Ultimate Guide to FinOps: Principles, Phases, and Technology

Feb 8th, 2026

What is FinOps?

FinOps is a cloud financial management discipline that enables organizations to maximize the value of their cloud investments. FinOps achieves this objective by helping finance, engineering, business, and technology teams effectively collaborate on cloud cost decisions to drive business outcomes.

FinOps is first and foremost a cultural practice—a way to manage cloud costs with one set of policies and best practices, while ensuring all collaborators take ownership of their cloud usage. It facilitates collaboration between cross-functional teams to enable quicker product delivery, while obtaining cost predictability and financial control.

The Key Benefits of FinOps

Organizations are adopting FinOps to bring financial accountability to cloud spending and to align costs with business goals. Here are the key benefits driving its growth:

1. Cost Visibility and Transparency
FinOps provides granular insights into cloud usage and associated costs. Teams can access real-time data, which helps identify waste, track spending by team or project, and improve forecasting accuracy.

2. Shared Accountability Across Teams
FinOps establishes a shared responsibility model where finance, engineering, and operations work together. This ensures that everyone involved in cloud usage understands the financial impact of their decisions.

3. Faster Decision-Making
By integrating financial insights into engineering workflows, FinOps enables teams to make informed decisions quickly. This reduces the delay between detecting a cost issue and taking corrective action.

4. Optimized Resource Usage
FinOps encourages practices such as rightsizing, commitment-based discounts, and automation. These reduce overprovisioning and idle resources, directly cutting costs without sacrificing performance.

5. Improved Budgeting and Forecasting
With continuous cost tracking and historical data, organizations can create more accurate budgets and forecasts. This leads to better financial planning and fewer surprises in cloud bills.

6. Business Alignment
FinOps ensures that cloud spending is aligned with business priorities. Teams can track the cost of delivering features or services, improving ROI analysis and enabling value-driven development.

The Core Principles of FinOps

The FinOps Foundation created six core principles that guide FinOps practices. The foundation re-examines and adjusts these core FinOps principles as needed.

Collaboration

Successful FinOps practices depend on tight collaboration between finance, engineering, product, and business stakeholders. Each group brings a different perspective: finance focuses on budgeting and cost control, engineers prioritize performance and scalability, and product managers look at feature delivery and timelines. FinOps bridges these priorities by establishing shared goals and a common language around cost efficiency. Regular touchpoints—such as cost reviews, usage audits, and sprint planning—are structured to include financial impact discussions. This leads to faster resolution of cost issues and a culture of continuous improvement.

Visibility

Visibility in FinOps means making cloud usage and cost data accessible, accurate, and timely. This requires setting up detailed tagging strategies, integrating cost data into dashboards, and aligning spend with business units, projects, or teams. Tools such as AWS Cost Explorer, GCP Cost Management, or third-party platforms are often used to break down costs by resource, region, and service. Teams monitor this data daily or weekly to spot anomalies, track spending trends, and understand the true cost of running specific workloads. Without visibility, optimization efforts are speculative and inconsistent.

Accountability

Accountability ensures that teams are responsible for managing the financial impact of their cloud resources. Engineers are expected to understand how their infrastructure choices—such as instance types, data transfer, or storage tiers—affect costs. This is enforced through budgets, usage alerts, and KPIs tied to cost efficiency. Teams are given autonomy but are held accountable through chargebacks, showbacks, or unit economics (e.g., cost per transaction). By making cost part of the team’s performance metrics, organizations create a culture where everyone treats cloud spend as a shared resource.

Reporting

Reporting provides the structure for decision-making and continuous oversight. FinOps reporting goes beyond monthly invoices; it includes real-time dashboards, forecast vs. actual spend reports, and trend analyses. Reports are customized by audience—finance may want detailed variance analysis, while engineers may prefer alerts on unexpected spikes or unattached resources. Automation plays a key role here, enabling frequent updates without manual effort. Effective reporting highlights actionable insights rather than raw data, helping teams course-correct quickly.

Centralization

Centralization in FinOps refers to having a centralized team or function that governs cost management processes, tooling, and education. This team does not control individual teams' cloud usage but sets standards for tagging, budgeting, tooling integration, and cost allocation. It also manages vendor relationships and negotiates pricing discounts across the organization. By centralizing policy and decentralizing execution, organizations strike a balance between agility and control. The centralized team ensures consistency, while individual teams retain flexibility.

