Now seeking enterprise design partners
Tune First. Right-Size Next. Scale Last.
Your tools default to adding capacity. Optillence tunes the code, gives back what you over-bought, and scales only when the evidence says to.
Enterprise evidence boundary
Your engineering evidence stays within your control
Optillence is designed for customer-controlled deployment in private cloud, customer cloud, or on-premises environments — so sensitive repository code, production diagnostics, Internal knowledge artifacts, operational artifacts, and engineering context can remain within your enterprise boundary.
We are working with design partners to validate the right deployment and data-governance model for sensitive engineering evidence, including where AI inference and vector data are processed.
Optillence works alongside your observability, ITSM, and engineering platforms — without requiring sensitive evidence to be sent to an external AI service.
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 governed optimization
Automation can increase as confidence, policies, and approval models mature — without bypassing the humans accountable for production change.
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
The cheapest safe change, in evidence order
One view for the service, one for the application — and they are linked. The saving is withheld because the service is breaching, so the code fix comes first. Every item opens into the measurement that ranked it.
Top 5 for one service
checkout
p95 38.3s · CPU peak 0.6% · 7-day window
- Latency SLA breach — start hereevidencedExplainApply Fix
Metrics · 7 days
p95 38.3s while CPU sits at 0.2% — the time is spent waiting on downstream, not computing.
- N+1 query in prepOrderItems loopevidencedExplainApply Fix
Repo scan · src/checkout/main.go
- Synchronous DB queries block threadsevidencedExplainApply Fix
Repo scan · src/checkout/main.go
- Fix the pool before tomorrow’s peakevidencedExplainApply Fix
ML analysis · 30-day pattern
- Rightsize memory limit 320Mi → 240Misafe reductionExplain
ML analysis · 7 days · peak 15.4% against your 80% target
Load is coming and the answer is still the code. Scaling is not excluded — it is placed where the evidence puts it.
Ranked for the application
ECommerce
4 × m5.large · 16 services · 7-day rollup
- Reduce node count 4 → 2Explain
$147.17 / mo theoretical — on-demand list
Peak use over 7 days is 14% of what is provisioned.
Held — checkout is breaching its latency target.
- Change node type to t4g.largeExplain
a further $42 / mo
Busy in bursts, not continuously — and the memory-to-CPU balance is already right, so only the size changes.
- No HPA or PDB on the node grouparchitecture
A fixed replica count risks outage during spikes or maintenance.
Checked before any cut
N-1 redundancy · no reduction during peak window · Tier-1 headroom retained
The money is found and then withheld, because the workload says now is the wrong time. Fix the performance findings first — the cut becomes safe after.
Governed workflow
Recommendation
Ranked by evidence, not by size
Human approval
Policy guardrails enforced
Pull request
Opened for review — never merged
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, source code, knowledge artifacts and your team's runbooks.
Reason across the stack
Deterministic correlation combines service topology, deployment changes, repository evidence, runtime signals, and organizational knowledge. AI translates the resulting evidence into clear explanations, recommended actions, and investigation guidance.
Recommend with guardrails
Receive explainable actions with confidence scores, impact estimates, and approval workflows before anything reaches production.
Why Optillence
Anyone can add capacity. Knowing when not to is the product.
Observability tells you a service is slow. FinOps tells you a cluster is oversized. Neither reads your code, and neither will tell you to leave money on the table. Optillence ranks the code fix above the capacity fix — and holds a saving back when the workload says now is the wrong time.
How Optillence fits your stack
Optillence works alongside your observability, ITSM, and engineering platforms — turning their signals, plus evidence outside those tools, into governed mitigation and remediation decisions.
| Capability | Other optimization tools | Observability platforms | Optillence |
|---|---|---|---|
| Customer-controlled deployment and evidence boundary | Often vendor-managed or cloud-first | Often SaaS or managed deployment | Designed for customer cloud, private cloud, or on-premises — validated with design partners |
| Incident evidence intake | Limited | Strong within native telemetry | Cross-stack: APM, internal knowledge artifacts, Linux, load balancer, queues, middleware, network, code and deployment context |
| Next-best evidence guidance | Limited | Emerging | Guided requests for the artifact or metric needed to reduce uncertainty |
| Code-to-production decision trace | Moderate | Growing | Repository, deployment, runtime evidence, standards, and remediation linked in one workflow |
| Validated decision memory | Limited | Moderate | Human-approved incident outcomes and proven remediation patterns |
| Governed human approval | Moderate | Moderate | Built into the operating model — architect and SRE approval before action |
| PE / SRE / Architect expertise encoded | Limited | Limited | Performance engineering, reliability, and architecture guardrails as reusable logic |
Illustrative landscape view — strengths vary by deployment and maturity. Optillence emphasizes governed, evidence-backed decisions over unattended automation.
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 governed optimization. Join us as a co-creator — not just a customer.
What a design-partner pilot proves in 60–90 days:
Select one high-value journey
API latency, Kubernetes rightsizing, JVM tuning, Oracle bottleneck analysis, or another diagnostic or optimization workflow your team already struggles to decide on.
Connect existing telemetry and a limited evidence set
Your current APM plus deployment history, code or configuration context, and selected operational artifacts — co-scoped with your platform and SRE teams.
Prove measurable decision value
Faster diagnosis, fewer blind changes, stronger remediation confidence, or measurable cost and performance improvement over 60–90 days.
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.