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.
The average organisation overspends on cloud by 30 to 35%, and the waste accumulates in predictable places: over-provisioned instances sized for a peak three years ago, dev environments running nights and weekends, and no Reserved Instance strategy because someone was worried about lock-in. Origin Softwares runs 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 makes the savings permanent.
What is cloud cost optimisation?
Cloud cost optimisation is the practice of identifying and eliminating cloud spend waste while maintaining the reliability and performance your applications require. It covers four categories: right-sizing over-provisioned compute instances, purchasing Reserved Instances or Savings Plans to replace on-demand pricing on predictable baseline usage, eliminating idle resources that generate charges without delivering value, and implementing governance so costs do not drift back after the initial reduction. Origin Softwares runs cloud cost optimisation as a FinOps programme — tagging every resource for attribution, right-sizing with utilisation data, purchasing commitments against a modelled baseline, and building budget alerts and Terraform tagging enforcement to prevent regression.
The problems this solves
- Cloud bill is growing faster than the engineering team or product usage, and nobody can explain where the incremental spend is going
- No resource tagging means costs cannot be attributed to specific teams, products, or environments — only a total monthly bill is visible
- Over-provisioned instances from past scaling events remain at their original size because right-sizing feels risky without utilisation data
- All compute runs on on-demand pricing despite predictable baseline usage that qualifies for 20 to 40% Reserved Instance discounts
- Development and staging environments run 24/7 at the same scale as production, generating charges during nights and weekends when nobody is using them
- Previous optimisation efforts delivered one-time savings that gradually eroded as new resources were provisioned without cost discipline
Business outcomes
- 25 to 40% reduction in monthly cloud spend typical for organisations without a prior optimisation programme
- 20 to 40% discount on committed baseline usage through Reserved Instances and Savings Plans versus on-demand rates
- 100% of resources tagged with cost attribution by engagement end — every team sees their own spend, not just a total bill
- Every right-sizing recommendation quantified before implementation — you approve each change knowing the exact expected saving
- Governance infrastructure prevents cost regression — savings persist because systems enforce discipline rather than relying on individuals
- Dev and test environment scheduling typically saves 60 to 70% of non-production compute spend with zero impact on development velocity
Who is this for?
SaaS & Startup CTOs
Engineering leaders whose cloud bill is growing faster than revenue and who need cost discipline without compromising the performance or reliability that drives growth.
Scale-Up Finance & Engineering Teams
Companies where cloud spend has become a material budget line and neither Finance nor Engineering has clear visibility into what is driving costs or where to reduce them.
E-Commerce Operations Teams
Retail businesses with highly variable traffic who are paying on-demand rates for peak capacity year-round when Reserved Instances on baseline usage and auto-scaling for peaks would cost significantly less.
Fintech & Healthcare Teams
Regulated businesses who need cloud cost reductions achieved without compromising the compliance and reliability controls that their frameworks require.
Teams After a Cloud Migration
Organisations who migrated to cloud and are now in their first or second year of cloud operations, discovering that the bill is higher than the pre-migration estimate and without the tooling to understand why.
Multi-Cloud Organisations
Companies running workloads across AWS, Azure, and GCP who need consolidated cost visibility and optimisation recommendations across providers rather than managing each independently.
When Cloud Cost Optimisation may not be the right fit
We'd rather tell you upfront than waste your time and budget.
- If your cloud spend is under $5,000 per month, the engineering effort of a formal FinOps programme will not pay back within a reasonable timeframe — budget alerts and basic tagging may be sufficient
- If your infrastructure is actively being rebuilt or migrated, optimise the target architecture rather than the current state that will be decommissioned
- If your team has already implemented a thorough optimisation programme within the last 12 months, the marginal savings from a repeat engagement will be smaller
- If your primary challenge is cloud reliability or security rather than cost, address those first — cost optimisation that compromises reliability creates larger business costs than it saves
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
Full Cloud Spend Audit
Analyse all cloud spend by service, account, and resource to identify waste categories and prioritise optimisation opportunities.
