token pricing.
Indicative list pricing shows why model choice dominates the cost conversation:
Nova Micro costs roughly 285 times less per input token than the frontier tier. Plenty of apps send everything to the expensive end, because that's what the prototype used. Check current pricing before you model anything, and run your own volumes through the Bedrock ROI calculator.
Optimizing latency in AI applications
Latency directly dictates user adoption and is an engineering challenge. Response times exceeding six seconds drive high drop-off rates. True optimization requires balancing compute savings against user experience, ensuring cost reductions preserve interactive speed.
Key latency optimizations include:
- Co-locate compute: Deploy inference engines within the same cloud region as your application and data storage.
- Cache prompts: Persist long, static system prompts to minimize repeated prefill computation.
- Stream output: Render tokens incrementally so users perceive immediate responsiveness via time-to-first-token.
- Route intelligently: Direct classification, filtering, and simple extraction to small, fast models, reserving frontier models for complex reasoning.
- Prune retrieved context: Restrict retrieval augmentation to high-precision chunks.
- Provision throughput: Reserve dedicated capacity for workloads sensitive to traffic spikes.
- Batch background jobs: Queue non-interactive, asynchronous tasks for bulk processing.
Agentic workflows follow a different efficiency profile. On multi-step tasks, trajectory length governs total wall-clock time. A capable model completing an objective in twelve steps consistently outperforms a faster model requiring forty.
Case studies of successful Amazon Bedrock migrations by GoML
Mariana.AI: Clinical notes and medical coding, OpenAI to Bedrock by GoML
Mariana.AI is a digital health company automating clinical documentation. They already had a working OpenAI system generating clinical notes and medical codes from transcribed doctor-patient conversations. It functioned, but accuracy, tone, and clinical relevance were falling short, and the team wanted out of single-vendor dependency.
The constraint determined the entire Open AI to Bedrock migration approach: any evaluation had to be non-disruptive. So GoML built a standalone proof of concept that benchmarked Claude Sonnet 3.5, 3.7, and 4 against the existing OpenAI system on tone, completeness, accuracy, and clinical alignment, deliberately kept separate from the production backend. Prompt orchestration ran through Portkey and Langchain, preprocessing through ECR containers, and embeddings were indexed in OpenSearch for retrieval. A Chief Medical Officer agent signs off validated reports.
GetAsset: Transaction classification, cost-driven migration by GoML
GetAsset runs personal finance management tools that categorise transactions and deliver spending insights. Their OpenAI-based transaction classification system worked but got harder to sustain as volume grew. API costs rose, prompt customisation was limited, and the lock-in risk was becoming a business problem rather than a technical one.
This is the classic cost-driven migration, and the method reflects that. GoML audited the existing workflows, input and output formats, and dependencies to find compatibility gaps. Then refactored the prompts for Claude 3.5 Sonnet, normalised payload structures and parsing logic, replaced the OpenAI calls with Bedrock endpoints, and benchmarked outputs against OpenAI for accuracy and errors, including hallucination testing.
BlueRecruit: Platform migration with a new Bedrock workload by GoML
BlueRecruit is a hiring platform for skilled trades. Their problem was not the model, it was the platform: scaling limits on Heroku, rising operational costs, and growing data complexity. So this was a full-stack platform migration rather than a model swap, and the AI work happened alongside it.
GoML ran a Heroku to AWS migration with zero-loss MongoDB data transition and reconfigured third-party integrations for Twilio, Mailgun, and Auth0, on an architecture of EC2, RDS and DocumentDB, S3, and Lambda. The new AI career assistant was built on Bedrock using GoML's Unified Migration stack, with content moderation and abuse detection tuned for an audience that includes high school students.
Lessons Learned from Migration Projects
When our ninja FDEs guide customer engineering teams for an OpenAI to Bedrock migration, the biggest insights are drawn from the fitting and re-fitting of the deployment to the customers environment. The theoretical model benchmarks are totally irrelevant there.
Before touching a line of production code, the first action item is undertaking early, measured comparisons of the system. In the case with Mariana.AI and GetAsset, we validated the target models through head-to-head empirical testing, before making any recommendation. For Mariana.AI, our ninja FDE benchmarked three different Claude models (Sonnet 3.5, 3.7, and 4) directly against their already existent OpenAI setup to evaluate factors like tone, completeness, clinical accuracy, etc. We purposely built this inside a standalone proof of concept - separate from the live backend system. So Mariana.AI faced no interruptions and physicians got to work as per usual.
Once testing is complete, the actual heavy lifting begins with prompt and payload refactoring. When we migrated the system for GetAsset, keeping the new model accurate required rewriting their prompts specifically for Claude. Bypassing payload normalization might seem tempting if you want to ship quickly, but that shortcut is usually where output accuracy collapses. In our experience, accuracy, latency and cost operate as an inseparable triad - balancing all three at once is the real engineering challenge of any model migration.
Reliability in production also demands strict output enforcement and systematic failure testing. For Mariana.AI, we implemented structured JSON validation that hit 97% schema adherence across complex specialties like cardiology, neurology and gastroenterology, cutting manual report verification time by 82%. In fintech workflows like GetAsset, we treated hallucination and edge-case testing as mandatory deployment hurdles. Workloads evolve constantly, so our team always makes sure to build pipelines designed for ongoing model substitution rather than treating a migration like a one-off patch.
Finally, a well-architected migration is essentially about scaling the business, along with trimming API bills. While our cloud and platform work with BlueRecruit helped cut their infrastructure costs by 65%, the bigger operational win was unlocking the system throughput their team needed to handle surge demand, and doing this without failing.
Future trends in OpenAI and Amazon Bedrock integration
Building prod-grade AI systems over the past few years taught us that model capability plateaus are far away. So, locking your entire architecture to a single model provider is a big mistake.
For a decent while, moving to Amazon Bedrock meant giving up the OpenAI models that your engineering teams were really familiar with. This trade-off no longer exists, and you get the added bonuses of security and compliance that the Amazon ecosystem affords. Having frontier models by OpenAI along with all controls within your cloud environment just makes things a lot more streamlined.
In our migration exercise for Mariana.AI, moving clinical notes and medical notes was just not possible on public API endpoints. Sensitive verticals like healthcare and fintech demand that data and inference doesn’t leave a particular region - and maintains compliance hygiene. Running the whole AI system inside your own cloud perimeter solves those headaches easily.
We also see teams breaking free from tight prompt binding. When your engineering team tunes a prompt for months on one specific model, swapping it out affects accuracy. For instance, a prompt finetuned for Opus 4.8 performs very differently on Fable 5. In our experience, prompts built for OpenAI models rarely return clean output on Claude or open weight models. The trend we see happening across the board is about decoupling the prompt layer from the core model routing engine.
Aside from the integration, open weight models and distilled small language models (SLMs) are becoming part of enterprise AI conversations. There is a growing realisation that frontier models are overkill for specific, high volume enterprise workflows. In 2027 and beyond, we anticipate a lot more enterprise deployments that twin OpenAI and other frontier models with open weight models and purpose-built distilled SLM alternatives to frontier models. That means a lot of enterprise teams will migrate AI workloads from Open AI to Amazon Bedrock, SageMaker, and the larger AWS ecosystem as they mature.
Migrate with GoML
GoML is an AWS partner with 3 official certifications and we are an OpenAI Select Partner - with several dozen enterprises migrated, $20M+ of generative AI workloads moved and production migrations delivered in weeks.
Talk to us about an OpenAI to Bedrock migration today.
