# Best AI Agents for Cloud Cost Optimization (2026) Canonical: https://www.pointfive.co/guides/best-ai-agents-for-cloud-cost-optimization-2026 AI agents for cloud cost optimization compared on a five-level autonomy scale: what each can change, what approvals it needs, and how it is priced. By: PointFive Team Published: 2026-10-09 Updated: 2026-10-09 [All guides](https://www.pointfive.co/guides) # Best AI Agents for Cloud Cost Optimization (2026): Autonomy Levels Compared PointFive Team October 9, 2026 11 min read Disclosure: PointFive wrote this guide, and PointFive is listed first because it is our platform, not because of a ranking. Every competitor statement comes from that vendor's own website, documentation or press material, checked in October 2026, and linked in the sources. Where a vendor does not publish something, we say so. ## TLDR - "AI agent" now covers everything from a chat box over billing data to software that buys commitments or resizes pods with no human in the loop. Ask what the agent can change, not whether it has AI. - Fully autonomous agents exist today mostly in narrow domains: commitments (ProsperOps, Usage.ai, Harness) and Kubernetes (CAST AI, Zesty, Sedai). Cloud-native assistants from AWS, Microsoft and Google answer and recommend, and Azure's preview agent writes scripts, but none of them executes fixes for you. - PointFive is the agentic FinOps platform that finds waste across cloud, data and AI and lets each customer choose, process by process, whether an agent answers, prepares a fix for an engineer, or executes it under per-change, per-playbook or no approval. - The real buying criterion for automated remediation is safety: read-only discovery, scoped write permissions, an approval model you control, an audit trail and rollback. ## How we evaluated We placed each tool on a five-level autonomy scale, based on what the vendor documents today: - Level 1, Answers: natural-language questions over cost data. Read-only. - Level 2, Recommends: prioritized findings, tickets or issues. A human still designs and makes the change. - Level 3, Prepares fixes: generates code, scripts or pull requests that a human reviews and executes. - Level 4, Executes within approved scope: the tool makes the change with write permissions, after per-change or policy-level approval. - Level 5, Continuous autonomous optimization: the tool acts continuously without per-change approval, inside a bounded domain. We also recorded what each tool can change, its approval model and its pricing model as publicly stated. Many tools span several levels. ## The tools ### 1. PointFive (PointFive OS and TokenShift) Category: Agentic FinOps platform (waste detection and remediation across cloud, data and AI) Best for: Enterprises whose problem is fixing known waste across many platforms, not seeing it. [PointFive OS](https://www.pointfive.co/products/pointfive-os) , "The Agentic FinOps Platform for your Cloud, Data & AI", runs 500+ deep detections across 40+ [platforms and integrations](https://www.pointfive.co/coverage) , including AWS, Azure, Google Cloud, OCI, Kubernetes, Snowflake, Databricks, BigQuery and AI providers. It spans Levels 1 to 5. In Chat, teams ask Pointer, the AI agent in PointFive OS, about costs (Level 1). With [prompt remediation](https://www.pointfive.co/blog/prompt-remediation) , engineers generate and review fixes in their own IDE and deploy them through their own workflow (Level 3). Coworkers find owners and execute changes using customer-authorized write permissions (Levels 4 and 5), with approval set per process: per change, once for a trusted playbook, or not at all. PointFive reports that at least 20% of the waste it detects is now [handled completely autonomously](https://www.pointfive.co/blog/safe-automated-cloud-remediation) under customer-set approval policies. [TokenShift](https://www.pointfive.co/products/tokenshift) , "The unified control plane for every AI agent", governs AI agent usage. - Strengths: One approval model across cloud, data and AI. Read-only discovery. Agents monitor results, offer rollback and log every step. Rated 4.9/5 on G2. - Limitations: No public price list or self-serve tier. Automated remediation needs customer-authorized