FinOps Governance for Cloud AI Infrastructure
Go Beyond Visibility. Fix and Prevent AI Waste.
The control plane that acts. Discover waste across every layer of AI infrastructure, then fix it and prevent it from returning, through automated policy and agentic AI.
Reduction in AI bill
Eliminate waste and inefficiencies at scale
Faster time to savings
Capture savings and eliminate waste faster
Less time spent in governance
Increase visibility while reducing reporting time and effort
Foundation
The Stacklet Process
Discover
See every layer of AI spend
Real-time visibility across GPUs, jobs, endpoints, and storage. Surface the token counts and signals behind model spend: untagged profiles, idle throughput, wasteful agents. Tie AI cost to business value.
Fix
Remediate waste, automatically or on approval
Fix waste through policy-driven workflows and agentic AI, autonomously or with a human in the loop. Stop runaway jobs, right-size GPUs, retire idle endpoints, and clean up untagged profiles before budgets spiral.
Prevent
Guardrails from provisioning to runtime
Enforce guardrails so waste never reaches production. Require approved configs and ownership at creation. Alert or block when a resource breaks policy.
Discover
See every layer of AI spend
Real-time visibility across GPUs, jobs, endpoints, and storage. Surface the token counts and signals behind model spend: untagged profiles, idle throughput, wasteful agents. Tie AI cost to business value.
Fix
Remediate waste, automatically or on approval
Fix waste through policy-driven workflows and agentic AI, autonomously or with a human in the loop. Stop runaway jobs, right-size GPUs, retire idle endpoints, and clean up untagged profiles before budgets spiral.
Prevent
Guardrails from provisioning to runtime
Enforce guardrails so waste never reaches production. Require approved configs and ownership at creation. Alert or block when a resource breaks policy.
Every Layer of Cloud AI Infrastructure
Waste Hides in Every Layer. So Does the Fix.
GPUs, custom models, foundation models, data, and the network beneath them. Stacklet discovers, fixes, and prevents waste at every layer.
GPUs & Compute Accelerators
Govern every GPU from provisioning to retirement.
Stacklet sees every GPU and who owns it, then enforces the right class at build time. Idle instances get flagged and stopped before they bill overnight. Oversized ones get caught on low VRAM. Untagged GPUs are held back before they run unattributed.
Foundation Models
Govern which models run, and what they leave behind.
Foundation models leave a trail of untagged deployments, idle agents, and dead endpoints, all billing. Stacklet surfaces each one and its owner, then acts: flagging unowned resources, routing simple tasks to approved models, and retiring what’s left running before it turns into spend..
Tokens
Govern token spend before budgets spiral.
Tokens are the unit of spend for foundation models, and most teams can’t see them until the bill lands. Stacklet meters consumption per profile and ties it to an owner. Cross a baseline and the owner is alerted, with the profile suspended before runaway jobs burn the budget.
Custom Models
Govern every stage of the custom model lifecycle.
Training pipelines, tuning jobs, and inference endpoints are the highest-churn workloads in the cloud, leaving clusters, checkpoints, and running resources behind long after the work is done. Stacklet tags every job at launch so nothing runs unattributed, terminates stalled jobs, and auto-retires idle endpoints before they quietly bill.
Data Lifecycle & Storage
Govern the storage AI workloads leave behind.
Checkpoints, datasets, logs, and vector indexes pile up with no lifecycle rules to clear them. Stacklet extends proven storage governance to AI-native types like vector databases and model artifacts. It flags stale vector indexes for cleanup, archives cold datasets and orphaned checkpoints to cheaper tiers, and enforces retention on evaluation logs..
Networking & Data Movement
Govern the AI traffic driving your egress bill.
AI workloads move large volumes of data between services, regions, and the outside world. Stacklet surfaces that egress alongside GPU and model spend in one view, catches unexpected inter-region movement from training and inference pipelines, and applies your existing routing and bandwidth policies to AI traffic.
One Control Plane
Every Layer Governed. Every Dollar Accounted For.
Idle resources stopped, spend attributed, waste cleared. One control plane keeps all six layers running lean.
What customers are saying
Stacklet has helped us save millions of dollars by driving action across multiple engineering teams and reducing our Mean Time to Savings
Lindbergh Matillano
Director Cloud Optimization, Avalara
Stacklet brings the best of Cloud Custodian compliance policies within manageable reach of even the most disparate and large organizations
Solutions Architect
G2 Review
Stacklet has helped us save millions of dollars by driving action across multiple engineering teams and reducing our Mean Time to Savings
Lindbergh Matillano
Director Cloud Optimization, Avalara
Stacklet brings the best of Cloud Custodian compliance policies within manageable reach of even the most disparate and large organizations
Solutions Architect
G2 Review
Featured Resources and Events
Stacklet Short Explainer Video
Most cloud FinOps and security tools stop at visibility. Stacklet goes further—helping you discover, fix, and prevent cloud inefficiencies and risks in one platform.
Accelerating Cloud Governance with Stacklet MCP Server
A new way to extend Stacklet’s FinOps and security governance capabilities into the AI ecosystem.
FinOps Book – Continuous Cloud Usage Optimization – 2nd Edition
This book goes beyond tools, diving into real-world insights and the often-overlooked aspects of cloud usage optimization and cost governance.
FinOps Policy & Governance at Scale: Why Hierarchy and Layers Matter
Scaling governance, however, is difficult – especially in large organizations. This blog covers how hierarchical and layered policies can help.
Questions
Cloud AI Infrastructure, governed. Questions answered.
Need more help?
Get in touch with our team
Cloud AI Infrastructure is the set of cloud services AI workloads run on, GPU compute, foundation model services like AWS Bedrock and Google Vertex AI, custom model platforms like Amazon SageMaker, plus the storage and networking behind them. Gartner uses the term as its own category. Each layer bills differently, and most cloud cost tools only see part of it.
Stacklet fixes it. Most tools stop at detection, surfacing waste on a dashboard and leaving remediation to you. Stacklet closes the loop: it remediates through policy-driven workflows, autonomously or with human-in-the-loop approval, then prevents recurrence by enforcing guardrails at provisioning and in IaC before resources are ever created. Visibility is the starting point, not the product.
Stacklet governs the full GPU lifecycle, from provisioning through runtime to decommission. At provisioning, it enforces approved instance classes and blocks high-end accelerators unless there’s a justified need. At runtime, idle instances are stopped before they run up an overnight bill and underutilized ones are flagged. Dev and test are held to spot pricing, and untagged resources are caught before they run unattributed.
AWS, Google Cloud, and Azure, down to the specific services: Bedrock, SageMaker, Vertex AI, Azure AI, plus the GPU and storage layers underneath. Where most tools go wide but shallow, Stacklet governs every layer in depth, reading the metrics that matter and acting on them directly.
Your Journey to Autonomous Cloud & AI Starts Here!
Put your Cloud & AI infrastructure on autopilot – boosting team productivity while cutting costs, eliminating risk, and maintaining compliance at scale.