CloudCEA is not another dashboard. It's an autonomous AI agent that investigates cost anomalies, forecasts demand, and optimizes across AWS, Azure, and GCP.
Cloud waste is rampant. Enterprise organizations spend an average of 30-40% more on infrastructure than necessary — and most don't even know where the waste is coming from.
Backward-looking tools tell you what you spent last month. Bills arrive too late to course-correct, and forecasting remains a guessing game.
73% of enterprises use 2+ cloud providers, yet most tools only optimize within a single vendor. Cross-cloud arbitrage opportunities are completely missed.
Reserved Instances and Savings Plans are purchased once and forgotten. No continuous rebalancing means suboptimal commitment mix and locked-in overspend.
GPU availability is unpredictable. Pricing varies 10x across regions and providers. Demand forecasting for AI workloads requires purpose-built signals.
CloudCEA deploys autonomous AI agents that investigate, analyze, and act — delivering finished work products, not just charts. Think of it as a senior capacity engineer who works 24/7 and gets smarter every week.
An AI agent that builds demand models per workload — ingesting traffic patterns, business signals, seasonal data, and deployment schedules. It produces forecasts with confidence intervals, not just trend lines.
An AI agent that continuously compares workload costs across AWS, Azure, and GCP. When your ML training pipeline is 22% cheaper on another provider, it finds it — with a concrete migration plan and savings estimate.
When your spend spikes 30%, this agent doesn't just flag it. It autonomously investigates billing data, deployment logs, autoscaler events, and traffic — then delivers a complete root-cause report with recommended fixes.
Agents collaborate on complex queries. Ask "What's the cheapest way to handle our Black Friday traffic spike?" and CloudCEA dispatches the Capacity Forecaster, Arbitrage Scanner, and Commitment Optimizer together — assembling a unified recommendation from all inputs.
CloudCEA works across three interaction modes — ambient, review, and exploration — each tuned to a different level of attention.
The AI works while you don't. Savings opportunities surface in Slack. Cost impact analysis appears on PRs. Weekly digests land in your inbox. Value is delivered without ever opening a dashboard.
When you sit down for a weekly review, the dashboard shows finished investigations waiting for approval — sorted by impact. You review the AI's work and approve with one click.
Ask anything in natural language. "Why did our spend spike last Tuesday?" "What happens if we double checkout traffic?" The AI chat is backed by your real data and the full optimization engine.
Start simple, scale deep. Choose your level of integration and unlock capabilities progressively.
Fastest way to get started. No infrastructure changes needed.
Unlock predictive capabilities with metrics integration.
Full-power platform with complete control and automation.
Every feature is powered by specialized AI agents pre-trained on how senior cloud engineers actually think and work.
When spend spikes, the AI agent correlates billing, deployments, autoscaler logs, and traffic — then delivers a finished incident report with root cause and remediation plan.
AI classifies workloads by performance profile, fault tolerance, and cost sensitivity — then optimally allocates them across instance types, commitment vehicles, and providers.
ML models trained on 5+ years of Spot history forecast pricing and interruption rates. The agent designs your fallback chain and retry strategy automatically.
A GitHub Action powered by CloudCEA's AI comments on every PR with cost impact analysis, alternative architectures, and savings suggestions — before code ships.
Ask "What if we migrate to EKS?" or "What do we need for 5x Black Friday traffic?" — the AI produces data-backed projections grounded in your real infrastructure.
The AI continuously analyzes traffic patterns and recommends autoscaler parameter adjustments. It treats scaling as a control theory problem, not a set-and-forget config.
AI-driven analysis of actual vs. requested resources across every pod. Recommends optimal limits and can auto-apply via mutating webhook — with full reasoning for every change.
An AI agent that monitors dev/staging environments, learns your team's patterns, and auto-hibernates idle resources. It knows your Thursday load tests aren't anomalies.
CloudCEA learns your environment — batch job schedules, team policies, deployment patterns. Month 3 is smarter than month 1. This memory is your moat and our switching cost.
Existing tools show you charts and wait. CloudCEA investigates, reasons, and acts.
| Capability | CloudCEA | Cloudability | Spot.io | Native Tools |
|---|---|---|---|---|
| Cost Optimization | ✓ | ✓ | ✓ | ✓ |
| Predictive Demand Modeling | ✓ | − | − | − |
| Cross-Provider Arbitrage | ✓ | ✓ | − | − |
| Real-Time Rebalancing | ✓ | − | ✓ | − |
| GPU/AI Workload Planning | ✓ | − | − | − |
| Business Signal Integration | ✓ | − | − | − |
| Automated Migration Plans | ✓ | ✓ | − | − |
| Developer CI/CD Integration | ✓ | − | − | − |
CloudCEA's agents deliver value through the tools your team already uses — Slack, GitHub, Terraform, CLI. The dashboard is for the CFO. The AI agent is for everyone else.
Connect CloudCEA to the tools your team already uses.
Example: GitHub PR Comment
Connect your cloud account in 5 minutes. CloudCEA's AI agents start finding savings immediately. No credit card required.