Nuvrix AI Cost Review

Overview The Nuvrix AI Cost Review is a fixed-scope engagement that analyses your AWS AI and machine learning spend and hands you a concrete, prioritised plan to reduce it. It covers token-level spend, model selection, inference configuration, and cost allocation - the levers most teams have never pulled because they didn't know they existed.

AI spend on AWS grows fast and is poorly understood by most finance and engineering teams. Token costs are rarely allocated to the features or workflows driving them. Model choices made during a proof of concept stay in production long after cheaper, faster alternatives became available. Prompt caching and batch inference go unconfigured because nobody flagged the savings opportunity. This engagement surfaces all of it.

What we analyse We pull your cost and usage data at the token level and analyse spend by model, by workload, and by feature where tagging allows. We identify model right-sizing opportunities - cases where a smaller or newer model delivers equivalent output at lower cost. We find prompt caching candidates and quantify the saving. We identify workloads suitable for batch inference. We review your token allocation and tagging to determine what percentage of AI spend is currently unattributable.

What you receive

• Token-level spend breakdown by model and workload
• Model right-sizing recommendations with estimated savings
• Prompt caching and batch inference opportunities with effort and saving estimates
• Tag hygiene recommendations to make AI spend fully attributable
• Prioritised savings plan with total identified savings
• Findings session with your engineering and finance leads

Who it's for Organisations running AI workloads on AWS - particularly those using Amazon Bedrock or deploying multiple models in production who want to understand and reduce their AI infrastructure spend. Suited to any team whose AI bill has grown faster than expected and whose finance team is starting to ask questions.

How it works The engagement runs over one week. We access your cost and usage data using read-only permissions, run our query pack against your billing data, and spend the majority of the engagement on analysis and prioritisation. We close with a findings session walking through the savings plan and a clear view of which actions your team can implement immediately.