FinOps That Acts,
Not Just Advises
GPU and inference are the new cloud bill. Agents watch cloud and model spend, find the savings your provider misses, and execute them - with a human on every high-impact change.
30+
Happy Clients
$2M +
Cloud Costs Saved
10+
SOC 2 & HIPAA Ready
10+
Countries Served
Why Cloud Costs Spiral Out of Control
The complexity of modern cloud and AI infrastructure leaves waste that dashboards surface but nobody has time to fix.
Cloud & GPU Waste
Idle instances, over-provisioned nodes and GPUs running at 20% utilization.
Lack of Visibility
No clear view of who is spending what and where - across cloud and inference.
Unpredictable Billing
Surprise invoices that break budget forecasts.
Complex Multi-cloud
Fragmented cost data across AWS, Azure, and GCP.
Recommendations Nobody Acts On
Provider recommenders produce lists. Tickets pile up, sprints slip, and the savings expire.
Poor Tagging
Untagged resources making attribution impossible.
Advice Doesn't Save Money. Actions Do.
Your provider's recommender stops at a list. Our agents keep going every day.
Traditional FinOps
Provider recommender + monthly review
Skybyte AI FinOps
Autonomous agents, human-approved
Cadence
Traditional FinOps
Monthly cost reviews and a quarterly cleanup sprint.
Skybyte AI FinOps
Continuous - agents act as workloads change, every hour.
Signal
Traditional FinOps
14-day CPU averages, one resource at a time.
Skybyte AI FinOps
Workload-aware: traffic, SLOs, commitments and context.
Scope
Traditional FinOps
Compute right-sizing and a few idle-resource flags.
Skybyte AI FinOps
Compute, Kubernetes, storage, commitments, GPU and tokens.
Output
Traditional FinOps
A recommendations list someone has to triage.
Skybyte AI FinOps
A ranked plan with dollar impact, risk score and rollback.
Execution
Traditional FinOps
Ticket, backlog, maybe next quarter.
Skybyte AI FinOps
Agents execute in guardrails; you approve high-impact changes.
Proof
Traditional FinOps
Savings estimated, rarely reconciled.
Skybyte AI FinOps
Verified against the next invoice - and re-optimized if it drifts.
Savings addressed · Share of a typical cloud bill
Recommenders stop at right-sizing. Agents reach the rest of the bill.
How much of each cost lever is actually captured - a flagged saving is not a saving.
Compute right-sizing
partial
workload-aware
Idle & scheduling
flags only
auto-scheduled
Commitments (RI / SP)
static advice
forecast-driven
Kubernetes & storage
not covered
bin-packing + tiers
GPU & inference
not covered
routing + caching
Provider recommender
Skybyte agents
Visualizing the AI FinOps Journey
Agents cut spend once, then hold it there as workloads change.
Immediate Waste Elimination
20% saved in the first 30 days.
Unit Cost Optimization
On-demand shifted to commitment-based pricing.
Savings That Don't Decay
Month-six savings look like month-one.
One Continuously Optimized Bill
Each agent owns a slice of your cloud economics and reports to the same approval queue.
Cost Discovery Agent
Maps every dollar across cloud accounts and inference endpoints, and finds idle spend in hours.
Multi-cloud + GPU inventory
Waste report in 48 hours
Savings roadmap
Rightsizing & Scheduling Agent
Matches instances, nodes and pods to real demand, and shuts down what nobody uses off-hours.
Workload-aware rightsizing
Off-hours scheduling
Spot & Karpenter strategy
Commitment Agent
Buys and rebalances reservations and savings plans against a live forecast.
RI / Savings Plan coverage
Forecast-driven purchasing
Exchange & rebalance
GPU & Inference Agent
Right-sizes GPU fleets and routes, caches and batches model calls so token spend tracks usage.
Model routing & caching
GPU utilization
Token cost strategy
How Our Agents Work
A closed loop that runs every day - not a project that ends after the audit.
Observe
Agents ingest billing, metrics, Kubernetes and inference telemetry across every account and cloud.
Reason
Model each workload, forecast demand and price every option - commitments and architecture included.
Plan
Rank actions by dollar impact and risk, each with a rollback path and an owner.
Human gate
Act
Low-risk changes execute inside guardrails. High-impact changes wait for a human to approve.
Verify
Reconcile every action against the next invoice, learn, and feed the result back into Observe.
Tools We Use
Leveraging the latest tools and technologies to build efficient, scalable, and future-ready solutions.
AWS Cost Explorer
Azure Cost Management
GCP Billing
Kubecost
Opencost
KarpenterWork that ships and scales
How we've helped teams put AI and cloud to work in production.
AWS
EKS / KUBERNETES
MICROSERVICES
FINOPS
DEVSECOPS
86+ Services Migrated to AWS EKS in 8 Weeks - Zero Downtime, Zero Incidents
GCP
SOC 2 TYPE 1
GKE
TERRAFORM
DEVSECOPS
SOC 2 Type 1 in 30 Days - Multi-Region AI Product on GCP, 50+ Services Hardened
AZURE
FINOPS
AKS
COSMOS DB
COST OPTIMIZATION
55% Azure Cost Reduction in 11 Weeks - $137K/Month Saved on a $250K/Month SaaS Spend
Ready to build AI-first?
Join the teams shipping production AI with Skybyte - from first prototype to reliable, governed scale.