Optimization

Optimization is a continuous, data-driven process focused on reducing waste and maximizing value. This involves a mix of technical strategies—such as rightsizing instances, eliminating idle resources, and selecting optimal storage classes—and financial strategies like leveraging reserved instances, savings plans, or spot instances. Automation tools are often used to identify and apply changes, while teams conduct regular cost reviews to assess effectiveness. Optimization is not just about cutting costs, but aligning spending with actual usage and business value.

Learn more in the detailed guide about cloud cost optimization

Related guides

Authored by Finout

Related technology updates:

[Webinar] Driving a Successful Company-Wide FinOps Cultural Change

3 Phases of the FinOps Lifecycle

According to the FinOps Foundation, FinOps is a crawl, walk, run process which should be iterative. It involves continuously circling through phases to refine FinOps and drive it forward.

The FinOps Foundation defines three major FinOps lifecycle phases:

Inform

A FinOps team must have visibility into cloud utilization and costs. The first step in implementing FinOps is assessing the organization’s efforts on cloud resources and services’ allocation, current costs, benchmarks, budgeting, and forecasts. A FinOps team will use this visibility and analysis to develop appropriate purchasing strategies for their organization.

Detailed allocation information enables the FinOps team to tie cloud utilization and costs to relevant cost centers and stakeholders. This visibility helps assign accountability. avoid unexpected costs, identify opportunities to improve, and show stakeholders the business value of the cloud.

Optimize

A FinOps team uses analysis information to optimize cloud utilization. The team employs various automation techniques to optimize cloud costs and resources. They can use committed-usage discounts such as reserved instances to reduce costs, evaluate a cloud environment and right-size resources accordingly, and utilize tools to automatically scale down or shut off unnecessary resources.

Operate

FinOps teams continuously track cloud operations, evaluating them against business objectives and metrics. The team monitors and works to ensure cloud utilization and performance align with business needs. Additionally, the team shares this information with the relevant stakeholders to demonstrate the cloud’s operational and financial effects on the business.

Organizations can cycle through several or all phases of the FinOps lifecycle simultaneously, according to the workload or department involved.

Who Are the Key FinOps Stakeholders?

Successful FinOps implementation depends on the active participation of several stakeholders. Each plays an integral role in managing cloud costs and making informed financial decisions.

Executives

Executives are the decision-makers in an organization. Their role in FinOps involves setting the vision, defining strategic objectives, and providing support and resources for the FinOps initiatives. They are responsible for ensuring that the organization’s FinOps practices align with its broader financial and operational goals.

Executives drive the adoption of FinOps across the organization. They champion the importance of financial management in the cloud environment and foster a culture of financial accountability. By setting the stage for FinOps, executives play a crucial role in its successful implementation.

Business/Product Owners

Business or Product Owners are responsible for the financial performance of their products or services. In the context of FinOps, they are involved in managing the cost efficiency of their cloud services. They need to understand the cost implications of their decisions and how to balance the needs of their product or service with the organization’s financial objectives.

Business/Product Owners play a crucial role in budget planning and cost forecasting. They work closely with the FinOps team to allocate resources efficiently and make informed decisions about cloud usage. They are also involved in monitoring cloud costs and identifying opportunities for cost savings.

Engineering and Operations

Engineers and Operations teams are the ones who design, build, and manage the cloud infrastructure. They are the technical experts who understand the ins and outs of the cloud environment. Their role in FinOps involves ensuring that the cloud resources are used efficiently and cost-effectively.

Engineers and Operations teams work closely with the FinOps team to monitor cloud usage and costs. They provide technical insights into the cloud environment and help identify opportunities for cost optimization. They also implement cost-saving measures and track their impact.

FinOps Practitioners

FinOps Practitioners are the champions of FinOps within an organization. They are responsible for implementing and managing the FinOps practices. They monitor cloud costs, analyze spending patterns, and provide insights to help make informed financial decisions.

FinOps Practitioners work closely with all the other stakeholders to ensure that everyone understands the financial implications of their decisions. They provide training and support to help teams adopt FinOps practices and foster a culture of financial accountability.

Finance/Procurement

Finally, the Finance and Procurement teams play a crucial role in FinOps. They are responsible for managing the organization’s finances, including its cloud expenditure. They work closely with the FinOps team to monitor cloud costs, track spending, and ensure financial accountability.

Finance/Procurement teams provide financial expertise and insights to help guide the FinOps practices. They are involved in budgeting, forecasting, and financial reporting. They also help negotiate contracts with cloud service providers and manage the financial aspects of the organization’s cloud usage.

