FinOps & Cloud Cost Optimisation

Find 20–40% cloud spend reduction without touching your architecture.

Cloud cost optimization services that identify 20–40% spend reduction within 90 days, without a full re-architecture. We audit your AWS, Azure, GCP and OCI footprint, eliminate waste, optimise reserved capacity, and apply FinOps governance frameworks so your cloud bill reflects actual business value.

What We Deliver

Core Capabilities

  • FinOps Assessment & Cloud Spend Audit
  • Rightsizing & Resource Optimisation
  • Reserved Capacity & Savings Plans Strategy
  • Cloud Waste Elimination
  • AI Inference & GPU Cost Governance
  • FinOps Operating Model & Tagging Strategy

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By the Numbers

What clients achieve with GYSP

20–40%
cloud spend reduction

identified within 90 days of a FinOps engagement, without a re-architecture project

90 days
to first savings realisation

rightsizing, waste elimination and reserved capacity optimisation deliver results before the quarter ends

100%
cloud resource tagging compliance

cost allocation by team, product and environment enables accurate showback and chargeback from day one

Proven Results

FinOps Case Studies

Global Enterprise AI Platform
Enterprise Technology

Global Enterprise AI Platform

MLOpsKubernetesFinOps

A global enterprise AI platform operating ML workloads across recommendation engines, demand forecasting systems, and fraud detection pipelines needed to scale from 4 to 18 data scientists without GPU spend spiralling out of control. GYSP engineered a Kubernetes-native, multi-tenant MLOps platform with Run:ai fractional GPU virtualisation, Karpenter dynamic compute provisioning, and a full FinOps enforcement layer that cut monthly GPU infrastructure spend from $68,000 to $18,000 while driving average GPU utilisation from 27% to 91%.

GPU Utilisation via Run:ai Fractional Slicing27% → 91%
Monthly GPU Infrastructure Spend$68K → $18K
Model Deployment Time via GitOps5 Days → 20 Min
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OYO
TravelTech

OYO

CloudDevOpsCompliance

Legacy infrastructure, idle cloud spend, DDoS vulnerability, and manual deployments were compounding simultaneously for one of the world's fastest-growing hospitality brands. GYSP tuned Kubernetes auto-scaling to eliminate idle capacity on GCP GKE, driving a 30% cost reduction while achieving 99.98% platform uptime.

Platform Uptime99.98%
Faster Deployments55%
Cost Reduction via Kubernetes FinOps Tuning30%
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Cars24
Automotive

Cars24

CloudData EngineeringDevOps

Scaling India's largest digital auto marketplace meant modernising cloud infrastructure, real-time data pipelines, and observability, simultaneously, without slowing the product team.

Uptime99.95%
Faster Feature Rollouts60%
Faster Decision-Making2×
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Industry Expertise

Industries We Serve with FinOps

FAQs

Common questions

Everything buyers typically ask before starting a finops engagement.

Ask us anything
What do cloud cost optimization services include?

Cloud cost optimization services include a full spend audit across compute, storage, data transfer and AI inference, rightsizing of oversized instances and containers, a reserved instance and savings plan strategy, elimination of idle and orphaned resources, and a FinOps operating model with tagging governance. Findings are delivered within two weeks, with the first savings realised within 90 days.

How much can FinOps consulting reduce our cloud bill?

Most organisations running workloads on AWS, Azure or GCP for more than 12 months carry 20–40% in avoidable spend. The main sources are oversized instances, unused reserved capacity, storage lifecycle gaps, and data transfer costs that have never been reviewed. AI and GPU workloads frequently add another 10–20% on top. We identify and eliminate each category without requiring a re-architecture project.

What is FinOps and how does it work?

FinOps (Financial Operations for cloud) is a practice that brings engineering, finance and operations teams together to make data-driven decisions about cloud spend. It works in three phases: Inform (visibility into what is being spent and why), Optimise (rightsizing, reserved capacity, waste elimination), and Operate (governance, tagging, accountability). Unlike a one-off cost review, FinOps embeds cost awareness into your engineering culture so savings compound over time.

How do you manage AI inference and GPU cost governance?

AI inference and GPU workloads are the fastest-growing cost category in most cloud environments. We right-size GPU instance types, implement inference batching to reduce per-request cost, set up model caching to avoid redundant inference calls, and establish cost-per-inference monitoring so teams can see the financial impact of every model call. For training workloads, we use spot and preemptible instances and implement checkpoint-resume pipelines to cut GPU spend by 40–60%.

How long before we see savings from a FinOps engagement?

The assessment and audit phase takes two to three weeks. Quick wins — rightsizing, idle resource cleanup, and storage lifecycle fixes — are implemented in weeks three to six and deliver the first measurable savings within 90 days. Reserved instance and savings plan optimisation follows in weeks six to twelve. A FinOps operating model, tagging governance and chargeback reporting are established in parallel so savings continue to compound after the engagement ends.

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