write permissions that security teams will review. - Pricing: Custom; contact sales. Available on AWS Marketplace and Azure Marketplace. - Choose it if: You have a backlog of validated savings that nobody gets to. Nubank saved $8.2M in 9 months with PointFive. ### 2. Sedai (as of October 2026) Category: Autonomous optimization platform Best for: Teams that want one autonomous engine for Kubernetes, VMs, serverless and other runtime resources. Sedai calls itself "The Optimization Platform for Cloud & AI" and documents three modes: Datapilot (observability only, Level 1 to 2), Copilot (one-click approval per optimization, Level 4) and Autopilot ("fully autonomous execution of optimizations", Level 5). It covers Kubernetes, VMs, serverless, storage, databases, data and streaming, GPUs and AI agents on AWS, Azure, Google Cloud, IBM Cloud and Oracle Cloud. - Strengths: Explicit, documented autonomy modes. States that "Every optimization is constrained, validated, and reversible." - Limitations: Rollback mechanics are not detailed publicly. Runtime-focused; commitments and data platforms such as Snowflake are not documented. - Pricing: Custom, "based on your unique cloud environment and usage"; 30-day free trial. - Choose it if: You want to move runtime optimization from one-click approval to autopilot. ### 3. CAST AI (as of October 2026) Category: Kubernetes automation ("Application Performance Automation") Best for: Large Kubernetes estates. CAST AI "turns Kubernetes workload, infrastructure, cost, and SLO signals into safe automated actions". Its Workload Autoscaler applies rightsizing continuously (Level 5) under policies, with per-workload opt-in, exclusion labels and Pod Disruption Budgets respected. Agentic runbooks that fix drift and operational issues run with approval: "You approve every change before it ships" (Level 4). Onboarding starts read-only. - Strengths: Pod rightsizing, node autoscaling, Spot and GPU optimization across EKS, AKS, GKE, OpenShift and on-premises. - Limitations: Kubernetes only. The cluster must run in automated optimization mode for changes to apply. - Pricing: Custom quote based on your environment. - Choose it if: Most of your spend is Kubernetes compute. ### 4. Harness Cloud & AI Cost Management (as of October 2026) Category: Cost management inside a software delivery platform Best for: Harness customers that want commitments and idle non-production resources handled automatically. Harness says its Cost Management Agent "finds and fixes cloud and AI waste autonomously": it "buys and manages your commitments and shuts down idle infrastructure", "with no scripts or manual approvals required" (Level 5 in those domains). AutoStopping stops idle non-production VMs and containers and restarts them on demand. The agent also answers spend questions (Level 1). - Strengths: Commitments, AutoStopping and Kubernetes optimization in one product, across AWS, Azure, GCP and AI providers. - Limitations: The agent page does not describe audit logs, rollback or per-change approvals. - Pricing: Enterprise plan, "Priced by quotation". - Choose it if: You already run Harness for delivery. ### 5. ProsperOps (as of October 2026) Category: Autonomous commitment management Best for: Hands-off Savings Plans, RIs and CUDs. ProsperOps ("Automatic Cost Optimization for AWS, Azure, and Google Cloud") manages commitment portfolios continuously (Level 5). Flexera acquired it in January 2026. Its least-privilege role "cannot launch, start, stop, or terminate" resources, and prepayments are capped at a quarterly budget the customer approves. Scheduler (early access) starts and stops resources on schedules with extra permissions. - Strengths: Clear permission boundary. Fee tied to realized savings. - Limitations: Rate optimization only; Scheduler does not find idle resources. - Pricing: Percentage of realized savings for discount management; flat monthly fee per resource for Scheduler. - Choose it if: Commitment coverage is your biggest lever. ### 6. Usage.ai (as of October 2026) Category: Automated commitments with downside protection Best for: Teams worried about committing to usage that may shrink. Usage.ai buys and manages Savings Plans, Reserved Instances and CUDs