Size infrastructure, estimate cloud spend, and pressure-test AI decisions - every input is editable and every default is explained. Run the numbers in the browser; no sign-up, no black boxes.
Estimate the monthly and one-time AWS cost of running a production RAG platform: embeddings, vector store, LLM generation, storage and compute.
Open tool →Estimate the one-time cost of migrating off your data warehouse to a lakehouse, then see what staying costs, what moving costs, and the payback period.
Open tool →Cost the same AI workload three ways - pay-per-token API, rented GPUs, or owned GPUs - side by side, and find your crossover point.
Open tool →Compare the true cost of building an AI capability in-house against buying or partnering: team cost, ramp time, failure risk, and break-even point.
Open tool →Answer a few questions about how you build and run AI, data and cloud, and get a map of the readiness gaps, delivery risks, and regulations most likely to matter.
Open tool →Six dimensions that separate teams where AI multiplies output from teams where it amplifies the mess. Answer honestly and get your tier plus three moves that matter.
Open tool →Eight dimensions that decide whether your quality signal is trusted enough to ship on, and whether your team is ready for agentic testing. Get your tier, your weakest areas, and three moves that matter.
Open tool →Turn an estimate into a plan. Bring your inputs to a working session with our engineering leads - implementation, governance, and security handled as one connected responsibility.
