Now seeking enterprise design partners
The intelligence layer between signals and actions
Optillence helps enterprises move from observability to governed optimization decisions — across application, infrastructure, cloud, and cost.
The challenge
Enterprises have dashboards. They still lack decisions.
Most organizations already have observability, monitoring, AIOps, and cloud dashboards. But engineering teams still spend hours connecting signals, identifying root causes, validating safe changes, and deciding what to optimize.
Alert fatigue
Teams drown in signals from observability, AIOps, and cloud tools — with little clarity on what actually matters.
Slow root cause analysis
Engineers spend hours correlating metrics, logs, traces, and change events before they can act with confidence.
Over-provisioned cloud resources
Kubernetes clusters, JVM heaps, and compute tiers are sized for worst case — driving persistent infrastructure waste.
Manual tuning decisions
Performance and cost optimizations depend on tribal knowledge, spreadsheets, and one-off runbooks instead of governed workflows.
Platform vision
From reactive operations to autonomous optimization
Monitor
Ingest signals
Analyze
Cross-stack reasoning
Recommend
Ranked actions
Approve
Human-in-the-loop
Optimize
Governed execution
Governed optimization — not blind auto-remediation
The platform does not blindly change production systems. It surfaces explainable recommendations, scores confidence, enforces guardrails, and requires human approval before any action is taken.
- Explainable recommendations with confidence scoring
- Policy guardrails and safe-change validation
- Full audit trail for every recommendation
- Human approval before any remediation
Platform preview
Governed recommendations — not blind automation
Optillence ranks optimization opportunities across performance, reliability, and cost — then routes safe changes through human approval and auditable remediation workflows.
Optimization recommendation
CPU requests overprovisioned
- Resource
- booking-api
- Confidence
- 92%
- Expected savings
- ~$12.4k / year
- Risk
- Low
Finding: CPU requests set at 2 cores; p95 utilization under 35% for 14 days. Recommend 1.2 cores with HPA headroom.
Illustrative example — design partner preview. Not customer data.
Governed workflow
Recommendation
Ranked with confidence & impact
Human approval
Policy guardrails enforced
Remediation
Git MR or change ticket
Capabilities
Optimization across performance, reliability, and cost
A unified decision layer for hybrid platform teams — cloud, on-prem, and everything in between.
Cloud, on-prem, and Kubernetes optimization
Right-size workloads across Azure, AWS, GCP, and on-prem estates — Kubernetes, VMs, and bare metal. Tune requests, limits, autoscaling, and capacity policies to actual demand and SLOs.
JVM and application tuning
Analyze GC logs, thread dumps, and runtime configs to recommend safe heap, pool, and concurrency settings.
Cloud cost rightsizing
Identify over-provisioned compute, storage, and memory across cloud, on-prem, and container estates.
Root cause analysis acceleration
Correlate metrics, ML insights, and knowledge base context to shorten time-to-diagnosis.
Capacity forecasting
Predict traffic and resource demand to inform scaling decisions before incidents occur.
Guarded remediation workflows
Route approved changes through PR-based Git workflows with audit trails and rollback paths.
How it works
Three steps to governed optimization
Connect signals
Integrate metrics, logs, traces, CI/CD pipelines, and cloud platforms — plus your team's runbooks and performance knowledge.
Reason across the stack
ML models and LLM reasoning correlate performance, reliability, and cost data to surface ranked optimization opportunities.
Recommend with guardrails
Receive explainable actions with confidence scores, impact estimates, and approval workflows before anything reaches production.
Why Optillence
Decision intelligence — not another optimization engine
Most optimization and AIOps solutions excel at infrastructure rightsizing, cloud autoscaling, and automated tuning. Optillence addresses a different gap: how enterprises decide what to optimize, when, and with what guardrails — across the full stack.
Typical solution providers
Strong at capacity, cost, and runtime decisions — often with less depth in application behavior, release causality, and governed change workflows.
Optillence
The intelligence layer between signals and actions — how performance engineering leaders actually run optimization programs.
Example: elevated API latency
Illustrative — how decision depth differs
Typical approach
- · Scale CPU or replica count
- · Autoscale workload based on utilization
- · Apply generic JVM or pool tuning
May mask a root cause such as a release regression or missing index.
Optillence response
- · Correlate latency spike with recent release + query plan change
- · Recommend index creation — not CPU increase
- · Expected impact: ~35% latency reduction
- · Confidence: 94% · Risk: Low
- · Route through human approval before change
Decision intelligence
Not just what to change — why, with what confidence, and under what governance. Ranked decisions, not raw alerts.
Optimization knowledge graph
Reasons across application code, JVM, database, Kubernetes, cloud cost, releases, and workload patterns — not a single layer.
Enterprise governance
Confidence and risk scoring, explainability, human approval, change guardrails, and a full audit trail — built for regulated industries.
Cross-stack remediation
One engine connects symptoms to root cause to safe action — index creation, pool tuning, rightsizing, or release rollback.
Performance engineering depth
Grounded in how senior performance engineers actually work — not generic “scale CPU” heuristics.
We don't claim a better algorithm. We help enterprises govern optimization decisions the way senior performance engineers and platform teams already think — with evidence, confidence, and approval.
Design partners
Seeking enterprise design partners
We are partnering with a small group of engineering and platform teams to shape the next generation of autonomous optimization. Join us as a co-creator — not just a customer.
Ideal design partners:
- Running Kubernetes, OpenShift, EKS, or AKS
- Managing JVM, API, database, or cloud performance challenges
- Looking to reduce incident resolution time and infrastructure waste
- Willing to co-create use cases over a 60–90 day pilot
Leadership
Built from enterprise performance engineering experience
Founded by a technology leader with nearly two decades of experience across Performance Engineering, SRE, Observability, Cloud Automation, DevOps, Non-Functional Testing, and enterprise-scale platform optimization.
Get in touch
Start a design partner conversation
We're looking for 3–5 enterprise teams to co-create the platform. Share your context and we'll schedule a focused discussion.