Cloud cost governance
Uncover 400+ types of inefficiencies and Shadow Waste across Multi-Cloud, Kubernetes, AI Platforms, Data Cloud and Code, with the owner and the code behind every finding.
Waste traced back to the exact code that created it, fixed with automated pull requests, and stopped before it ships.
Uncover 400+ types of Shadow Waste across AWS, Azure, Google Cloud, Kubernetes, Data Cloud (e.g., Snowflake, Databricks) and AI platforms.
Trace each issue back to the exact code that created it and resolve it at the source.
Policy-driven automations and scheduled actions apply fixes and enforce guardrails, engineer-in-the-loop or fully autonomous.
CI/CD guardrails block costly issues before merge, stopping waste before it reaches production.
How It Works
Shadow Waste
Reveal hidden cloud cost inefficiencies, from complex service misconfigurations to architectural pitfalls. The resources look healthy, so standard tools pass them by.
Full context
Accelerate resolutions with full technical context, remediation scripts, 1-click fixes and Agentic Remediation. Each finding reaches the right engineer in the tools they already use, like Jira, ServiceNow, Slack or Teams.
Cloud-to-Code
Adaptive6 traces inefficiencies to your Infrastructure as Code with Cloud-to-Code technology for instant pull requests, for example in Terraform. The fix is reviewed like any other change, and it stays fixed.
Shift-left
Cost policies in CI/CD block costly changes before merge, with suggested fixes for quick resolution. New waste is caught in code, before it reaches production.
Shift-right
A lightweight Runtime Sensor scans workloads and AI models across your VMs and containers, providing the runtime context needed to pinpoint application inefficiencies and drive down cloud costs.
Cost management
Visibility, accurate allocation, budgets and forecasting, anomaly detection and commitments management, across every provider and account.
Unified visibility across all clouds, with showback and chargeback by team, project and virtual dimensions.
Budgets, alerts and forecasting that keep every team’s cloud spend on plan.
Unusual spend is flagged as it happens, so overspend is caught before it grows.
Maximize savings from commitments with clear visibility into coverage, utilization, and renewal opportunities.
AWS, Azure and Google Cloud, Kubernetes, and data platforms like Snowflake, Databricks and BigQuery.
“This is the beauty of Adaptive6. Right away you can see all the context available to you about your Python runtime and a clear recommendation showing you what you were running and what you should be running.”
Jeff HyattTicketmaster
Shadow Waste is hidden cost inefficiency in resources that look healthy: complex service misconfigurations, lesser-known features left off, outdated runtimes and architectural pitfalls that standard tools miss. Beyond the roughly 30% of cloud spend commonly considered waste, Shadow Waste can account for up to 15% more.
AWS, Azure and Google Cloud; Kubernetes on EKS, AKS and GKE; data platforms such as Snowflake, Databricks and BigQuery; and AI platforms such as Amazon Bedrock, Azure OpenAI, Vertex AI and SageMaker.
Each finding goes to its owner with full technical context and a concrete fix: a remediation script, a 1-click fix, Agentic Remediation, or an Infrastructure as Code change that becomes a pull request in one click. Findings can also be sent to Jira, ServiceNow, Slack or Teams.
Yes. Unified cost and usage visibility, cost allocation with showback and chargeback, budgets, alerts, forecasting, anomaly detection, commitments and more.
When you choose. Policy-driven automations and scheduled actions apply fixes and enforce guardrails, engineer-in-the-loop or fully autonomous.
