Build and scale AI/ML workloads on AWS with cost and security in view
AI projects need more than a GPU instance. Data access, security, model selection, inference patterns, observability and unit economics all influence whether a workload can scale.
Solutions built around your requirement
Start with your current environment, business goals and constraints. We help define a practical scope before recommending a product, platform or migration route.
From first conversation to a workable plan
Define model, data, latency and throughput requirements
Choose compute, storage, networking and managed services
Build a secured POC and measure performance/cost
Optimise and prepare for production
Scope an AI/ML workload
Share your current setup, what you want to achieve, approximate users or workload count, and target timeline. We will help you identify the next practical step.
Frequently asked questions
Can AWS credits support AI/ML projects?
Some eligible AWS programmes may support qualifying workloads, but credit scope and service applicability depend on the programme terms and approval. Confirm the exact terms before assuming GPU or AI services are covered.
How do I estimate AI infrastructure costs?
Estimate model and data sizes, training frequency, GPU or accelerator hours, inference volume, storage, networking and monitoring. Run a representative POC to validate assumptions.
Can you help move an AI workload from another cloud?
Yes. We can assess data, model dependencies, containerisation, APIs, GPU requirements and security before mapping the workload to AWS services.
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