Kubernetes Intelligence Platform
Stop guessing inside Kubernetes.
Optimize with Confidence.
Stop guessing inside Kubernetes.
Optimize with Confidence.
The problem
Is this you?
Are you seeing Kubernetes as a growing cost line—but not knowing what’s actually driving it?
Are you seeing Kubernetes as a growing cost line—but not knowing what’s actually driving it?
Do you only see one Kubernetes line item—with no clear view into where the waste actually lives?
Do you only see one Kubernetes line item—with no clear view into where the waste actually lives?
Do your FinOps tools stop at the namespace level, while your engineers operate at the workload level?
Do your FinOps tools stop at the namespace level, while your engineers operate at the workload level?
Do your engineers push back on cost recommendations because they don’t trust the data?
Do your engineers push back on cost recommendations because they don’t trust the data?
Your Kubernetes problem isn't cost.
It's intelligence.
Your Kubernetes problem isn't cost.
It's intelligence.
Your Kubernetes problem isn't cost.
It's intelligence.
The Solution
Four dimensions of intelligence.
One platform.
Nothing else in the market combines all four. Multidimensionality earns engineering trust — and trust unlocks action
Cost Intelligence
Contract-accurate Kubernetes cost tied to your actual negotiated SKU-level pricing. Every cluster, every workload, in real time.
SKU-level contract pricing — not estimates
Workload-level attribution across all object types
Real-time — not delayed billing data
Performance Intelligence
CPU, memory, networking, and saturation signals across your entire fleet. The context that makes cost recommendations defensible.
P99, min/max, full time-series metrics
Pod-level breakdown and outlier detection
Service map that predicts reliability risk
Reliability Intelligence
OOM kills, crash backoff loops, pod health, and cluster saturation signals. Rightsizing that accounts for production behavior.
OOM events and crash loop detection
Pod scheduling failures and evictions
Reliability risks correlated with cost impact
Configuration Governance
Policy guardrails and configuration drift detection across clusters and teams. Standards that travel with the platform.
Drift detection across open source components
Kubernetes best practice enforcement
Custom rules for internal platform components
The Problem
Other tools estimate.
Other tools estimate.
RealTheory calculates.
RealTheory calculates.
RealTheory calculates cost from Kubernetes telemetry directly — matched to your actual negotiated SKU-level pricing, in real time.
How other tools work
Cloud Bill
Estimation
Approximation
With RealTheory
K8s Telemetry
Contract Pricing
Calculated Truth
VS
How other tools work
Cloud Bill
Estimation
Approximation
With RealTheory
K8s Telemetry
Contract Pricing
Calculated Truth
VS
How it works
Truth. Context. Action.
Layer by layer.
From shared data truth to shipped changes — that is how Optimize with Confidence™ works.
IaC Configuration
IaC configurations from rightsizing recommendations
PR-ready changes
PR-ready changes for overprovisioned workloads
Guardrails
Guardrails for resource request standards
Jira integration
Jira integration: insights become trackable tickets
Truth — Cost Fidelity
Real-time Kubernetes telemetry matched to your contract pricing. The data your FinOps team can present to the board.
Context — Performance + Reliability
Cost decisions with full operational context. Not cost-only. The complete picture engineers trust.
Action — Artifacts + Workflow
IaC, PR-ready changes, guardrails — reviewed by engineers, shipped on their terms.
How it works
Truth. Context. Action.
Layer by layer.
From shared data truth to shipped changes — that is how Optimize with Confidence™ works.
IaC Configuration
IaC configurations from rightsizing recommendations
PR-ready changes
PR-ready changes for overprovisioned workloads
Guardrails
Guardrails for resource request standards
Jira integration
Jira integration: insights become trackable tickets
Truth — Cost Fidelity
Real-time Kubernetes telemetry matched to your contract pricing. The data your FinOps team can present to the board.
Context — Performance + Reliability
Cost decisions with full operational context. Not cost-only. The complete picture engineers trust.
Action — Artifacts + Workflow
IaC, PR-ready changes, guardrails — reviewed by engineers, shipped on their terms.
The AI Copilot
Meet KIP.
Meet KIP.
Your Kubernetes Intelligence Copilot.
Your Kubernetes Intelligence Copilot.
KIP is not a chatbot on top of your data. It is your data — made conversational.
Every answer is grounded in real-time telemetry, scoped to what you're authorized to see.
The Results
Built for the world's most complex
Built for the world's most complex
Built for the world's most complex
Kubernetes environments.
Kubernetes environments.
Kubernetes environments.
Multi-cloud fleet
AWS, Azure, GCP, bare metal — every cluster in one unified view.
API-first
Every capability accessible via API. Pull intelligence into your existing dashboards.
Least privilege RBAC
Every user, every view, every KIP response — scoped to your authorization boundary.
The Results
Real outcomes for
Real outcomes for
enterprise Kubernetes fleets.
enterprise Kubernetes fleets.
RealTheory calculates cost from Kubernetes telementry directly - matched to your actual negotiated SKU-level pricing, in real time.
RealTheory calculates cost from Kubernetes telementry directly - matched to your actual negotiated SKU-level pricing, in real time.
40-60
40-60
%
%
Reduction in Kubernetes infrastructure waste
Up to 40–60% reduction typical for enterprise deployments where K8s resources are consistently idle.
3-5
3-5
mo
mo
Full platform ROI
Full platform ROI
Tool investment returned through realized savings within one quarter — typical for enterprise deployments.
Tool investment returned through realized savings within one quarter — typical for enterprise deployments.
1
1
x
x
Single pane of glass
Fleet-wide visibility across every cluster, every cloud, every workload. No context switching.
Optimization that actually ships
Optimization that actually ships
Artifacts generated, tickets created, changes reviewed by engineers. Not recommendations that sit in a backlog.
Where we're going
Intelligence today.
Intelligence today.
Intelligence today.
Governed automation tomorrow.
Governed automation tomorrow.
Governed automation tomorrow.
RealTheory encodes enterprise policy into the intelligence layer. KIP operates inside those guardrails. Engineers review, approve, and ship — on their terms. Human-in-the-loop by design. Always.
RealTheory encodes enterprise policy into the intelligence layer. KIP operates inside those guardrails. Engineers review, approve, and ship — on their terms. Human-in-the-loop by design. Always.
RealTheory encodes enterprise policy into the intelligence layer. KIP operates inside those guardrails. Engineers review, approve, and ship — on their terms. Human-in-the-loop by design. Always.
Four intelligence dimensions
Four dimensions of intelligence.
One platform.
Nothing else in the market combines all four. Multidimensionality earns engineering trust — and trust unlocks action
Cost Intelligence
Contract-accurate Kubernetes cost tied to your actual negotiated SKU-level pricing. Every cluster, every workload, in real time.
SKU-level contract pricing — not estimates
Workload-level attribution across all object types
Real-time — not delayed billing data
Performance Intelligence
CPU, memory, networking, and saturation signals across your entire fleet. The context that makes cost recommendations defensible.
P99, min/max, full time-series metrics
Pod-level breakdown and outlier detection
Service map that predicts reliability risk
Reliability Intelligence
OOM kills, crash backoff loops, pod health, and cluster saturation signals. Rightsizing that accounts for production behavior.
OOM events and crash loop detection
Pod scheduling failures and evictions
Reliability risks correlated with cost impact
Configuration Governance
Policy guardrails and configuration drift detection across clusters and teams. Standards that travel with the platform.
Drift detection across open source components
Kubernetes best practice enforcement
Custom rules for internal platform components