DevOps and platform engineering teams spend 5-10% of their time on capacity planning and cost optimization—time that could be better spent on innovation. Meanwhile, production incidents from misconfigurations don't just impact SLAs and revenue; they drive up costs exponentially.
Today, we're excited to introduce StackOptimizer, the newest addition to StackGen's Autonomous Infrastructure Platform. This intelligent AI agent moves beyond simple rightsizing to deliver autonomous, SLO-driven optimization that learns from your infrastructure patterns and prevents cost inefficiencies before they're deployed.
Walkthrough of StackOptimizer
The Problem with Traditional Cost Optimization
Current cloud cost optimization tools offer reactive, surface-level recommendations in a vacuum. They lack contextual understanding of your application's architecture, performance characteristics, and the relationship between infrastructure and application code. This leads to several critical problems:
- Generic recommendations that ignore application-specific needs, potentially degrading performance if applied blindly
- Manual implementation requiring Infrastructure as Code (IaC) changes that create more toil for already stretched teams
- Reactive approach that identifies cost-saving opportunities after they appear, rather than preventing inefficiencies upfront
- No learning capability to make smarter, preventative recommendations based on past incidents and changes
Meet StackOptimizer: Self-Optimizing Infrastructure
StackOptimizer represents a fundamental shift from reactive cost management to proactive, intelligent optimization. As part of StackGen's AI Agent Layer, it leverages our deep understanding of application topology, Infrastructure as Code, and the relationship between infrastructure and application performance to deliver contextual, automated optimization.
SLO-Driven Architecture Optimization
StackOptimizer's core differentiator is its ability to take your user-defined goals for performance and budget and recommend architectural changes that meet both objectives.
For example, when you define an SLO requiring P99 latency under 200ms with a monthly budget of $5,000, StackOptimizer analyzes your current architecture, identifies bottlenecks, and proposes specific optimizations—like consolidating cross-AZ database traffic or rightsizing compute instances—while ensuring performance requirements are maintained.
Proactive Prevention During Development
Rather than waiting for cost issues to appear in production, StackOptimizer analyzes Infrastructure as Code during the pull request process. When a developer provisions a new microservice with an oversized compute instance, StackOptimizer automatically flags it with context:
"This instance type is 40% more expensive than typically required for a service with this performance profile. Based on analysis of 5 similar services, I recommend using t3.medium instead, which would have no performance impact."
Continuous Learning from Incidents
StackOptimizer works with StackScribe (our knowledge management agent) to learn from every deployment and incident. When StackHealer resolves a performance issue by scaling up a service, StackOptimizer analyzes the event context and suggests permanent, system-wide improvements:
"I've learned that services tagged 'critical-service' often require more memory to avoid performance degradation. I suggest updating the StackBuilder template for these services to use a default memory limit of 256MB to prevent similar incidents."
This creates a feedback loop where your infrastructure becomes more efficient over time, propagating learnings across your entire platform.
StackOptimizer Workflow Example
Let's walk through how StackOptimizer works in practice. A platform engineering team noticed high cross-AZ data transfer costs for their analytics service—$2,000 monthly and growing.
Traditional tools would simply flag the high costs. StackOptimizer took a different approach:
1. Cost Anomaly Detection with Deep Analysis: StackOptimizer automatically detects budget overruns and immediately identifies the root cause through architectural analysis. In this case: analytics service in us-east-1b querying database in us-east-1a, causing $8,800/month in cross-AZ data transfer costs (35% over budget).
2. Policy-Aware Architecture Optimization: AI generates a policy-compliant solution that balances cost, performance, and availability. Recommendation: Create read-replica in us-east-1b for 95% read-only queries, maintaining High Availability policy compliance while eliminating expensive cross-AZ traffic.
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3.Compatibility & Performance Validation: Code-level analysis confirms solution feasibility: 15-minute data freshness requirement vs. <1 minute replication lag (15x buffer), db.r5.large instance with 50% headroom for traffic spikes, and comprehensive rollback protection for zero-risk deployment.
4.Zero-Downtime Deployment Execution: Risk-minimized deployment: Phase 1 provisions new read-replica (~15 minutes, zero production impact), Phase 2 updates service configuration (~2 minutes, minimal connection reset). Automated rollback triggers if any step fails.
5. 60-Minute Monitoring & Optimization Confirmation: Comprehensive validation tracking cost reduction (cross-AZ transfer → near $0), performance maintenance (P99 latency within SLO), and reliability metrics (zero DB errors). Final result: $3,200/month savings (30% cost reduction), moving from 35% over budget to 5% under budget.
Part of the Autonomous Infrastructure Vision
StackOptimizer operates within StackGen's broader AI Agent Layer, working alongside agents for infrastructure creation (StackBuilder), governance (StackGuard), healing (StackHealer), and drift detection (StackAnchor). This coordinated approach means your infrastructure doesn't just deploy and maintain itself—it continuously optimizes itself.
The agent integrates seamlessly with your existing DevOps workflow through the StackGen Agent Command Center, providing optimization recommendations that can be automatically implemented with appropriate approvals and safeguards.
What's Next
StackOptimizer will be available in Q4 2025 as part of StackGen's Autonomous Infrastructure Platform. The initial release focuses on SLO-driven analysis and automated, policy-compliant remediation suggestions with human-in-the-loop approval for all actions.
In upcoming releases, we'll be adding advanced governance enforcement suggestions, expanded learning capabilities from StackScribe integration, and enhanced autonomous actions for trusted optimization patterns.
Ready to move beyond reactive cost management to proactive, intelligent infrastructure optimization? Schedule a demo to see StackOptimizer in action.
StackOptimizer is part of StackGen's Autonomous Infrastructure Platform™—AI agents that build and manage your cloud infrastructure. Learn more about our full agent ecosystem at stackgen.com.