FinOps vs. DevOps: How Do They Compare?

While FinOps and DevOps share cultural and operational principles—such as cross-functional collaboration, agility, and automation—they focus on different outcomes. DevOps is centered on accelerating software delivery and improving system reliability. FinOps, on the other hand, is focused on financial accountability and optimizing cloud spend.

DevOps teams aim to ship features quickly and reliably through CI/CD pipelines, monitoring, and infrastructure as code. FinOps teams integrate with this workflow to ensure that the infrastructure choices made during development and deployment are also cost-efficient. For example, while DevOps might automate the provisioning of instances for scalability, FinOps evaluates if those instances are the right size or if discounts are being applied.

A key difference lies in their metrics. DevOps measures success through deployment frequency, mean time to recovery, and uptime. FinOps focuses on metrics like cost per service, forecast accuracy, and cloud spend against budget. When aligned properly, FinOps complements DevOps by embedding cost considerations into the development lifecycle without slowing down delivery.

Ultimately, FinOps ensures that cloud efficiency becomes a shared responsibility—not just for finance but also for engineering, operations, and product teams—just as DevOps ensures operational excellence is shared across development and operations.

Key FinOps Technologies and Capabilities

Cloud Automation

Cloud automation in FinOps refers to using scripts, tools, and platforms to automatically manage cloud infrastructure based on cost and usage policies. This includes provisioning, scaling, and decommissioning resources without manual intervention, driven by predefined rules.

Examples include auto-scaling groups that adjust compute capacity based on demand, or serverless functions that shut down non-production environments after hours. FinOps teams also automate enforcement of cost-saving measures like applying reserved instance coverage or flagging underutilized assets.

By integrating automation with monitoring tools and cost data, teams can proactively manage spend and reduce human error. Automation ensures consistent execution of cost controls, increases operational efficiency, and supports real-time financial governance across environments.

Cost and Usage Ingestion

Cost and usage ingestion involves collecting detailed billing and usage data from cloud providers. This data, which often comes from APIs or usage reports like AWS CUR (Cost and Usage Report), is the foundation of all FinOps analysis. It includes granular metrics by service, region, instance type, and tag.

Once ingested, the data is cleaned, normalized, and enriched for reporting and optimization. Tools like BigQuery, Snowflake, or custom ETL pipelines are often used to transform raw billing data into actionable insights. Timely ingestion enables near real-time visibility into spend patterns, supporting faster financial decisions and cost anomaly detection.

Allocation and Tagging

Allocation and tagging are key to understanding and attributing cloud spend accurately. Tags are metadata labels (e.g., environment=prod, team=marketing) attached to cloud resources. Proper tagging allows organizations to track costs by team, project, product, or customer.

FinOps teams define mandatory tagging schemas and enforce compliance through automation or governance policies. Unallocated or untagged costs are tracked and reduced over time. Advanced allocation strategies may also include cost sharing for shared services or applying business rules to apportion untagged spend. Accurate allocation is essential for chargebacks, showbacks, and accountability.

Forecasting and Budgeting

Forecasting and budgeting allow organizations to predict future cloud costs and align them with financial goals. FinOps tools analyze historical spending trends, seasonality, and growth patterns to generate forecasts. These predictions inform budget planning, funding decisions, and purchasing strategies (e.g., committing to reserved capacity).

Budgets can be set at different organizational levels—teams, projects, or departments—and monitored with real-time alerts for overages. Finance and engineering collaborate on budget creation and adjustments, ensuring that forecasts are technically feasible and financially sound. Accurate forecasting reduces surprises and improves cost predictability.

Key FinOps Use Cases

Cloud Computing Cost Optimization

Cloud computing cost optimization is one of the most common and impactful use cases for FinOps. Organizations use FinOps practices to continuously analyze cloud spend across services and accounts, and implement controls that reduce waste without affecting performance.

This includes identifying underutilized or idle resources such as unattached volumes, oversized instances, or underused databases. FinOps teams collaborate with engineering to rightsize these resources and adopt purchasing strategies like reserved instances, savings plans, or spot instances to lower costs.

Learn more in the detailed guide about Cloud Computing Costs

Related product offering:Faddom | Instant Application Dependency Mapping Tool

Kubernetes Cost Management and Optimization

Kubernetes introduces a layer of abstraction that complicates cost tracking. In a FinOps context, managing Kubernetes costs requires detailed visibility into cluster usage at the pod, namespace, and workload level.

