Beyond FinOps: How Deep Technical Optimization Can Unlock Up to 70% in Cost Savings

Cost optimization today sits squarely within the DevOps and platform engineering domain. As cloud infrastructure continues to scale, it now represents one of the largest and fastest-growing line items in modern technology budgets. 

True cloud efficiency depends on how systems are architected, how workloads scale, how observability is managed, and how infrastructure is automated. The practices that enable fast, reliable software delivery also determine whether cloud spend remains sustainable as demand increases.

Recent industry studies estimate that nearly 30% of cloud spend is wasted each year through idle resources, overprovisioned infrastructure, and unused services. When left unaddressed, this waste compounds as environments grow more complex. However, when approached with the right technical rigor, cost optimization can deliver meaningful results. 

This article offers a deep dive into how EverOps delivers advanced cost-optimization strategies that extend beyond traditional FinOps practices and standard models for partners. Through deep technical execution, embedded elite engineers, and unified AI native operations, we help organizations reduce waste, re-architect high-cost systems, and align infrastructure spend with business performance.

For teams focused on scaling efficiently, these insights provide a practical path to spending less while delivering more.

What is Cost Optimization as an Engineering Discipline?

In DevOps and FinOps contexts, cost optimization intersects directly with delivery performance and reliability. Inefficient architectures increase spend through excess compute capacity, redundant tooling, excessive data ingestion, and network inefficiencies. As systems grow, these costs compound unless engineering teams address the root causes embedded in platform design, CI/CD workflows, and observability pipelines.

Engineering-led cost optimization practices today focus on high-leverage technical interventions, including:

• Re-architecting workloads to better align compute, storage, and scaling behavior with real usage patterns
• Re-platforming legacy systems to modern, cloud-native services that reduce operational and licensing overhead
• Consolidating tools across observability, monitoring, and infrastructure management to eliminate redundant spend
• Redesigning network paths to reduce data transfer costs and improve traffic efficiency

When cost optimization is approached this way, spend is reduced by improving how systems are designed, deployed, and operated, without limiting teams or slowing delivery.

From Engineering Theory to Executed Savings

External research shows that organizations that re-architect cloud workloads can achieve 25% reductions in computing costs in large-scale environments, validating the leverage of engineering-led optimization. EverOps operationalizes this approach by combining technical re-architecture, workload re-platforming, and network redesign under full execution ownership.

These initiatives are delivered through our embedded TechPods, which are our senior engineering teams that operate inside client environments with full delivery ownership. Rather than producing mere recommendations, our teams implement changes across infrastructure, platforms, and tooling directly within clients' organizations. 

While our current framework includes rightsizing workloads, optimizing cloud commitments, and eliminating orphaned resources, the highest impact consistently comes from re-architecting core infrastructure. In one recent retail engagement, this approach led to a documented 30% cost reduction through workload re-architecture.

Treating Cost as an Engineering Metric

High-performing engineering teams should treat cost as a core signal of reliability and performance, not just another financial outcome. Cost, alongside latency, error rates, and saturation, helps leaders assess whether systems are scaling in a healthy, cost-effective way as demand grows.

To make this real, teams should begin tracking a more focused set of metrics, such as:

• Unit economics: cost per transaction, cost per active user, cost per environment, cost per feature
• Operational FinOps metrics: mean time to resolution for cost-related incidents, change failure rate for scaling and provisioning changes, and perhaps even error budget consumption caused by overly complex architectures 

EverOps’ embedded teams are designed to improve these metrics and then harden them into how platforms are built and operated. Instead of one-time savings, we instrument the right KPIs, integrate them into CI/CD and infrastructure-as-code workflows, and make cost, reliability, and performance part of a single engineering-owned discipline.

Where Cloud Cost Optimization Delivers the Greatest Impact

When cost optimization is applied at the architecture and platform layers, the impact extends beyond incremental savings. Re-architecture and re-platforming consistently rank among the most leveraged strategies for engineering leaders because they address the structural drivers of cloud spend rather than surface-level inefficiencies.

Industry research shows that organizations that re-platform legacy systems achieve an average of 30-50% savings from such initiatives. These gains are driven by improved scaling behavior, reduced operational overhead, and tighter alignment between infrastructure design and real workload demand.

Networking optimization also contributes a meaningful impact. Research indicates that organizations have reduced cloud networking costs by up to 50% through re-networking strategies, reinforcing the importance of addressing the architecture, platform, and network layers together as a unified system. 

This combination of re-architecture, re-platforming, and network optimization establishes a durable foundation for sustained cost efficiency, and one that can be measured, repeated, and scaled across environments.

How AI Native Operations Changes the Cost Story

Most modern teams are likely already aware that AI can “spot anomalies” in cloud bills and recommend rightsizing, but that is only the tip of the iceberg of what AI can do for FinOps today. The real story is that AI can sit inside your delivery and operations workflows, continuously learning from performance, cost, and incident data to keep spend aligned with reliability and velocity, not just trimming a few overprovisioned nodes at the end of the month. 

EverOps takes a broader view, treating AI as an operating discipline that shapes how platforms are designed, monitored, and improved over time, rather than as a one-off dashboard or experiment. We have put these to the test for prior client engagements, such as building Victor for Life360, a custom AI agent that turns thousands of lines of developer feedback into prioritized action plans with ownership and timelines in seconds rather than weeks. 