on AWS, Azure and Google Cloud. Autopilot runs decisions automatically (Level 5) and can be paused or overridden; Copilot lets you approve, skip or schedule each recommendation (Level 4). Cashback covers "eligible unused value". - Strengths: Per-account choice between Autopilot and Copilot. - Limitations: Commitments only. - Pricing: Share of new savings; no upfront fee or subscription. - Choose it if: You want automation and insurance on commitments. ### 7. Zesty (as of October 2026) Category: Autonomous Kubernetes optimization Best for: Kubernetes rightsizing and storage autoscaling within guardrails. Zesty, an "Autonomous Kubernetes Optimization Platform", rightsizes resources, places pods and autoscales persistent volumes within guardrails you define (Level 5), and also optimizes AWS and Azure commitments. - Limitations: No AI agent or conversational features described; Kubernetes and commitments only. - Pricing: Usage-based and tied to value delivered. - Choose it if: You want guardrailed Kubernetes automation without an agent layer. ### 8. Vantage (as of October 2026) Category: Cost visibility and allocation with an agent Best for: Engineering-led teams that want self-serve reporting. Vantage's FinOps Agent answers cost questions and edits Vantage reports, scoped to the user's permissions (Level 1). A GitHub integration in private preview since April 2026 opens issues with the recommended change for a coding agent or engineer to implement (Level 3). Autopilot automates AWS Savings Plans (Level 5). - Strengths: Public pricing and a free tier. - Limitations: The GitHub app can create issues "but not directly merge or deploy changes." The Automated FinOps Agent is listed on the Enterprise plan. - Pricing: Starter free; Pro $30/month; Business $200/month; Enterprise custom. Agent usage is billed on tokens, waived during an introductory period. - Choose it if: You want an agent next to self-serve reporting. ### 9. Amnic (as of October 2026) Category: FinOps platform with AI agents Best for: Giving many roles plain-language answers and reports. Amnic describes "a FinOps OS powered by AI agents" with four agents, X-Ray, Insights, Governance and Reporting, across AWS, Azure, GCP and Kubernetes. It states its model is "recommends and routes, human approves" and that the platform "stays read-only" (Levels 1 to 2). - Limitations: Agents do not execute changes. - Pricing: Custom, based on cloud spend and team size; AI Token Management from $999/month. - Choose it if: You need insight and governance, and engineers will make every change. ### 10. CloudZero (as of October 2026) Category: Cost allocation and AI ROI ("The Financial Control Plane for AI") Best for: Engineers who want to ask cost questions from their coding agent. CloudZero's AI Hub MCP server works in Claude Code, Cursor, Codex, Gemini CLI and other tools, so teams can investigate cost changes and get recommended actions (Levels 1 to 2). - Limitations: No documented execution of changes. - Pricing: Custom quote. - Choose it if: Allocation and unit economics come first. ### 11. CostAnalyst (as of October 2026) Category: Recommendation tool with no account access Best for: Small teams that cannot grant any cloud access. CostAnalyst analyzes uploaded AWS, Google Cloud and Azure billing exports and SaaS subscriptions, and "has no access to your cloud accounts or SaaS tools at all, read or write." Its own summary: "CostAnalyst recommends, you decide." (Level 2). - Strengths: Zero access risk; public self-serve pricing. - Limitations: No execution, and analysis is only as fresh as the export. - Pricing: $59 to $1,245 per month, billed yearly. - Choose it if: Access is the blocker and spend is modest. ### 12. Native assistants: AWS, Microsoft Azure and Google Cloud (as of October 2026) Category: Built-in cost assistants Best for: Everyone, as a free or low-cost baseline. Amazon Q Developer answers cost questions, investigates changes and surfaces Cost Optimization Hub, Compute Optimizer and commitment recommendations, explaining implementation steps (Levels 1 to 2). The AWS Billing and Cost Management MCP server brings that analysis to other assistants. The Azure Copilot Optimization Agent (preview) recommends changes for