- Export billing data and categorise spend by service, region, and resource type
- Identify idle resources: unattached EBS volumes, idle load balancers, unused Elastic IPs, stopped instances still incurring charges
- Map on-demand spend against actual usage patterns to size the Reserved Instance and Savings Plans opportunity
- Identify over-provisioned instances using CloudWatch, Azure Monitor, or GCP Cloud Monitoring utilisation data
Tagging & Cost Attribution
Implement resource tagging so every cost can be attributed to a team, product, and environment.
- Design tagging taxonomy: environment, team, product, and cost centre tags as minimum required set
- Apply tags to all existing untagged resources — automated where possible, manual for resources that require context
- Implement Terraform tagging enforcement for new resources — missing required tags fail the plan
- Configure cost allocation tags in the billing console so attribution appears in Cost Explorer reports
Right-Sizing & Idle Resource Elimination
Eliminate idle resources and right-size over-provisioned instances based on utilisation data.
- Review AWS Compute Optimizer or Azure Advisor recommendations and filter against burst workload requirements
- Present right-sizing recommendations with estimated monthly savings and reliability impact assessment per instance
- Implement approved changes in planned maintenance windows with monitoring for performance regression
- Eliminate confirmed idle resources after a two-week confirmation period — no deletions without explicit approval
Commitment Planning
Design and execute a Reserved Instance and Savings Plans strategy to reduce on-demand costs on predictable baseline usage.
- Model stable baseline compute usage from 90-day utilisation data — separate predictable baseline from variable and burst workloads
- Design commitment mix: Savings Plans for flexible instance type coverage, Reserved Instances for specific workloads with known instance requirements
- Review and approve commitment purchase plan with Finance and Engineering before execution
- Monitor Reserved Instance utilisation at 60 and 90 days post-purchase to validate consumption matches the commitment
Governance & Ongoing Controls
Build the governance infrastructure that prevents costs from drifting back after the initial reduction.
- Configure budget alerts at account, environment, and team level with defined escalation procedures
- Implement dev and test environment scheduling — automatic shutdown during non-business hours
- Establish monthly FinOps review cadence with defined agenda: spend vs budget, new recommendations, commitment utilisation
- Document FinOps policy: tagging requirements, right-sizing approval process, and Reserved Instance renewal calendar
How long does a cloud cost optimisation engagement take?
An initial optimisation engagement covering audit, tagging implementation, right-sizing, and Reserved Instance purchase planning typically takes four to six weeks from start to first savings implemented. The first two weeks cover a full cloud spend audit and resource tagging taxonomy implementation. Weeks three and four involve right-sizing analysis with per-instance saving estimates reviewed and approved before any change is made. The final phase covers Reserved Instance and Savings Plans purchase planning and governance setup — budget alerts, tagging enforcement, and monthly review process. Origin Softwares implements savings in phases so reliability is never compromised in the process of reducing cost.
Technologies we use
- AWS Cost Explorer
- Azure Cost Management
- GCP Billing
- Terraform
- Infracost
- CloudHealth
- Spot.io
- Looker Studio
- AWS Compute Optimizer
- Kubecost
Architecture & scalability
- Reserved Instance vs Savings Plans: Savings Plans offer more flexibility (coverage applies across instance types and regions) but slightly lower discounts than instance-specific Reserved Instances — use Savings Plans for general compute baseline, Reserved Instances for specific workloads with known long-term requirements
- Right-sizing burst workloads: instances handling irregular traffic spikes require auto-scaling configuration review alongside instance type changes — right-sizing without tuning auto-scaling parameters can degrade performance on the next traffic event
- Data transfer costs: egress charges are frequently the second-largest cost category after compute and are often underestimated — map data flows between services and regions to identify VPC endpoint, same-region processing, and CloudFront optimisation opportunities
- Storage tier optimisation: EBS gp2 to gp3 migration delivers 20% cost reduction at the same performance for most workloads; S3 Intelligent-Tiering automates lifecycle management for access-pattern-variable object storage
- Spot instance suitability: batch processing, CI/CD workers, and fault-tolerant stateless services are good candidates for spot pricing (60 to 90% discount) — stateful or latency-sensitive production services are not
- Multi-account governance: AWS Organizations Service Control Policies and Azure Policy can enforce tagging requirements and prevent certain resource types from being created — governance at the policy level is more reliable than governance through individual discipline
FinOps Programme vs One-Time Audit vs Native Cloud Tools Only
| Criterion | FinOps Programme | One-Time Audit | Native Tools Only (Advisor, Compute Optimizer) |
|---|---|---|---|
| Initial savings | 25-40% reduction | 15-25% initial | 5-15% on acted recommendations |
| Savings persistence | High — governance prevents drift | Low — costs drift back in 6-12 months | None — no enforcement |
| Attribution visibility | Full — every resource tagged | Partial — if tagging implemented | None unless tagging already exists |
| Governance enforcement | Yes — Terraform + budget alerts | None — relies on manual discipline | None |
Why choose Origin Softwares
Our approach
- Every right-sizing recommendation comes with a quantified monthly saving — you approve each change knowing the exact expected impact before it is made
- We design governance infrastructure that prevents cost regression — budget alerts, tagging enforcement in Terraform, and monthly reviews keep savings permanent
- We never right-size instances that handle burst traffic without validating the impact on performance — reliability is not compromised for savings
- We model Reserved Instance and Savings Plans purchase against actual usage patterns — commitment sizing is evidence-based, not a guess
- Resource tagging taxonomy is implemented as part of the engagement so cost attribution is operational, not just a recommendation
- We have delivered cloud cost reductions across AWS, Azure, and GCP for organisations ranging from growth-stage startups to mid-market enterprises
Delivery standards
- Savings quantified per change before implementation — no surprise results after making a change
- Right-sizing decisions based on 30 to 90 days of CloudWatch or Azure Monitor utilisation data, not on instance configuration alone
- Reserved Instance and Savings Plans purchase modelled against the stable baseline workload — variable and burst workloads excluded from commitment
- Resource tagging enforced in Terraform for new resources — tagging gaps on existing resources identified and closed
- Budget alerts configured at account, environment, and team level before the engagement closes
- Reliability review on every right-sizing recommendation — no changes to single-instance production services without a reliability impact assessment
Quality assurance
- Pre-change baseline captured: current spend, resource configuration, and performance metrics documented before any change is implemented
- Post-change validation: actual spend measured against estimated saving in the first full billing cycle after each change
- Right-sizing validation: CPU and memory utilisation monitored for two weeks after instance type changes to confirm no performance degradation
- Reserved Instance coverage review at 60 and 90 days post-purchase to validate the committed usage matches actual consumption
- Tagging audit: automated scan of all resources for missing required tags run before the engagement closes
Security practices
- IAM roles for cost management tooling follow least-privilege — read-only billing access for analysis, with change approval required for any resource modification
- Cost management tool credentials stored in secrets manager — no hardcoded API keys in reporting scripts
- Budget alert notifications routed through approved communication channels — no sensitive billing data in unencrypted email
- Compliance review on all right-sizing changes to regulated workloads — no changes to HIPAA or PCI-scoped resources without compliance sign-off
- Audit trail maintained for all changes made during the optimisation engagement — documented justification for every resource modification
Performance
- Utilisation data pulled at P95 and average — right-sizing based on average alone misses burst workloads and causes performance issues
- Auto-scaling reviewed and tuned alongside right-sizing — smaller instance types require well-configured scale-out to handle traffic spikes
- Storage tier optimisation reviewed: S3 Intelligent-Tiering, EBS gp3 migration, and RDS storage type alignment with I/O patterns
- Data transfer costs mapped and optimisation opportunities identified — same-region data processing, CloudFront for static assets, and VPC endpoint usage
- Spot and preemptible instance opportunities identified for batch and fault-tolerant workloads that can tolerate interruption
What you receive
- Cloud cost audit report with waste categorised by type — idle resources, over-provisioning, commitment gaps, and tagging gaps
- Resource tagging taxonomy and implementation across all existing resources
- Right-sizing recommendations with per-instance estimated monthly savings and reliability impact assessment
- Reserved Instance and Savings Plans purchase plan modelled against actual usage patterns
- Cost monitoring dashboard with account, environment, and team attribution
- FinOps governance documentation: budget alert configuration, tagging policy, and monthly review process
Support tiers
- Launch support: 30-day post-implementation monitoring to validate savings against estimates and address any performance issues
- FinOps retainer: Monthly cost review, new resource right-sizing recommendations, and Reserved Instance strategy updates as usage evolves
- Managed FinOps: Ongoing cost management including proactive right-sizing, commitment renewal planning, and anomaly investigation
- Advisory: Quarterly architecture cost review evaluating new cloud service options that may reduce spend for specific workload patterns
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
Typical delivery timeline
| Phase | Duration | What happens |
|---|---|---|
| Spend Audit | 1-2 weeks | Full spend categorisation, idle resource identification, and right-sizing opportunity mapping. |
| Tagging Implementation | 1 week | Tagging taxonomy design, existing resource tagging, and Terraform enforcement setup. |
| Right-Sizing & Idle Elimination | 1-2 weeks | Recommendations reviewed, approved, and implemented with monitoring. |
| Commitment Planning & Purchase | 1 week | Reserved Instance and Savings Plans modelling, review, and purchase. |
| Governance & Controls | 1 week | Budget alerts, environment scheduling, monthly review process, and FinOps policy documentation. |
Before you start — a checklist
Use this to prepare for your first conversation with us.
