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Cloud Cost Optimization and FinOps for AWS, Azure and Google Cloud
Cloud cost optimization and FinOps is the combined practice of removing waste from a running cloud estate, buying the remaining capacity at the best rate, and making every team accountable for what it spends. NTSPL delivers twelve focused engagements across AWS, Azure and Google Cloud, from a bill audit through rightsizing, commitments, allocation and Kubernetes and AI FinOps to standing governance.
Cloud Cost Optimization & FinOps services
Cloud Cost Optimization & FinOps at a Glance
Indian companies now spend more on cloud than on the data centres it replaced, and most cannot explain the bill line by line. Waste builds up in predictable places: oversized instances, orphaned disks, environments running all weekend, snapshots kept forever, shared clusters nobody allocates, GPU capacity sized for a peak. Rate optimisation lags because nobody owns commitments, and accountability is weak because a third or more of spend has no clear owner. The result is cloud growth that outpaces business growth, year after year.
NTSPL splits the problem into twelve engagements that can be bought individually or sequenced as a programme. Each one has its own evidence method, its own change controls and its own billing-verified savings measure, so finance can trust the numbers and engineering can trust the changes. Most clients start with a Cloud Cost Assessment, execute the highest-value workstreams in the following two quarters, and then embed Continuous FinOps Governance so the savings hold.
Four Workstreams for Lower, Accountable Cloud Spend
Visibility and accountability
Establish what the bill really contains and who owns each part of it. A forensic audit of billing exports, an enforced tag taxonomy with shared-cost split rules, and forecast-based budgets with segmented anomaly detection give every team its own number and an early warning when it moves. Finance and engineering then argue about actions, not numbers.
Cloud Cost Assessment
Audit of your running AWS, Azure or Google Cloud bill: waste, commitment coverage and allocation gaps, priced into an INR savings register in 4-6 weeks.
Tagging & Cost Allocation
Design tag taxonomies, enforce them with policy, and split shared cloud costs across products, BUs and tenants on AWS, Azure and GCP for showback.
Budgets & Cost Anomaly Management
Owner-level budgets, ML anomaly monitors and triage runbooks on AWS, Azure and Google Cloud that catch runaway spend in hours, not on the invoice.
Compute and environment efficiency
Remove waste in what is running. Rightsize instances and databases on memory-inclusive evidence, quarantine and delete orphaned assets, schedule non-production around working hours, and bring shared Kubernetes clusters under namespace allocation, request rightsizing, bin-packing and Spot node pools. Every change carries a rollback path.
Rightsizing
Evidence-based rightsizing of EC2, Azure VMs, Compute Engine, managed databases and volumes using p95 metrics, Arm migration and staged change windows.
Idle Resource Optimization
Find and safely remove unattached disks, idle VMs, unused IPs, empty load balancers and stale snapshots on AWS, Azure and GCP, quarantine first.
Dev Test Scheduling
Schedule non-production VMs, databases and Kubernetes clusters on AWS, Azure and GCP around IST working hours, with on-demand wake-up and owner overrides.
Kubernetes FinOps
Allocate Kubernetes cost by namespace and team, rightsize pod requests, improve bin-packing and add Spot node pools on EKS, AKS and GKE with OpenCost.
Data, storage and AI spend
Control the cost lines that grow fastest. Tier object storage and retune volumes on access evidence, redesign backup retention around actual obligations, and attribute lakehouse, GPU and model-API spend to use cases with capacity plans that fund AI growth deliberately.
Storage Optimization
Cut object, block and file storage cost on AWS, Azure and GCP with access-pattern tiering, gp3 and Premium SSD v2 retuning, lifecycle rules and dedupe.
Backup Cost Optimization
Reduce AWS Backup, Azure Backup and Google Cloud backup spend by redesigning retention tiers, snapshot chains, vault redundancy and duplicate copies.
Data & AI FinOps
Showback for Databricks, Fabric, BigQuery and model APIs, plus GPU capacity and commitment planning, so data and AI spend has owners and unit costs.
