A control plane for multi-cloud, not another dashboard.

Govern spend across AWS, Azure and Google Cloud on one Value-Control™ loop — plan it, explain it, act on it, prove it. Every step takes action, not just a report. OCI on the roadmap.

~30%

of cloud spend is wasted on average — idle, oversized or uncommitted. The recommendations exist. Acting on them is the hard part.

Industry benchmark · State of Cloud reports
The problem

Your cloud bill outruns your ability to explain it.

Every team spins resources up; nobody spins them down. Commitments lapse unwatched, non-prod runs all weekend, and savings recommendations pile up in a dashboard no one acts on. By the time finance sees the number, the money is already spent.

Idle & non-prodrunning off-hours+Oversizedover-provisioned+Uncommittedon-demand rates
The Value-Control loop

The same four steps govern every provider

Plan spend before it happens, attribute every dollar, act under policy, and prove the impact — across AWS, Azure and Google Cloud. Each step takes action; it does not just report.

Plan
Approve with cost confidence before spend happens
  • Pre-deploy cost estimation (COSTIX)
  • Cost intelligence in the IDE (FI Pulse)
  • CI gate blocks budget-breakers pre-merge
PRODUCT · ENGG
Explain
Every dollar has an owner
  • Cost-to-serve by app, customer & LOB
  • Automated chargeback / showback
  • In the workflow — Teams & GitHub Copilot
FINANCE · PRODUCT
Act
Bounded before it overruns — governed action
  • Service parking automation (IdleGuard)
  • Commitment (RI / SP) planning
  • Right-sizing & wastage via JIRA / ServiceNow
  • Anomaly detection & RCA
FINANCE · IT EXEC · ENGG
Prove
Impact you can take to the board
  • Realized savings, re-baselined
  • Forecasting & variance alerts
  • Budgets & total cost of ownership
FINANCE
Inside the Plan pillar

Approve with cost confidence, before spend happens

COSTIX designs the workload and prices it across every layer. FI Pulse brings that cost into the editor, and policy gates hold the line in CI.

COSTIX · Solution Architect
pre-deploy · cross-tech
Real-time fraud scoring API · 5k req/s · <100ms p95 · multi-AZ
Generated blueprint
🌐API Gateway + ALB
⚙️EKS · GPU node group (inference)
🗄️Aurora PostgreSQL · feature store
🌊Kinesis · event stream
📦S3 · model artifacts
Cross-tech estimate · monthly
Compute · EKS / GPU$18,400
Data · Aurora + Kinesis$6,200
AI inference$9,100
Storage · S3$480
Estimated cost-to-run$34,180/mo
COSTIX · solution architect

Design the workload, price it before it exists

Describe what you're building; COSTIX acts as your solution architect. It drafts a full solution blueprint, writes the workload spec, and returns a cross-tech cost estimate spanning compute, data, storage and AI — before a line of Terraform is written.

An evidence-grounded, auditable estimate you can put in a design review — not a guess, and not a surprise on next month's invoice.
FI Pulse · IDE + CI

Cost in the editor, policy gates in the pipeline

FI Pulse prices your changes as you write. In CI, policy-driven gates block a merge that breaks a budget or a tagging rule — so cost and governance become a code-review fact, not a billing surprise.

Policies you set: spend ceilings by environment, mandatory tags, approved regions and instance families. The gate holds the line on the rules you set.
main.tf — eks_node_group.gpu
Cost estimate $18,400/mo· 6× g5.2xlarge · on-demand
Terraform
42resource "aws_eks_node_group" "gpu" {
43  instance_types = ["g5.2xlarge"]
44  scaling_config { desired_size = 6 }
45}
Policy gate — blocked
$18,400/mo exceeds the non-prod ceiling of $12,000 · use committed capacity or reduce size
PPolicy: non-prod ≤ $12k/mo · all resources tagged · us-east-1 / us-west-2 only
Inside the Explain pillar

Every dollar has an owner — just ask

Ask FI is your FinOps agent: ask in plain language, get deep analysis and a dashboard to match — and it lives where your teams already work.

Tfinops· Ask FI (MCP) · Microsoft Teams
RS
Ravi S. Platform · 10:12
@Ask FI why did our EKS cost jump 40% last month?
FI
Ask FI agent · 10:12
EKS rose +$42k (+40%). Root cause: 3 new GPU node groups in us-east-1 (team fraud-ml), running 24/7 since Mar 12, on-demand with no commitment. Avg utilization 61%.
Dashboard · EKS cost by team
Built you a dashboard →
GHSame agent in GitHub Copilot & your AI tools · MCP
Ask FI · your FinOps agent

A natural-language agent that analyses and builds

Ask FI is a natural-language FinOps agent. Ask it anything about your spend and it runs the deep analysis — root cause, trends, attribution — then builds the dashboard to match. It's an MCP agent, so it works inside Microsoft Teams, GitHub Copilot and your other AI tools, not just the FinOpsly console.