That same philosophy also underpins EverOps’ AI native operations and newly released AI Accelerator Programs, further proving that AI strategies, when paired with our embedded TechPods, can be used to drive concrete outcomes like lower cloud costs, faster incident response, and clearer engineering roadmaps.

Cost Optimization Success Stories

As seen above, engineering-led cost optimization delivers the greatest impact when execution spans infrastructure, observability, and various network layers. The following engagements demonstrate how EverOps has applied deep technical expertise to reduce cloud spend while improving reliability and operational clarity in even the most complex environments.

Fintech Observability at Scale

EverOps’ recent work with a $50B fintech cryptocurrency exchange is a perfect example of how observability can become a cost lever. The client faced challenges across network monitoring, infrastructure health, and data visibility due to fragmented tooling and high ingestion costs.

Our embedded teams unified observability and replaced fragmented monitoring stacks with a platform designed for scale and cost control. This ‘Unified Network Monitoring’ case study details how this re-architecture reduced operational complexity while improving visibility across cloud environments.

As a result, the client reduced operational overhead, achieved a 60% reduction in mean time to resolution (MTTR), and eliminated unnecessary tooling and licensing costs. Unified visibility also enabled teams to proactively identify critical issues that had previously gone undetected, establishing a scalable observability cost model that supports continued growth without linear increases in spend.

Zendesk Infrastructure Optimization

EverOps delivered a 70% reduction in Zendesk's infrastructure costs by optimizing a large EC2 estate spanning more than 2,500 instances across multiple regions and teams. Rapid growth, long-running instances, and accumulated licensing overhead had created an expensive and operationally complex environment that required a systematic approach to optimization.

Our embedded TechPods partnered closely with Zendesk’s engineering and operations leaders to perform deep analysis across the entire fleet. Rather than applying incremental fixes, the engagement focused on modernizing instance provisioning, automating lifecycle management, eliminating unnecessary licensing, and enabling dynamic scaling.

Key initiatives included migrating to immutable AMI-based workflows, implementing automated instance recycling, upgrading operating systems to eliminate extended security maintenance licensing, and moving workloads behind Auto Scaling Groups to match capacity with real demand.

This execution-led approach reduced infrastructure costs at scale without compromising reliability or performance. More importantly, it established cost optimization as a continuous, engineering-owned discipline, embedded directly into how infrastructure is built, operated, and scaled.

Partner with EverOps & Move Beyond Basic FinOps 

Cost optimization should be engineered into how systems are built and operated. Organizations that invest in deep technical optimization save an average of 35% more than basic FinOps approaches today. 

EverOps provides embedded optimization services that combine AI native cost modeling, senior-level technical execution, and full-cycle ownership. For instance, sprawl and observability bloat are constrained by legacy platform limitations; however, our teams implement the changes required to reduce spend while preserving performance and delivery velocity.

If cloud costs are growing faster than your systems should, partner with EverOps to engineer a better outcome.  Start with the Cloud Cost Assessment, a structured engagement that identifies high-impact optimization opportunities and delivers a technical roadmap with outcomes validated in production.

What would a 30–70% reduction in spend mean for your organization? Contact us today to find out!

Frequently Asked Questions

What makes engineering-led cost optimization more effective than traditional FinOps practices?

Traditional FinOps focuses on spend visibility, reporting, and financial governance. Engineering-led cost optimization addresses the root causes of cloud waste by changing how systems are architected, scaled, and operated. By re-architecting workloads, re-platforming legacy systems, and optimizing observability and networking, engineering teams can eliminate structural inefficiencies rather than simply managing their financial impact.

When should organizations move beyond FinOps into deeper technical optimization?

Organizations typically need deeper technical optimization when cloud costs continue to rise despite rightsizing, commitment discounts, and budget controls. This often signals architectural inefficiencies, legacy platform constraints, or observability sprawl that require hands-on engineering execution to resolve.

What role does observability play in cloud cost optimization?

Observability often becomes a major cost driver as environments scale. By consolidating tools, reducing redundant ingestion, and improving signal quality, observability can shift from a growing cost center to a lever for cost control. Unified visibility also enables faster incident response and more informed infrastructure decisions.

How does EverOps actually reduce cloud costs in production?

EverOps embeds senior engineers directly into client environments to execute optimization initiatives. These include workload re-architecture, re-platforming to cloud-native services, observability consolidation, infrastructure automation, and network optimization. All changes are implemented, validated, and measured in production rather than delivered as recommendations.

How does the EverOps Cloud Cost Assessment work?

The Cloud Cost Assessment is a structured engagement that maps cloud spend against performance, reliability, and architecture. EverOps engineers identify high-impact optimization opportunities and deliver a technical roadmap aligned to business goals, with outcomes validated in production rather than projected in reports.

Can EverOps support both cloud-native and legacy environments?

Yes. EverOps works across cloud-native, hybrid, and legacy environments. Whether optimizing modern container platforms or modernizing long-running EC2 estates, EverOps applies the same execution-led approach to reduce cost while preserving performance and reliability.

How quickly can organizations expect to see results with EverOps’ support?

Most engagements start with a 4–6-week execution phase focused on quick wins such as rightsizing and observability consolidation, followed by deeper re-architecture and automation work that compounds savings over subsequent quarters.