Your cost-intelligence diligence layer. More than 100 AICost.ai cost decision engines cover every AI workload and use case, so you can pressure-test AI unit economics across the portfolio and price the cost side of any deal with confidence.
We provide the largest free toolset and knowledge base for the questions that decide deals: does a cost claim survive vendor-exact pricing, where is margin really going, and where is the optimization upside across your portfolio.
More than 100+ AICost.ai cost decision engines support every AI workload, from RAG to agentic loops to physical AI. You chain, mix, and drop them like lego blocks into your workflows via MCP, and stand up real-time AI cost monitoring and management within a day.
Explore the AICost engines →Each engine models one real cost surface, an agent loop, a RAG pipeline, a model-routing decision, the total cost of a workload. Chain them and you have an accurate cost model for any AI workflow a portfolio company runs, sitting on top of its existing LLM gateway.
Like a firewall lets the good traffic through and stops the bad, CostWall lets your companies move quickly with AI while it keeps cost control and governance safeguards in place, visibility and optimization applied on the fly.
Turns a workload into verifiable, vendor-exact cost evidence, the numbers a board, an LP, or a diligence team can stand behind, priced from a daily-maintained pricing source rather than a founder's spreadsheet.
A few examples, each is a chain of live engines that together price one common portfolio workflow:
Prefer hands-on? The same AICost.ai cost decision engines are available as an interactive cost-intelligence layer, so your teams (or ours) can model and visualize AI costs on demand. Explore the AICost.ai cost decision engines →
Have a portfolio company with a specific AI & cloud workload? We'll assemble the AICost.ai cost decision engines, or an MCP chain, to model it. Email [email protected].
Build my MCP chainPurpose-built on top of the engines, for the questions that decide term sheets in 2026: does the cost story survive vendor-exact pricing, and is growth efficient or subsidized?
Stress-test a startup's claimed cost-per-unit against vendor-exact pricing. Verdict, real gross margin vs the 2026 AI median, a 12-24-month margin trajectory, and an auto-written deal-memo.
The most-scrutinized 2026 efficiency metric, with the AI twist. Reclassifies inference disguised as growth spend back into COGS to expose the true burn multiple and gross margin.
Prices the API path vendor-exact against a self-hosted fleet per inference, solves the break-even volume, and checks whether the claimed GPUs can physically serve the claimed traffic.
For value creation: converts achievable AI and cloud savings into basis points of EBITDA expansion, values it at your exit multiple, and rolls it up across the portfolio at a realistic adoption rate.
Each runs as an MCP tool your diligence agents can query directly, aicost.cost-claim-diligence, aicost.burn-multiple-diligence, aicost.gpu-inference-diligence, aicost.portfolio-ebitda-lift, or as a calculator with a downloadable, data-room-ready PDF memo.
Most organizations don't run one AI project. They run many, at different stages. Some are still ideas. Some are in design. Some are in pilot. Some are being built into production. And some are finished yet stuck, unable to launch because the cost is unpredictable or the governance isn't signed off. AICost works at each stage.
A quick, honest cost before anyone commits time.
Model the full workload before a line of code ships.
Prove the numbers on real traffic, then tune them.
Keep spend in bounds as volume grows.
Cleared to launch, but costs are unpredictable and governance isn't signed off. This is where projects stall.
AICost began with the AI cost surface. The next chapter connects it to where the money and the risk actually live, the systems of record and the physical world.
For cost intelligence across your portfolio, a diligence layer for AI unit economics, or to back the next chapter, start a confidential conversation.
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