- Is your cloud bill growing faster than your business metrics? Cost optimisation should be a recurring programme, not a one-time project.
- Can you attribute your cloud spend to specific teams, products, and environments? Without tagging, optimisation is guesswork — attribution comes first.
- Do you have predictable baseline compute usage running on on-demand pricing? Reserved Instances and Savings Plans on that baseline are typically the largest single savings opportunity.
- Have you right-sized instances in the last 12 months based on actual utilisation data? Instances provisioned for historical peak loads are a reliable source of savings.
- Do your dev and test environments run 24/7? Environment scheduling is typically the easiest and fastest saving with zero reliability impact.
- Did a previous cost reduction effort deliver savings that gradually eroded? Governance infrastructure — not a one-time audit — is what makes savings permanent.
Maintenance & support
- Monthly FinOps reviews: spend versus budget reviewed with anomaly investigation and new right-sizing recommendations as workloads change
- Reserved Instance renewal planning: 90-day advance review of expiring commitments with renewal recommendations based on current usage patterns
- New workload cost review: architecture cost estimate produced before any new significant workload is provisioned
- Quarterly commitment utilisation review: Reserved Instance and Savings Plans utilisation analysed with adjustments recommended where usage has shifted
- Annual FinOps programme review: full re-audit to identify savings opportunities created by product changes, traffic growth, and new cloud service availability
“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
Planning & scope
- How do we know which instances are safe to right-size?
- By reviewing utilisation data over a 30 to 90 day window before making any changes. An instance running at 15% average CPU with a 40% P95 can typically be right-sized down without performance impact. We review AWS Compute Optimizer or Azure Advisor recommendations and filter out instances with burst traffic patterns that the automated tools do not account for. Every change includes a monitoring period to catch any performance regression before it affects users.
- Won't Reserved Instances lock us in?
- Convertible Reserved Instances and Compute Savings Plans both allow changes to the commitment attributes — instance type, region, and operating system — reducing the lock-in risk significantly. The risk of over-committing is real but typically far smaller than the cost of paying on-demand rates for predictable baseline usage. We model your stable baseline from 90-day utilisation data before sizing any commitment purchase.
- How do we stop costs from creeping back up after the optimisation?
- With governance systems rather than individual discipline. Budget alerts notify teams when spend exceeds thresholds. Terraform enforces tagging on every new resource. Architecture reviews include a cost estimate for new infrastructure before provisioning. Monthly FinOps reviews keep optimisation on the agenda. Without these systems, costs drift back to baseline within 6 to 12 months — we design for persistence.
- Can you optimise costs across AWS, Azure, and GCP at the same time?
- Yes. We use tools like CloudHealth or Spot.io for multi-cloud cost visibility alongside the native billing APIs of each provider. The optimisation principles are consistent across providers — right-sizing, commitment purchasing, idle resource elimination, and governance — though the specific tools and commitment structures differ per provider. We produce a unified cost report across all three providers so you have a single view of total cloud spend.