VMs and scale sets and generates Azure CLI or PowerShell scripts you deploy (Level 3). Gemini Cloud Assist in Cloud Billing (preview) builds reports and summarizes FinOps hub insights (Levels 1 to 2). - Strengths: First-party data; little or no procurement. - Limitations: One cloud each; none executes the fix; Azure and Google features are in preview. - Pricing: Amazon Q Developer Free Tier allows 25 context questions per account per month, then Q Developer Pro; Azure and Google preview pricing not published. - Choose it if: You are single-cloud or just starting. ## Side-by-side comparison | Tool | Autonomy levels | What it can change | Approval model | Pricing model | | --- | --- | --- | --- | --- | | PointFive | 1 to 5 | Cloud, data, AI resources (40+ platforms) | Per change, per playbook, or none, set per process | Custom | | Sedai | 1 to 5 | Kubernetes, VMs, serverless, storage, databases | Copilot one-click or Autopilot | Custom; free trial | | CAST AI | 4 to 5 | Kubernetes workloads and nodes | Policies; runbooks need approval | Custom | | Harness | 1, 5 | Commitments, idle non-prod, Kubernetes | Rules; no manual approvals | Quote | | ProsperOps | 5 | Commitments; schedules | Quarterly prepay budget | Share of savings | | Usage.ai | 4 to 5 | Commitments | Copilot or Autopilot per account | Share of savings | | Zesty | 5 | Kubernetes resources, commitments | Guardrails | Usage-based | | Vantage | 1, 3, 5 | Vantage objects; AWS Savings Plans | User permissions | Public plans | | Amnic | 1 to 2 | Nothing | Human executes | Custom | | CloudZero | 1 to 2 | Not documented | Human executes | Custom | | CostAnalyst | 2 | Nothing | Human executes | Public | | AWS, Azure, Google | 1 to 3 | Nothing directly | Human executes | Free or low cost | ## Safety and approvals: the real buying criterion Detection is no longer scarce. What matters is whether you can let a tool act. Check five things: - Read-only discovery. Finding waste should never require write access. PointFive, CAST AI and Amnic document read-only starting points; ask every vendor. - Scoped write permissions. ProsperOps' role cannot stop or terminate resources; CAST AI honors exclusion labels and Pod Disruption Budgets. Ask for the exact permission list. - Approval granularity. Per-change approval is the right start. Per-playbook (standing) approval lets a proven fix run whenever conditions match. No approval should be a choice you make per process, not a vendor default. - Audit trail. Every action should be logged with its approver. Several vendor pages are silent here. - Rollback. Know which changes are reversible. A rightsizing can be undone; a deleted snapshot or a three-year commitment cannot. See [how to make automated remediation safe](https://www.pointfive.co/blog/safe-automated-cloud-remediation) . Also ask whether execution is deterministic: does the model improvise, or run a tested playbook the same way every time? ## Where agents still fall short - Narrow domains. Most Level 5 tools own one lever. Architecture, code and data-platform changes still need humans or broader platforms. - Business context. Agents do not know which idle environment runs a quarterly audit. Owners and exceptions must be taught. - Previews. Azure's and Google's agents are in preview; Vantage's GitHub integration is private preview. - Irreversible actions. Deletions and commitments limit rollback. - Estimates versus results. Verify that a tool reports realized savings against the bill, not projected savings. ## How to choose - Commitments are your biggest lever: ProsperOps, Usage.ai or Harness. - Most spend is Kubernetes: CAST AI, Zesty or Sedai. - You need answers, not action: native assistants, Amnic, CloudZero or CostAnalyst. - You have a backlog of known waste across cloud, data and AI: PointFive, starting with per-change approval. Nubank's platform team used PointFive's API to [optimize 3,000 DynamoDB tables](https://www.pointfive.co/blog/how-nubank-made-usage-optimization-a-boardroom-topic) . For the wider market, see [best cloud cost optimization tools](https://www.pointfive.co/guides/best-cloud-cost-optimization-tools-2026) and [what agentic FinOps means](https://www.pointfive.co/blog/what-is-agentic-finops) . ## FAQ ### What is the best AI agent for cloud cost optimization? It depends on the lever. For commitments, ProsperOps or Usage.ai; for Kubernetes, CAST AI, Zesty or Sedai; for answering questions, your cloud's native assistant. For finding and fixing waste across cloud, data and AI with your choice of approval, PointFive. ### What is the best tool for automated cloud waste remediation? Choose the tool that can execute in your environment under an approval model you control. PointFive runs remediation across 40+ platforms with per-change, per-playbook or no approval. Kubernetes-only estates may prefer CAST AI or Zesty. ### Is it safe to let an AI agent change production infrastructure? Yes, with scoped permissions, approval at first, deterministic execution, logging and rollback. Start with reversible waste. ### Do AWS, Azure and Google have their own cost agents? Yes. Amazon Q Developer and Gemini Cloud Assist answer and recommend; Azure Copilot's Optimization Agent (preview) generates scripts. None executes fixes for you. ### What permissions does a cloud cost agent need? Read-only for discovery. Write permissions only for the changes you want automated, scoped to those resources. ## Methodology and sources We reviewed each vendor's own website, pricing pages and documentation in October 2026 and did not use third-party sites for competitor claims. If something is out of date, tell us at info@pointfive.co and we will correct it. - Sedai: [homepage](https://www.sedai.io/) , [pricing](https://www.sedai.io/pricing) - CAST AI: [homepage](https://cast.ai/) , [pricing](https://cast.ai/pricing/) , [Workload Autoscaler docs](https://docs.cast.ai/docs/workload-autoscaling-overview) - Harness: [Cloud & AI Cost Management](https://www.harness.io/products/cloud-cost-management) , [Cost Management Agent](https://www.harness.io/demo/cost-management-agent) , [pricing](https://www.harness.io/pricing) - ProsperOps: [homepage](https://www.prosperops.com/) , [pricing](https://www.prosperops.com/pricing/) , [Flexera acquisition](https://www.prosperops.com/blog/the-next-chapter/) - Usage.ai: [homepage](https://usage.ai/) - Zesty: [homepage](https://zesty.co/) , [pricing](https://zesty.co/pricing/) - Vantage: [FinOps Agent in console](https://www.vantage.sh/blog/finops-agent-console) , [GitHub integration preview](https://www.vantage.sh/blog/github-finops-agent-preview) , [pricing](https://www.vantage.sh/pricing) - Amnic: [homepage](https://amnic.com/) , [Amnic AI](https://amnic.com/amnic-ai) , [pricing](https://amnic.com/pricing) , [AI agents comparison](https://amnic.com/blogs/top-ai-agent-tools-for-finops) - CloudZero: [homepage](https://www.cloudzero.com/) , [AI getting started](https://docs.cloudzero.com/docs/ai-getting-started) , [MCP server changelog](https://docs.cloudzero.com/changelog/mcp-server-for-agentic-coding-tools) - CostAnalyst: [homepage](https://costanalyst.ai/) - AWS: [Amazon Q Developer cost management](https://docs.aws.amazon.com/cost-management/latest/userguide/ce-q-overview.html) , [Cost Optimization Hub](https://docs.aws.amazon.com/cost-management/latest/userguide/cost-optimization-hub.html) , [Billing and Cost Management MCP server](https://aws.amazon.com/about-aws/whats-new/2025/08/aws-billing-cost-management-mcp-server) - Microsoft: [Azure Copilot Optimization Agent](https://learn.microsoft.com/en-us/azure/copilot/optimization-agent) - Google Cloud: [Gemini Cloud Assist in Cloud Billing](https://docs.cloud.google.com/billing/docs/how-to/gemini/overview) , [Optimize costs with Gemini](https://docs.cloud.google.com/hub/docs/optimize-gemini) ## Related reading [All guides](https://www.pointfive.co/guides) - ### [Best Cloud Cost Optimization Tools (2026): An Honest, Comparison-Driven Guide](https://www.pointfive.co/guides/best-cloud-cost-optimization-tools-2026) June 2, 2026 - ### [Best AWS Cost Optimization Tools (2026): Native vs Third-Party, Compared](https://www.pointfive.co/guides/best-aws-cost-optimization-tools-2026) October 9, 2026 - ### [Best Snowflake Cost Optimization Tools (2026): An Honest, Comparison-Driven Guide](https://www.pointfive.co/guides/best-snowflake-cost-optimization-tools-2026) October 9, 2026 --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.