AI on AWS: readiness assessment and FinOps for AI
Find out what your data, infrastructure and team need before an AI project starts, build it on AWS the right way, and keep model and GPU costs visible and under control once it's live.
From readiness to a bill you can explain
AI readiness assessment
Use cases, data quality and access, security and infrastructure reviewed, with a short list of projects worth doing first and what each needs.
Model selection
Models on Amazon Bedrock compared on quality, latency and cost for your actual use case, not on benchmarks alone.
Generative AI architecture
Retrieval (RAG), guardrails, private networking and data handling designed so a prototype can become a production service.
AI cost visibility
Model and token spend broken down by team, application and environment with tagging and inference profiles, so AI costs stop being one line on the bill.
GPU & inference costs
Idle GPU instances and endpoints found, instance choice reviewed, and Spot, AWS Inferentia or Trainium used where they cut the cost.
Budgets & alerts for AI
Budgets and anomaly alerts on AI spend, so a runaway agent or a traffic spike shows up the same day, not at month end.
How AI work gets funded
A new AI workload on AWS can qualify for AWS funding, requested through a Partner.
AWS programs
AWS PoC funding
A generative AI proof of concept for a new workload on AWS can qualify for AWS PoC funding, sized by AWS from the workload's expected spend.
Averium
Where the work lowers your AWS bill, part of Averium's fee is recovered from those savings instead of being invoiced upfront. The split is fixed in the statement of work before anything starts.
Only an AWS Partner can request AWS funding. We prepare and submit the application; AWS makes the final award. How AWS funding works
From first call to done
- 1
Assess
Use cases, data, security and infrastructure reviewed.
- 2
Prove
A focused proof of concept with agreed success criteria.
- 3
Build
Production architecture on AWS, delivered as code.
- 4
Control
Cost allocation, budgets and alerts for AI spend.
AWS Partner. Certified engineers. Paid from results.
The team that builds it also runs the FinOps platform that keeps it cost-efficient afterwards.
- AWS Certified Machine Learning Specialty and AI Practitioner engineers
- Cost is designed in from the start, backed by the Averium FinOps platform
- Funding applications prepared and submitted as your AWS Partner
- Everything built in your AWS accounts, as code you own
Common questions
What is an AI readiness assessment?+
A short review of where AI can help your business and what's in the way: data quality and access, security and compliance, infrastructure and skills. You get a prioritized list of use cases and what each one needs to succeed.
Which models do you work with?+
Mainly the models available on Amazon Bedrock, such as Anthropic Claude, Amazon Nova, Meta Llama and Mistral, plus custom models on Amazon SageMaker when a hosted model doesn't fit.
Is our data used to train the models?+
On Amazon Bedrock, your prompts and outputs are not used to train the base models and are not shared with the model providers. We design the architecture so your data stays in your AWS account.
How do you track AI costs by team or product?+
With cost allocation tags, application inference profiles on Amazon Bedrock and separate accounts or endpoints where it makes sense, so every model call can be attributed to a team, application or customer.
Can AWS fund an AI project?+
A proof of concept for a new AI workload on AWS can qualify for AWS PoC funding, which only a Partner can request. AWS decides each award.