FinOps teams rely on tools that integrate cloud billing data with Kubernetes metrics to allocate costs to specific teams or services. This enables chargebacks, showbacks, and unit cost tracking (e.g., cost per deployment or per API call).

Optimization strategies include setting appropriate resource requests and limits to avoid over-provisioning, scaling workloads based on demand, and bin packing workloads to maximize node utilization. Horizontal Pod Autoscaling (HPA) and Cluster Autoscaler are commonly used to automatically adjust resource consumption based on traffic and usage patterns, ensuring that workloads scale up or down efficiently without manual intervention. These autoscaling mechanisms help balance performance and cost by provisioning only the necessary resources at any given time.

Learn more in the detailed guides about:

Kubernetes Pricing

Kubernetes Autoscaling

Anomaly Detection and Spend Governance

FinOps introduces controls to detect unusual spending patterns early and prevent cost overruns. Anomaly detection involves setting thresholds, baselines, or machine learning models that alert teams to deviations in real-time.

Teams configure daily or hourly monitoring across accounts, regions, or services. Alerts are routed to relevant stakeholders for fast investigation, minimizing financial impact. For example, if a new workload causes a sudden increase in egress traffic or provisioned storage, FinOps teams can investigate and resolve the issue before it affects the monthly bill.

Governance includes defining budgets, quotas, and policies for tagging, provisioning, and spend approval. FinOps enforces these controls through policy-as-code and integrates cost guardrails into CI/CD pipelines to prevent costly misconfigurations during deployment.

VDI Capacity Planning and Optimization

Virtual desktop infrastructure (VDI) environments are often subject to fluctuating demand, making them a strong candidate for FinOps optimization. FinOps teams help align VDI capacity planning with user behavior patterns and business needs.

Cost optimization begins with analyzing usage trends to determine peak hours, idle periods, and overall utilization. This data supports rightsizing of compute and storage, and helps decide between persistent vs. non-persistent desktops. Autoscaling policies can then be implemented to expand or shrink capacity based on real-time demand.

FinOps also facilitates pricing strategy decisions, such as using savings plans or spot instances for predictable workloads. By tracking cost per user session or application, organizations can better evaluate ROI and prioritize improvements to reduce operational costs without degrading user experience.

Learn more in the detailed guide about Virtual Desktop Infrastructure

AWS Cost Optimization

Cost Optimization Best Practices

Use Amazon EC2 Spot Instances to Reduce EC2 Costs
Spot Instances let you purchase unused EC2 capacity at a significant discount—often up to 90% compared to On-Demand prices. They're ideal for stateless, fault-tolerant, or flexible workloads such as batch jobs, data analysis, or CI/CD pipelines. However, Spot Instances can be interrupted with little notice, so they should be used alongside mechanisms like EC2 Auto Scaling groups or Spot Fleet with diversified allocation strategies to maintain availability.

Use Reserved Instances (RI) to Reduce Costs
Reserved Instances offer discounted rates in exchange for committing to a specific instance type and region over a one- or three-year term. RIs are best suited for predictable, steady-state workloads where usage patterns remain consistent. Organizations can choose between Standard RIs for the highest savings or Convertible RIs for flexibility in changing instance attributes. Regular analysis of usage patterns ensures that RI coverage aligns with actual demand.

Use Compute Savings Plans to Reduce EC2, Fargate and Lambda Costs
Compute Savings Plans provide flexible pricing for compute services (EC2, AWS Fargate, and AWS Lambda) based on a committed hourly spend over a one- or three-year term. Unlike RIs, Savings Plans automatically apply to any instance family, size, or region, allowing for workload changes without losing savings. They are ideal for organizations needing commitment-based discounts without locking into specific instance configurations.

Identify Amazon EC2 Instances With Low Utilization
Low-utilization EC2 instances consume resources without delivering proportional value. These can be identified by monitoring metrics such as CPU, memory, and network I/O over time using tools like AWS CloudWatch or Compute Optimizer. Instances running consistently below performance thresholds should be right-sized, consolidated, or scheduled for automatic shutdown during idle periods to reduce unnecessary spending.

Delete Unattached EBS Volumes
Unattached Elastic Block Store (EBS) volumes continue to incur charges even when not in use. After instance termination, these volumes often remain unless explicitly deleted. Regularly auditing EBS volumes using tools like AWS Trusted Advisor or automation scripts helps detect and remove unused volumes, reclaiming storage costs while reducing risk from forgotten resources.