Rates and governance
Pay less for the capacity that remains and keep it that way. Laddered Savings Plans, reservations and CUDs sized to post-optimisation baselines, plus a FinOps council, policy set, KPI tree and executive review cadence that stop savings eroding. Each tranche and policy has a named owner.
Commitment & Discount Planning
Plan AWS Savings Plans, Azure reservations and savings plans, and Google Cloud CUDs with laddered purchases, coverage targets and an INR risk model.
Continuous FinOps Governance
Set up a FinOps practice on the FinOps Framework: council, personas, cost policies, KPIs, forecasting and executive reviews across AWS, Azure and GCP.
Results a FinOps Programme Should Deliver
How NTSPL Delivers FinOps on Each Hyperscaler
On AWS, NTSPL works from CUR 2.0 in Athena, Cost Optimization Hub and Compute Optimizer for evidence, Organizations tag policies and SCPs for enforcement, Cost Categories for split rules, Cost Anomaly Detection and Budgets for guardrails, and Savings Plans for rates. Analysis runs in your own Mumbai or Hyderabad account.
On Azure, NTSPL uses Cost Management exports, including FOCUS format, at billing-account scope, Azure Advisor and Resource Graph for evidence, Azure Policy for tag inheritance and enforcement, cost allocation rules for shared spend, and a combination of reservations and savings plan for compute, often starting from Microsoft's FinOps toolkit.
On Google Cloud, NTSPL builds on the detailed billing export in BigQuery, Recommender and the FinOps hub for opportunities, labels and organisation policies for allocation, GKE cost allocation for clusters, budgets with Pub/Sub automation for alerts, and resource-based and spend-based CUDs for rates, with datasets kept in Mumbai or Delhi.
Cloud Cost Optimization & FinOps in Practice: Industry Scenarios
Pre-Series-E SaaS company finds ₹58 lakh a month of waste before its board review
E-grocery platform moves 410 EC2 instances to Graviton and cuts compute cost 33%
IT services firm reclaims ₹22 lakh a month from 160 closed client-project subscriptions
Regional news app halves Cloud Storage cost on 1.9 PB of reader video and logs
Digital lending platform cuts snapshot spend 61% while keeping five-year loan records
Streaming service rebuilds its commitment portfolio and lifts savings rate to 31%
Cloud Cost Optimization & FinOps: Frequently Asked Questions
Where should a cloud cost programme start?
With a Cloud Cost Assessment in most cases. It reconciles billing data to invoices, measures waste, commitment coverage and allocation gaps, and produces a priced register. That register decides which of the other engagements to run first, so effort goes where the money is rather than where the tools happen to point.
Can we buy individual engagements instead of a full programme?
Yes. Each of the twelve engagements has its own scope, deliverables and savings measure. Clients often buy two or three, such as rightsizing, scheduling and commitments, and run the rest internally. The only firm sequencing rule is to remove waste before buying long-term commitments.
How does NTSPL charge for cost optimisation work?
Fixed fees for assessments and governance design, and either fixed fees or a share of billing-verified savings for execution engagements such as rightsizing, storage and commitments. Savings are counted against agreed rules: run-rate reduction on the specific resources changed, net of any new commitment cost.
Does NTSPL need access to production systems?
Assessments use read-only billing, monitoring and inventory roles. Execution work changes resources through your change process and, where infrastructure is code-managed, through pull requests to your Terraform or Bicep repositories. NTSPL does not make console changes to production outside agreed windows.
Is cost data kept within India?
Yes. Billing exports, analysis datasets and dashboards are built in your own accounts in Indian regions: AWS Mumbai or Hyderabad, Azure Central India or South India, Google Cloud Mumbai or Delhi. This keeps tag values and resource names, which can include personal data, under your DPDP Act 2023 controls.
How is this different from a managed cloud cost operations service?
These engagements design and deliver specific changes and frameworks. Managed cost operations is the recurring run work afterwards: weekly anomaly triage, cleanup tickets, schedule upkeep and recommendation follow-up. Many clients use both, with the governance framework defining the rules that the operations service executes.