One agent, everywhere your teams already ask questions — no new console to learn, no ticket to file.
Cost-to-serve & chargeback

By app, by customer, by line of business

Under the agent sits the attribution model: every dollar mapped to an app, customer and LOB, with automated chargeback and showback delivered in the workflow — Teams and GitHub Copilot, not a monthly spreadsheet.

Cost allocation that used to take two weeks, ready in a day — and owned by the team that spent it.
App / LOBThis monthOwner
Fraud ML$148,200fraud-eng
Payments API$92,400payments
Data Platform$61,050data-eng
Customer Portal$38,900web
Inside the Act pillar

Governed action across your cloud spend

Some savings are safe to automate; some belong in your change process. FinOpsly parks non-prod itself, finds the deep wastage native tools miss, and routes every change to your ITSM — while the Commitment Planner models the coverage you buy.

Commitment Planner
Usage-based scenario planning
Compute Savings PlanEC2 Instance SPRDS Reserved Instances
SP targetBalanced · 84%
📅 Jul 7, 2026
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
SP coveredExpected coveredOn demand
Plan typeTermEst. savingsDiscRisk
Compute Savings Plan1yr$50,410/mo22%Moderate
RDS Reserved Instance1yr$9,423/mo20%High
Compute SP · post-fill1yr$11,302/mo22%Low
Rate · Commitment Planner

Model your Savings Plans & Reserved Instances

A usage-based scenario planner. Tune coverage and risk against your forecast, generate ranked plans across Savings Plans and Reserved Instances, and export the purchase plan. See the discount, the monthly saving and the risk before you commit a dollar.

FinOpsly models, you purchase. Commitments aren't reversible, so the modeling happens before you commit — FinOpsly does not buy on your behalf.
Usage · IdleGuard

Park non-prod overnight, automatically

IdleGuard is policy-driven service parking. It powers down your non-production resources over non-working hours and brings them back before the workday — automatically, on a schedule you set. This is the savings action FinOpsly executes directly, because start/stop is safe and reversible by design.

Scope by tag and environment; production is always excluded. Un-parks on schedule, and on demand if someone needs a resource early.
IdleGuard · service parking
policy-driven automation · non-prod
Automation on
34
non-prod resources
$14,200
saved / mo
128h
parked / week
M
T
W
T
F
S
S
Running (work hours)Parked
Schedule · Mon–Fri 8pm–7am + weekends · excludes tag:production · un-parks on demand.
Deep Wastage Optimizerbeyond native cloud recommendations
60% more actionable
💽Orphaned EBS volumes12 unattached · >30d$1,840/mo
⚖️Idle load balancers5 ALBs · 0 healthy targets$920/mo
🗄️Over-provisioned RDSp95 CPU 9% · 4 instances$3,200/mo
🔀Avoidable cross-AZ transferchatty service pair$2,600/mo
📸Stale snapshots>180 days$1,410/mo
One click → raise all 5 as change tickets · $9,970/mo
Usage · Deep Wastage Optimizer

60% more you can actually act on

Native cloud tools surface the obvious. FinOpsly's deep wastage analysis reports 60% more actionable causes than the CSPs flag — orphaned volumes, idle load balancers, over-provisioned databases, avoidable cross-AZ transfer, stale snapshots — each with the root cause and the saving.

And every finding is one click from action: initiate the change flow straight into Jira or ServiceNow. FinOpsly raises the ticket, assigns the owner and tracks it to done — FinOpsly initiates; your team executes.

Large Healthcare Association Unlocked Rapid Savings Through Automated FinOps

Within weeks, FinOpsly helped us uncover and execute savings opportunities we had struggled to act on for months. Rate optimization, commitment planning, and copilot-driven rightsizing finally became easy to operationalize

23%cost savings
through automated rate & commitment optimization
Agentic
Smart rightsizing across AWS and Azure.
Adoption
Natural-language Ask FI driving enterprise adoption
Automation
Always-on optimization with policy control
AzureAWS

Director of Cloud Infrastructure,

Large Healthcare Association

Take control of your cloud bill.

Plan it. Explain it. Act on it. Prove it.