AI & Cloud Integration
Cut your cloud bill without cutting your reliability.
The average company overspends on cloud by 30–35%. The waste accumulates predictably: over-provisioned instances that were sized for a load spike three years ago, development and staging environments running 24/7 when they're used for eight hours, unattached EBS volumes and idle load balancers generating charges nobody notices, and no reserved instance strategy because someone was worried about being locked in. We run cloud cost optimisation as a structured FinOps programme — audit, tagging, right-sizing, commitment planning, and governance — not a one-time pass that decays back to waste within six months. The target is maximum performance at minimum cost, with visibility that keeps it that way.
What's included
- Full cloud spend audit across all services
- Resource tagging & cost attribution by team/product
- Right-sizing analysis & instance optimisation
- Reserved Instance & Savings Plans strategy
- Dev/test environment cost controls
- FinOps governance & budget alerting
How we deliver
- 1Cloud cost audit report with waste categories
- 2Resource tagging taxonomy & implementation
- 3Right-sizing recommendations with impact estimates
- 4Reserved Instance / Savings Plans purchase plan
- 5Cost monitoring dashboard (Looker Studio / Cost Explorer)
- 6FinOps governance process & budget alerts
35%
avg cloud spend reduction in the first engagement
100%
resources tagged with cost attribution by engagement end
2–4 wk
from audit to first savings implemented
0
optimisations that compromise production reliability
Technologies we use
- AWS Cost Explorer
- Azure Cost Management
- GCP Billing
- Terraform
- Infracost
- CloudHealth
- Spot.io
- Looker Studio
- AWS Compute Optimizer
- Kubecost
Why Origin for Cloud Cost Optimisation
Savings quantified before any change is made
Every right-sizing recommendation comes with an estimated monthly saving. You know the ROI of each change before approving it — no surprises.
Governance that prevents cost drift
Budget alerts, tagging enforcement in Terraform, and monthly cost reviews are built into the engagement. Savings that rely on willpower don't last — we build systems.
Reliability-neutral — no cost cut that increases risk
We don't right-size instances that handle burst traffic or single-AZ deployments to save money on a redundant zone. Every recommendation is reviewed against your reliability requirements.
Industries we serve
SaaS & Startups
Cloud spend discipline as headcount scales, burn rate management
E-Commerce
Peak/off-peak scaling, CDN cost optimisation, database right-sizing
Fintech
Compliance-aware cost controls, data transfer optimisation
Healthcare
Storage optimisation for imaging data, HIPAA-compliant cost management
Manufacturing
IoT data ingestion cost controls, batch processing optimisation
Media & Streaming
CDN and egress cost reduction, transcoding pipeline optimisation
“We were spending ₹18 lakhs a month on AWS with no idea where it was going. Origin audited it in two weeks, implemented Reserved Instances and right-sizing over a month, and we're now at ₹11 lakhs — with the same performance and better visibility into what's driving the spend.”
Frequently asked questions
- How much can we realistically reduce our cloud bill?
- For organisations without a prior optimisation programme, 25–40% reduction is typical from the first engagement. The largest savings come from: Reserved Instances / Savings Plans (20–40% discount on committed usage vs on-demand), right-sizing over-provisioned instances (often 20–30% of compute spend is on instances larger than required), and eliminating idle resources (unattached volumes, idle load balancers, forgotten development environments). The exact figure depends on your current resource configuration and how much prior optimisation has been done.
- Won't Reserved Instances lock us in to resources we might not need?
- Convertible Reserved Instances and Savings Plans (AWS) and Reserved Instances with flexibility (Azure) mitigate this significantly — you can exchange or change the commitment attributes. The risk of locking in is real but typically much smaller than the cost of paying on-demand rates for predictable baseline usage. We model your usage pattern, identify the stable baseline (which is almost always the majority of your compute), and design a commitment strategy that covers that baseline without over-committing on variable workloads.
- How do you identify which resources are over-provisioned?
- With utilisation data from CloudWatch (AWS), Azure Monitor, or GCP Cloud Monitoring. We pull average and P95 CPU, memory, and network utilisation by instance over 30–90 days. An instance running at 15% average CPU with a 40% P95 can typically be right-sized down by one or two instance sizes without affecting performance. AWS Compute Optimizer and Azure Advisor automate part of this analysis — we review their recommendations and filter out the ones that would impact performance for short-burst workloads the tools don't account for.
- We don't have resource tagging set up — is that a blocker?
- Not a blocker, but it limits cost attribution. Without tags, you know your total cloud spend but not which team, product, or environment is responsible for each cost. We implement a tagging taxonomy as part of the optimisation engagement — every resource tagged by environment (prod/staging/dev), team, product, and cost centre. Terraform enforces tagging on new resources. Tagging gaps on existing resources are identified and closed. With attribution in place, cost ownership becomes clear and teams make better provisioning decisions.
- How do you prevent costs from creeping back up after the optimisation?
- With governance, not willpower. Budget alerts notify the team when spending exceeds thresholds. Terraform enforces tagging on every new resource. Architecture review includes a cost estimate for new infrastructure before it's provisioned. Monthly cost reviews keep optimisation on the agenda. The savings from the first engagement fund the governance infrastructure that prevents regression. Without governance, costs drift back to baseline within 6–12 months — we design for persistence.