Identify and Delete Orphaned Snapshots
Snapshots not tied to active volumes or required backup policies accumulate over time, consuming storage and increasing costs. These orphaned snapshots can be identified through snapshot metadata and cross-referencing with existing volumes or AMIs. Automated lifecycle policies or periodic manual reviews ensure unused snapshots are deleted, maintaining cost control and reducing storage sprawl.

Learn more in the detailed guide to AWS costs

Related product offering: N2W | Cloud Backup and Restore

Related technology updates:

[Tool] AWS Cost Saving Calculator

Supporting Technologies

AWS Auto Scaling

AWS Auto Scaling monitors applications and adjusts capacity automatically to maintain consistent, predictable performance. It helps ensure applications always have the required resources at all times, while also optimizing cost, because when applications scale down according to actual demand, they also conserve costs.

The service offers a simple user interface (UI) for building scaling plans for various resources. Teams can use it to quickly set up application scaling for several resources across different services.

AWS Auto Scaling offers recommendations to help optimize costs and performance or balance the two. Teams using Amazon EC2 Auto Scaling to scale EC2 instances dynamically can combine this feature with AWS Auto Scaling to scale resources across other AWS services.

AWS High Availability

Highly available systems ensure reliability by continuing to operate even as critical components fail. They achieve resiliency by handling failure without disrupting service or losing data and can seamlessly recover from failures with minimal costs and downtime.

High availability on the cloud usually comes at a cost – for example, cloud resources with more redundancy can have a higher cost. FinOps can help manage and optimize these costs, balancing them with the savings generated from reduced downtime.

AWS provides high availability for cloud workloads across the following areas:

Compute—AWS computing services, including EC2, offer high availability features such as auto-scaling, provisioning, and load balancing.
SQL databases—managed SQL databases like Amazon RDS offer various options that automatically deploy databases with a standby replica located in a different Availability Zone (AZ).
Storage services—AWS storage services, including S3, EBS, and EFS, provide various high-availability options. EFS and S3 automatically store data across multiple AZs, and EBS deploys snapshots to multiple AZs.

Azure Cost Optimization

Cost Optimization Best Practices

Use Spot VMs for Low-priority Workloads
Azure Spot Virtual Machines offer unused compute capacity at discounted prices—up to 90% off pay-as-you-go rates. These VMs are ideal for interruptible workloads like testing, dev environments, and large-scale batch jobs. Because Spot VMs can be evicted with little notice based on capacity availability, they should be paired with workload checkpointing or backup scheduling. Spot VMs support both Windows and Linux and can be managed via Azure Virtual Machine Scale Sets for automatic scaling.

Use Azure Reservations to Prepay
Azure Reservations allow organizations to commit to one- or three-year terms for virtual machines and other resources, significantly reducing costs compared to pay-as-you-go pricing. Reserved Instances are most effective for workloads with consistent usage patterns. They can be applied automatically to matching resources, and some flexibility is available to exchange or cancel reservations. Usage analysis helps ensure reservations align with actual resource consumption.

Right-Sizing VMs
Right-sizing involves analyzing the performance metrics of existing virtual machines and adjusting their size to better match actual workload requirements. Azure Advisor and Azure Monitor provide insights into CPU, memory, and disk usage to detect underutilized VMs. Over-provisioned VMs should be downsized or reconfigured to avoid paying for unused capacity, while under-provisioned VMs may require scaling to maintain performance.

Apply Tags to Identify Cost Owners
Tagging in Azure enables better cost attribution by associating resources with metadata such as department, environment, or project. Tags like owner=team-x or env=prod help categorize expenses and enforce accountability. Azure Cost Management can filter and group costs by tags, enabling detailed reporting and analysis. Consistent tag enforcement, often automated through policies, ensures cost ownership is clear across the organization.

Use Managed Services When Possible
Azure’s managed services like Azure SQL Database, App Services, and Azure Kubernetes Service (AKS) abstract infrastructure management, offering built-in scalability and cost controls. These services reduce operational overhead and can auto-scale based on demand, minimizing idle resources. Choosing managed services also optimizes licensing and infrastructure costs, especially when compared to self-managed deployments on virtual machines.

Use Storage Tiering
Azure provides multiple storage tiers—Hot, Cool, and Archive—to optimize costs based on data access patterns. Frequently accessed data should reside in the Hot tier, while infrequently accessed data can be moved to Cool or Archive tiers. Lifecycle management policies automate this movement, helping organizations lower storage costs without manual intervention. Careful tier selection balances retrieval costs with storage savings.