Technical
- How do you identify idle resources that are still generating charges?
- Through a combination of billing data analysis and resource utilisation checks. Unattached EBS volumes appear in the billing data without an associated instance. Idle load balancers have zero or near-zero request counts in CloudWatch metrics. Stopped EC2 instances still incur EBS storage charges. Forgotten development environments are identified by zero network activity over 30 days. We run automated scans for each category and present a list for confirmation before any deletion.
- What is the difference between Reserved Instances and Savings Plans?
- Reserved Instances provide a discount on a specific instance type, region, and operating system — the most predictable workloads at the lowest price. Savings Plans provide a discount on a dollar-per-hour commitment that applies flexibly across instance types and regions — more flexible, slightly lower discount. We use Compute Savings Plans for the general compute baseline and instance-specific Reserved Instances for workloads with known long-term requirements.
- How do you handle cost optimisation for Kubernetes workloads?
- Kubernetes cost optimisation requires tools that attribute costs to namespaces and workloads — Kubecost is the standard for this. At the infrastructure level, right-sizing node groups using cluster utilisation data, using Spot instances for non-critical node pools, and configuring Cluster Autoscaler to scale down aggressively during low-traffic periods. At the application level, setting resource requests and limits on all pods so the scheduler can pack workloads efficiently.
- How do you optimise data transfer costs?
- Data transfer costs are mapped by analysing VPC Flow Logs and billing data to identify the largest cross-region and cross-service data flows. Optimisation options include: VPC endpoints to eliminate NAT Gateway charges for AWS service traffic, same-region processing for data that crosses regions unnecessarily, CloudFront for static assets to reduce S3 egress, and Direct Connect for large on-premise-to-cloud data volumes. We prioritise by the magnitude of the transfer cost, not by the technical elegance of the solution.
Engagement & process
- How quickly will we see savings after starting the engagement?
- Idle resource elimination and dev environment scheduling typically deliver savings within the first two weeks. Right-sizing is implemented in the third and fourth weeks and appears in the next billing cycle. Reserved Instance and Savings Plans discounts apply immediately after purchase. The full estimated saving is typically visible in the first complete billing cycle after the engagement closes — usually within four to six weeks of starting.
- Do you need access to our AWS root account?
- No — we request read-only billing access via an IAM role to analyse costs and utilisation data, and limited change access to implement the approved optimisations. We document every permission we request and why it is required. For organisations with strict access policies, we can work with your team to implement changes you approve rather than making them directly.
- Can you optimise costs without any production downtime?
- Yes. Right-sizing involves stopping and starting an instance at its current size with a new instance type — this requires a brief outage of typically 2 to 5 minutes per instance, scheduled during a maintenance window. Reserved Instance and Savings Plans purchases, tagging, and governance setup involve no downtime at all. We schedule all instance changes during agreed maintenance windows and validate performance before closing the change.
What results should you expect from cloud cost optimisation?
For organisations without a prior optimisation programme, a 25 to 40% reduction in monthly cloud spend is typical from the first engagement. The largest savings come from Reserved Instances and Savings Plans — typically 20 to 40% discount on committed baseline usage versus on-demand rates — followed by right-sizing over-provisioned instances and eliminating idle resources. Origin Softwares quantifies every saving before implementing it: you see the estimated monthly reduction for each change before approving it, and verify the actual reduction in the first full billing cycle after implementation. Governance infrastructure prevents costs from drifting back to baseline within six to twelve months, which is the common failure mode of one-time optimisation passes.
Related services
Custom Software Development
Application architecture decisions directly affect cloud costs — we design cost-conscious architectures from the first commit.
Cloud & DevOps
FinOps governance integrates with CI/CD pipelines and Infrastructure as Code to enforce cost controls on every deployment.
Data Engineering
Data warehouse and pipeline costs are a significant portion of cloud spend for analytics-heavy organisations — we optimise these alongside compute.
Web Development
Web application infrastructure — CDN, load balancing, and origin compute — is a common area for cost optimisation without reliability impact.
Mobile App Development
Mobile backend infrastructure scaling and API gateway costs are optimised alongside the broader cloud cost programme.
Not sure where to start?
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