IT, marketing, finance and the field are all shipping AI & cloud projects on their own. AICost gives you one layer for visibility, optimization, and forecasting across all of it, so you can move fast without losing the plot on spend or governance.
We provide the largest free toolset and knowledge base for resolving your most complex cost issues: bill spiking and cost visibility, optimizing and reducing cost without losing quality, and planning a new AI workload accurately.
More than 100 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.
Use the decision engines for ad-hoc AI cost calculations →Different teams, different gateways, different clouds, different vendors, across branches and regions. The cost, the risk, and the duplication hide in the gaps between them. AICost unifies the picture without slowing anyone down.
Ingest AI + cloud billing across teams and geos and attribute every dollar by org, project, feature and workload, one pane over the whole estate.
The right lever depends on the workload. The engines find each one, routing, caching, batching, right-sizing, self-host break-even, RAG tuning, commitment discounts.
Model the cost of a new workload before it ships, TCO, ROI, scale projection, so finance and engineering plan on the same numbers.
No big-bang rollout. Prove it on one team's AI spend, wire it into the systems you already run, then extend org-wide with central governance.
Pick one team's AI workload. We model it and surface the savings and risks, no commitment, no cloud credentials required to start.
Wire into your gateway (LiteLLM, Cloudflare, Portkey) and billing ingest (AWS CUR, Azure, BigQuery). CostWall enforces budgets and guardrails in production.
Extend across IT, marketing, finance and the field, each org gets its own workspace, budgets, and attribution, under one roof.
SSO/SAML, RBAC, per-team pricing overrides, and a CostProof ledger of modeled-vs-actual, control and evidence, centrally.
A few concrete examples of how the engines cut spend depending on what a team is running:
Route routine drafts to a cheaper model, cache repeated prompts, and batch overnight jobs, often the single biggest per-call saving. Multi-Model Router · Prompt Cache ROI · Image Generation Cost
Tune chunking and embeddings, right-size the vector DB, and decide RAG vs fine-tuning on real numbers. RAG Pipeline · Vector DB Cost · RAG vs Fine-Tuning
Cap agent-loop fan-out, route reasoning-heavy steps deliberately, and reserve premium models for hard tasks only. Agent Loop Cost · Reasoning Token Cost · Multi-Model Router
Negotiate like the Fortune 500, commitment discounts across Anthropic, OpenAI, Bedrock, Azure OpenAI, Vertex, and self-host break-even at scale. Local vs Cloud · Buy vs Build · Annual vs Monthly
Every team can put the same decision engines to work directly, the central platform enforces while teams explore. Browse the decision engines →
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.
Enterprise AI isn't one model call, it's chains of tools across your systems of record. Our stack is the cost-and-discovery layer underneath them.
A 491-field schema over 115K+ tools across 39 hubs, agents query it via MCP & API to assemble compliant, integrated stacks (HIPAA/SOC2, integrations, API/MCP readiness) for 2800+ roles.
~100 decision engines pricing every AI workload in the chain, with CostWall enforcement and CostProof evidence, on a daily-maintained pricing SSOT.
Extends the same intelligence to cloud spend, so AI and infrastructure are optimized together across the tool chain.
SSO/SAML & RBAC · private MCP endpoint · DPA, data-retention terms & SLA · security review and procurement support · custom pricing overrides per team. Implementation from $5,000 one-off, gateway integration, billing ingest wiring, private deployment; most teams are live in a day.
Tell us about your AI & cloud workloads and what you're trying to solve. We reply to every serious enquiry.
✉ [email protected]One hour on your actual AI + cloud costs, diagnosing spikes, cutting spend without losing accuracy, or planning a new workload. You leave with a written report and a toolchain.
Fee credits toward any AICost plan, you never pay twice for the same ground.
Start a free pilot on one workload, or let’s scope a rollout across your orgs and geos.
✉ [email protected]