Locating and Deleting Unused Disks
Unattached or orphaned managed disks—often remnants of deleted VMs—incur ongoing charges despite being unused. Azure Resource Graph and Cost Management tools can identify such disks. Once confirmed to be unnecessary, these disks should be deleted to eliminate waste. Periodic cleanup routines help prevent accumulation and reduce storage expenses over time.

Consider B-Series Virtual Machines
B-Series VMs in Azure are optimized for workloads with variable CPU usage, such as development servers, small databases, or domain controllers. These burstable VMs accumulate CPU credits during idle periods and consume them during usage spikes. They offer a cost-efficient alternative to standard VMs for workloads that don't require consistent high performance, lowering overall compute costs.

Supporting Technologies

Azure Autoscaling

Azure Autoscaling can save costs in the Azure cloud by automatically monitoring a system’s performance and adding or removing resources as needed. Azure provides this feature for various computing options, including:

Azure Virtual Machines (VMs)—provides scale sets to manage several Azure VMs as a group.
Service Fabric—allows using each node type in a Service Fabric cluster as a separate VM scale set to enable every node type to be scaled in or out independently.
Azure App Service—includes built-in auto scaling, with settings that apply to all apps within an App Service.
Azure Cloud Services—offers built-in auto scaling options at the role level.

Azure High Availability

Azure High Availability refers to the ability of a system or service to remain operational and accessible to users during planned or unplanned outages or disruptions. It ensures that services continue to run and can automatically failover to another instance or location in the event of an issue.

Like in AWS, the Azure cloud provides multiple high availability options that have a price attached. FinOps can help manage and optimize these costs, to achieve positive return on investment by reducing risk of service interruption, loss and damage.

There are several ways to achieve high availability in Azure:

Availability Sets: This feature ensures that virtual machines (VMs) are distributed across multiple physical servers, network switches, and storage devices to minimize the impact of an outage.
Availability Zones: This feature provides fault-isolated locations within an Azure region, allowing you to create highly available applications that can withstand the loss of a single datacenter.
Load Balancing: Azure Load Balancer distributes incoming traffic across multiple VMs, ensuring that no single point of failure exists.
Azure Traffic Manager: This service allows you to direct traffic to the closest or most available instance of your application, improving its performance and availability.
Azure Site Recovery: This service enables you to replicate and recover VMs, physical servers, and applications in the event of a disaster or outage.
Azure Backup: This service provides a simple and reliable way to back up data and restore it in case of data loss.
Azure Monitor: This service provides monitoring, diagnostics, and analytics for Azure resources, enabling you to quickly identify and resolve issues that may impact the availability of your applications.

Google Cloud Cost Optimization

Cloud Billing Reports

Google Cloud provides a billing reports page displaying a view at a glance of cloud usage costs and features to help discover and analyze trends. It displays a chart that visualizes usage costs for all projects connected to a Cloud Billing account. Users can select the date, define a time range, set up chart filters, and group by service, project, location, or SKU.

These reports can help learn about current Google Cloud spending trends for the current month, identify the most costly Google Cloud project during the last month, and the most expensive Google Cloud service overall. It can show the amount of spending per region and the cost of resources with a certain label and forecast future costs according to historical trends.

Identify Idle VMs and Disks

Idle resources like disks and VMs can accumulate costs. Google Cloud provides recommendations to help optimize these resources, such as an idle VM recommender that can identify inactive VMs and persistent disks according to usage metrics. It can help locate proof-of-concept projects that have been deprioritized and zombie instances that were not deleted.

Using Lifecycle Policies

Storage classes can help optimize costs by associating resources with the right class. Lifecycle policies automate this process using object lifecycle management. Configuring a lifecycle policy enables programmatically setting an object to adjust its storage class according to a set of conditions or deleting it entirely when it is no longer in use. Another option is to use Google’s tools for automatically cleaning up snapshots after a certain period of time.

Leverage Preemptible VMs

A preemptible VM is a compute instance that exists for a maximum of 24 hours and is offered at up to 80% off the on-demand price. Preemptible VMs are ideal for fault-tolerant workloads, including big data, media transcoding, genomics, simulation, and financial modeling.

Azure allows using a combination of regular and preemptible instances by creating a specialized managed instance group. This configuration can help finish compute-intensive workloads quickly and cost-effectively.

