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The Problem
No company context
AI tools write code without knowing your codebase, your specs, your stored procedures, your standards. Generic in, generic out.
Zero audit trail
Nobody knows what changed, by whom, against which spec, at what cost. When something breaks, the log is empty.
Knowledge stays siloed
The senior engineer's head is still the source of truth. The pipeline learns nothing. That person leaves, the knowledge walks out.
Pilot never becomes default
The shiny demo works. Then adoption stalls. The new way of working never replaces the old one.
Why AIDLC?
Faster delivery
Tens of dollars per feature, hours of elapsed time. Not weeks.
Built-in compliance
Every action audited. Every dollar attributed to an issue.
Institutional memory
The pipeline learns. The org's knowledge stops walking out the door.
Faster onboarding
New engineers ramp on a pipeline that already knows the codebase.
A pipeline that improves
Each cycle's retro feeds the next cycle's configuration.
Humans stay in control
Agents propose. Humans approve. Every merge is gated.
Problems
No company context
Zero audit trail
Knowledge stays siloed
Pilot never becomes default
Why AIDLC?
Faster delivery
Built-in compliance
Institutional memory
Faster onboarding
A pipeline that improves
Humans stay in control
AWS FinOps
A Technical Strategy for Cost Optimization
We don't just move your workloads, we transform them. Containers, microservices, managed services, and full observability from day one.
The Reality
Most engineering teams overspend on AWS because of idle resources and misconfigured auto-scaling. This is a technical strategy to find infrastructure bottlenecks and fix them. It focuses on measurable results rather than vague assessments.
Two Steps to a Leaner Infrastructure
This process stabilizes the cloud budget by focusing on technical facts and active ownership.
Assessment
Audit current infrastructure and historical usage data to identify immediate waste.
Execution
Implement the technical changes and automation needed to reduce spend while maintaining system performance.
Our Technical Framework
Start by removing the noise and waste that accumulates in unmanaged environments.
Right-sizingMatch instance capacity to actual workload requirements to stop paying for unused headroom. |
SchedulingAutomate stop and start sequences for non-production environments during off-hours. |
Idle Resource RemovalIdentify and delete unattached storage and orphaned resources that continue to bill the account. |
Storage OptimizationImplement lifecycle policies to move data to appropriate storage tiers based on access frequency. |
Change how code and infrastructure run to take advantage of cloud-native efficiencies.
Cloud-Native OptimizationReduce instance counts by up to 65% using Kubernetes and EKS. Use automated scaling to manage resource usage within your clusters. |
Serverless TransitionMove event-driven workloads to serverless compute to eliminate the cost of idle servers. |
Database FreedomMigrate from expensive legacy licenses to cloud-native database options to reduce recurring costs. |
Application ModernizationRefactor architecture to reduce technical debt and associated infrastructure spend. |
Lock in long-term savings and implement guardrails to prevent future cost creep.
Spot Instance AdoptionUse spare capacity for fault-tolerant workloads to reduce compute costs by up to 90%. |
Reserved Instances and Savings PlansManage commitments for steady-state usage to secure long-term discounts. |
Data Transfer OptimizationAudit network architecture to minimize costs related to cross-region and egress traffic. |
Tagging and GovernanceImplement mandatory tagging so every resource has a clear owner and a business purpose. |
OpenTelemetry:
Baked in, not bolted on
Auto-instrumentation of .NET and Java services → ADOT collector sidecars on EKS → AWS X-Ray for distributed tracing → CloudWatch for metrics and logs → custom dashboards and SLO alerting. All traces, metrics, and logs flow through a single OTel pipeline, swap backends without re-instrumenting.
VM → Container replatforming
Move off bare VMs onto EKS with proper orchestration, autoscaling, and workload isolation.
Custom metrics
Business and technical KPIs exported via OTel metrics SDK to CloudWatch and Managed Prometheus.
Structured logging
JSON-structured logs with trace correlation IDs, shipped to CloudWatch Logs Insights.
SLO alerting
Composite alarms and SLO burn-rate alerts wired from day one not as an afterthought.
What changes in your architecture
AWS Landing Zone & multi-account governance
AWS Control Tower
Security baseline
Identity & access
Network architecture
FinOps & cost governance
Compliance as code
Measurable results our clients achieve
60-80%
infrastructure cost reduction vs on-prem VMs
10x
faster deployments via GitOps pipelines
99.9%+
availability through EKS self-healing & multi-AZ
<5 min
MTTR with full OTel trace-to-log correlation
AWS Cost Calculator
Results
Fix the Bottlenecks
Current infrastructure configurations are often the reason for the overspend. Discuss the technical challenges with an engineer who can help fix them.
FAQ
What is the Kloia AWS FinOps Framework?
Kloia's AWS FinOps Framework is a structured approach to cloud cost management aligned with the Linux Foundation FinOps standard. It covers the full lifecycle from assessing your current AWS spend to building a long-term cost optimization roadmap, bringing engineering, finance, and business teams into alignment around cloud economics.
How does the engagement process work?
It starts with an assessment phase where Kloia's team audits your AWS account to identify cost-saving opportunities. From there, a prioritized roadmap is developed, tackling quick wins first such as right-sizing and Reserved Instance optimization, before moving on to longer-term architectural changes like containerization or database migration.
What kind of cost savings can we realistically expect?
Savings depend on your current setup, but Kloia's solutions target meaningful reductions across multiple dimensions: up to 65% fewer EC2 instances via Kubernetes/EKS, up to 82% savings with smart Spot instance enablement, up to 80% cost reduction migrating Oracle/SQL Server to AWS Aurora, and up to 70% savings through Dewindowsification. Your specific results will be outlined in the assessment.
What does Right Sizing Optimization involve?
Kloia deploys an agent that continuously monitors your instance resource utilization across your AWS environment. Based on real usage data, it proactively recommends more appropriately sized instance types so you are not paying for capacity you do not use.
What is Spotification and is it safe for production workloads?
Spotification is Kloia's approach to intelligently enabling AWS Spot Instances wherever appropriate, potentially saving up to 82% compared to On-Demand pricing. Kloia's solution includes smart workload classification and fallback logic to make Spot usage reliable, not just for batch or dev workloads, but for suitable production scenarios as well.
What is Dewindowsification?
Dewindowsification refers to removing dependencies on Microsoft Windows-based licensing within your AWS workloads by migrating to Linux-based alternatives where applicable. This eliminates Windows license costs and can reduce related infrastructure expenses by up to 70%.
How does Graviton transition work and who is it suitable for?
AWS Graviton processors offer better price-performance compared to x86 instances. Kloia makes your applications compatible with Graviton-based instance types, delivering both cost savings and improved performance. It is suitable for most general-purpose workloads including web servers, microservices, and containerized applications.
Can Kloia help us get additional discounts on our AWS invoices?
Yes. Kloia offers two mechanisms beyond direct optimization. First, you can bring your monthly AWS invoices to Kloia to benefit from additional negotiated discounts on top of your existing spend. Second, Kloia can help you qualify for AWS credits and consultancy funding through AWS programs for new projects and requirements.