AI Workloads to Bedrock
Move your AI workloads to Amazon Bedrock
We migrate your GenAI workloads from third-party APIs to Amazon Bedrock: your data in your account, model choice driven by results and cost, and the same FinOps discipline we apply to the rest of your cloud.
Your data and your AI, in your own AWS account
The problem
- Your product depends on an external AI API you send customer data to.
- Token cost grows unchecked and you have no visibility per feature or per team.
- You're locked into a single model provider and migrating looks like a huge project.
- Security and compliance require inference to happen inside your perimeter.
What's included
- Survey of your current AI workloads: prompts, models, volumes and costs.
- Model selection on Amazon Bedrock by result and by cost, benchmarked against the current one.
- Prompt migration and inference logic adaptation, without degrading quality.
- Before/after comparative evaluation against a set of real business cases.
- Guardrails, invocation logging and cost control per use case.
- Network and permission architecture so inference happens inside your AWS account.
Bedrock as the foundation, without marrying a model
Amazon Bedrock gives you access to several models (including Anthropic's Claude) behind one API, inside your account. That lets you choose the model by result and by cost, and swap it when a better one appears, without rewriting your product.
How we work
Discovery
We inventory your AI workloads, their costs and their quality, security and latency requirements.
Plan & Quote
We define the target model, the per-workload migration plan and the evaluation set, at a closed price.
Execution
We migrate workload by workload, comparing quality and cost against the baseline before cutting over.
Hand-off & MSP
We leave quality, cost and usage monitoring in place. We can keep operating and optimizing the platform.
Stack & technologies
Same quality, your own perimeter, cost under control
FAQ
Will I lose quality by switching models?
That's why we migrate with evaluation: we compare the current and the new model over a set of real cases, and only cut over when quality holds up.
Does my data train third-party models?
No. On Amazon Bedrock inference happens in your account and your prompts and data are not used to train the base models.
How long does a migration like this take?
It depends on how many workloads you have and how critical they are. It's done incrementally, workload by workload, without stopping the product.
How do you control inference cost?
With per-use-case metrics, consumption limits and model choice matched to the task. It's FinOps applied to AI.