Learn more in the detailed guide about Google Cloud pricing

Related product offering: Finout | Enterprise-Grade FinOps Platform

Offered by Finout

Snowflake Cost Optimization

Snowflake provides a usage-based pricing model where costs are primarily driven by compute and storage consumption. While its scalability and flexibility offer performance benefits, improper use can quickly lead to unnecessary expenses. Effective cost optimization in Snowflake requires configuring compute resources properly, managing data lifecycle, and enforcing usage policies across teams.

Snowflake cost optimization best practices include:

Enable auto-suspend and auto-resume on all virtual warehouses to avoid paying for idle compute
Right-size virtual warehouses based on workload patterns and query performance
Use multi-cluster warehouses only when necessary for high-concurrency workloads
Avoid over-provisioning compute for ETL/ELT jobs; schedule and sequence tasks efficiently
Encourage reuse of query results through Snowflake’s automatic result caching
Periodically clean up unused tables, transient stages, and old data with no recent access
Configure resource monitors to enforce usage limits and prevent unexpected spending
Only apply manual clustering where it leads to significant query performance gains
Use external tables or Iceberg tables to store infrequently accessed data at lower cost

Learn more in the detailed guide about Snowflake pricing

Related product offering: Finout | Enterprise-Grade FinOps Platform

Offered by Finout

Databricks Cost Optimization

Databricks charges for both compute and storage, with compute typically being the most significant cost driver. Since clusters are billed by the minute, efficient resource management and workload tuning are key to minimizing expenses. Databricks offers built-in tools and configurations that help manage costs, but teams need to actively monitor usage patterns and optimize settings to avoid waste.

Databricks cost optimization best practices include:

Enable auto-termination on clusters to prevent idle compute from running indefinitely
Use job clusters for scheduled workloads instead of interactive clusters to reduce long-running session costs
Right-size clusters by selecting appropriate instance types and scaling parameters for your workloads
Prefer spot instances when workloads are fault-tolerant to save significantly on compute costs
Use cluster pools to reduce startup time and avoid idle compute between jobs
Optimize data file sizes and formats (e.g., Delta Lake, Parquet) for better performance and reduced I/O costs
Cache intermediate results only when reused across multiple operations; avoid unnecessary caching
Use cost monitoring features like the Databricks cluster usage reports or integrations with cloud cost tools (e.g., AWS Cost Explorer or Azure Cost Management)
Archive or delete unused notebooks, tables, and logs to reduce storage bloat and costs

Learn more in the detailed guide about Databricks Pricing

Multi Cloud Strategy and FinOps

With most organizations utilizing at least 2 cloud vendors, having a multi-cloud strategy is key to organization’s FinOps success. With a multi-cloud approach, organizations can combine, compare and contrast the costs and features of different cloud providers, and can make informed decisions about which provider is the best fit for each workload or service. This can help organizations optimize their cloud costs and avoid overspending on unnecessary services.

However, implementing a multi-cloud strategy can also create additional complexity for FinOps teams. They will need to monitor and track costs across multiple cloud providers, which is far more challenging than monitoring costs for a single provider. Additionally, the cost structures of different cloud providers can vary significantly, which make it difficult to compare costs and make informed decisions.

To address these challenges, FinOps teams can use tools and automation to monitor, synthesize and track cloud costs across multiple providers. They can also establish processes for comparing costs and evaluating the best provider for each workload or service. Additionally, FinOps teams can work with other departments within the organization to establish governance and compliance processes that ensure all cloud spending aligns with the organization’s overall financial objectives.

Notable FinOps Solutions

1. Finout

Finout is a cloud cost management platform designed for enterprise-scale FinOps practices. It connects billing data from multiple cloud providers and SaaS tools into a single layer, providing detailed cost allocation, forecasting, and reporting without requiring strict tagging. Finout focuses on unit economics, enabling teams to track and optimize costs by product, feature, or customer.

Key features include:

Agentless integration with AWS, GCP, Azure, Kubernetes, and popular SaaS tools
Business mapping engine for cost attribution without relying exclusively on tags
Real-time unit cost analysis (e.g., cost per API call, cost per customer)
Custom dashboards and reports for different stakeholders (engineering, finance, product)
Cost guardrails and anomaly alerts based on historical behavior and budget thresholds
Support for chargebacks, showbacks, and shared cost models across departments

2. Harness Cloud Cost Management

Harness Cloud Cost Management helps engineering and finance teams monitor and optimize cloud spending across AWS, GCP, and Azure environments. It integrates with CI/CD pipelines and Kubernetes clusters to correlate cost data with deployments, services, and features. This context-aware approach allows teams to track spend by business unit, product, or environment, and identify cost anomalies tied directly to engineering activity.

