FinOps

Cloud and AI cost optimization that drives business value. Cut waste, right-size workloads, and understand true unit economics.

What We Do

Cloud and AI cost have spiraled out of control for many enterprises. Without visibility and discipline, your multi-cloud infrastructure and AI workloads quickly become a financial liability. FinOps—Financial Operations—is the practice of bringing engineering, finance, and business together to drive cloud and AI cost optimization without sacrificing performance or innovation.

We help you implement FinOps from the ground up: visibility into actual costs, chargeback to drive accountability, rightsizing recommendations backed by data, and commitment planning to lock in discounts. For AI workloads, we go deeper—calculating unit economics for LLM inference, batch processing, and training to understand true consumption patterns and cost-per-outcome metrics.

Our dashboards and anomaly detection catch cost spikes before they spiral. Our commitment optimization tools help you balance flexibility with savings. The result: a culture where teams understand the cost impact of their decisions—and optimize them accordingly. On average, clients realize 25–40% cost reductions within the first year, often with improved service quality.

Core Capabilities

Cost Visibility & Attribution

Unified dashboards across AWS, Azure, and GCP. Tag-based cost allocation, cost center chargeback, and granular visibility by service, environment, and team.

Rightsizing & Instance Recommendations

Analyze utilization patterns to recommend rightsized instances. Eliminate oversized VMs, poorly utilized databases, and unused storage. Track actual vs. recommended sizing.

Commitment Planning & Optimization

Data-driven RI/SavingsPlans recommendations. Calculate break-even points, optimal commitment mix, and negotiate leverage with cloud providers. Maximize discounts while maintaining flexibility.

Anomaly Detection & Alerts

ML-powered anomaly detection flags unusual cost spikes in real time. Investigate root causes (bad queries, runaway containers, etc.). Alerting and escalation workflows.

Unit Economics for AI Workloads

Understand the true cost of LLM inference, fine-tuning, and batch processing. Calculate cost per token, per request, per outcome. Benchmark against inference APIs vs. self-hosted.

Showback & Chargeback Models

Implement transparent cost allocation frameworks. Educate teams on their consumption; drive accountability through chargeback. Build incentives for cost-conscious engineering.

Multi-Cloud Dashboards

Unified reporting across AWS, Azure, and GCP. Compare unit costs, identify cloud-specific savings opportunities, and manage multi-cloud strategy efficiently.

Kubernetes & Container Optimization

Kubecost integration for container cost visibility. Right-size Kubernetes workloads, identify idle clusters, optimize resource requests, and manage multi-tenant costs.

Tools & Frameworks We Leverage

FinOps Foundation

Industry framework, best practices, and community standards.

AWS Cost Explorer

Native AWS cost analysis and recommendations.

Azure Cost Management

Comprehensive Azure spend analysis and governance.

GCP Cost Management

Google Cloud billing and budget tools.

Kubecost

Kubernetes cost visibility and optimization.

CloudHealth

Multi-cloud cost and governance platform.

Cloudyn

Microsoft Azure cost management and analytics.

Custom Analytics

Bespoke dashboards and reporting for your use cases.

How We Deliver

1

Cost Assessment & Planning

Baseline your current cloud and AI spend. Identify quick wins and opportunities. Develop a FinOps roadmap with realistic targets and timelines.

2

Implementation & Automation

Deploy dashboards, set up alerts, implement chargeback models. Automate rightsizing recommendations and commitment optimization workflows.

3

Governance & Ops Training

Train finance, engineering, and operations teams. Build cost-conscious culture. Establish FinOps working groups and governance forums.

4

Optimization & Continuous Improvement

Ongoing cost analysis, monthly reviews, new savings opportunities. Scale best practices. Adapt strategy as cloud usage evolves.

Expected Outcomes

30%

Average Cloud Savings

Typical cost reduction within first 12 months through rightsizing, commitments, and waste elimination.

40%

Faster Rightsizing

Automated recommendations accelerate the process of identifying and implementing instance right-sizing.

90+%

Cost Visibility

Clear attribution of costs to teams, projects, and cost centers. Full transparency into multi-cloud spend.

50+

Recommendations/Month

Continuous stream of optimization opportunities identified and prioritized by our systems.

Use Cases & Scenarios

High-Growth SaaS: Cost Control at Scale

As your SaaS platform scales, cloud costs spiral without discipline. We implement FinOps to maintain healthy unit economics: cost per user, cost per transaction, cost per API call. Align engineering and finance on cost efficiency.

✓ 25–35% cost reduction

AI/ML Teams: LLM Inference Optimization

LLM inference is expensive. We help you calculate true unit economics: cost per token, cost per inference, cost per user interaction. Optimize batch vs. real-time, self-hosted vs. API, model size vs. latency. Massive savings on inference bills.

✓ 40–50% LLM cost reduction

Enterprise Multi-Cloud: Unified Cost Control

Managing costs across AWS, Azure, and GCP is complex. We build unified dashboards, apply consistent FinOps practices, and identify cloud-specific optimization opportunities. Drive accountability across teams.

✓ 20–30% multi-cloud savings

DevOps & Kubernetes: Container Cost Transparency

Kubernetes clusters are notoriously opaque in terms of cost. We integrate Kubecost, allocate container costs to teams and services, and identify idle or oversized workloads. Right-size resource requests.

✓ 25–35% Kubernetes cost reduction

Frequently Asked Questions

Ready to Optimize Your Cloud Costs?

Let's eliminate waste, drive accountability, and unlock 25–40% in cloud and AI cost savings. Talk to our FinOps specialists today.

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