Key features include:

Real-time cost visibility across multi-cloud and Kubernetes environments
Cost allocation by service, team, environment, or business unit
Integration with CI/CD pipelines for spend-to-deployment mapping
Anomaly detection and proactive cost alerts
Usage-based recommendations to reduce waste

Learn more in the detailed guide to Harness cloud management

Related product offering: Finout | Enterprise-Grade FinOps Platform

Offered by Finout

3. Datadog Cloud Cost Management

The Datadog Cloud Cost Management (CCM) solution enables engineering, FinOps and finance teams to view cloud spend in real time alongside performance and telemetry data. By integrating billing and observability, teams can assign cost to services, teams or products, get alerts for anomalies, and receive recommendations for resource optimization across multi‑cloud environments.

Key features include:

Unified dashboards showing cost + performance metrics across AWS, Azure, Google Cloud.
Automated cost‑recommendations to identify idle, orphaned or over‑provisioned resources.
Granular cost attribution by service, product, team, environment using tagging and custom allocation rules.
Budgeting, anomaly alerts and root‑cause detection on cost spikes.
Integration with observability workflows so engineering teams can act directly on cost data.

Learn more in the detailed guide about Datadog Pricing

Related guides

Authored by Finout

Related technology updates:

[Webinar] Mastering Kubernetes Spend-Efficiency

4. IBM Cloudability

IBM Cloudability (formerly Apptio Cloudability) is a FinOps solution that helps organisations ingest cloud billing, usage and tag data, and convert it into actionable insights, dashboards and cost‑optimisation recommendations. It supports multi‑cloud environments and aims to improve cost allocation, commitment usage and resource efficiency.

Key features include:

Custom dashboards and reports to explore usage and cost across services and resources.
Commitment portfolio management (reserved instances, savings plans) with recommendations to improve utilisation.
Multi‑cloud cost tracking, usage monitoring, and cost‑forecasting capabilities.
Tag explorer and cost driver tools to uncover untagged spend and drive accountability.
Role‑based access and workflows to allow finance, IT, engineering teams to collaborate under a shared model.

5. Flexera One

Flexera One is a comprehensive platform for hybrid IT management and cloud cost optimization. It supports FinOps processes across cloud, SaaS, licensing and on‑premises assets, enabling organisations to gain visibility, optimize spend and establish governance across their cloud environments.

Key features include:

Multi‑cloud visibility: ingestion and consolidation of spend across major public clouds (AWS, Azure, GCP, Oracle) + SaaS.
Automated cost allocation and “show‑back” capabilities to attribute costs to teams, projects or business units.
Resource rightsizing and discount‑optimisation recommendations (e.g., savings plans, commitments).
Budgeting, forecasting, anomaly detection and cloud sustainability/carbon visibility.
Governance and FinOps enablement: linking technology, finance and business in shared cost‑control model.

FinOps Best Practices

Have a FinOps Plan Before Migrating to the Cloud

A successful FinOps implementation begins before any cloud resource is provisioned. Organizations should integrate financial accountability into their cloud migration strategy by defining cost management goals, processes, and tooling upfront. This includes determining how costs will be tracked, how teams will be held accountable, and which reports and KPIs will measure financial performance.

Start by identifying business units, projects, or applications that will migrate to the cloud, and align them with cost centers. Establish governance standards such as mandatory tagging policies, automated alerts for budget thresholds, and clear ownership for each resource. Define how you will allocate shared service costs, such as networking or monitoring, across consuming teams.

By planning FinOps in advance, organizations can avoid misaligned expectations, poor tagging practices, and untracked cost growth. Early involvement from finance, engineering, and operations ensures that cost efficiency is baked into the migration process—not retrofitted after costs surge.

Gain Visibility Over Actual Costs

Gaining accurate and real-time visibility into cloud spending is the foundation of FinOps. Teams must be able to trace every dollar of cloud spend back to the projects, services, and teams responsible for it. This starts with implementing a robust tagging strategy across all cloud resources. Tags should include metadata such as environment, owner, application, department, and cost center.

Use native tools like AWS Cost Explorer, Azure Cost Management, or GCP Billing Reports to segment costs by tag, service, and usage pattern. These tools can generate detailed reports and identify anomalies such as unexpected spikes in compute usage or underutilized storage.

Integrate cost data into dashboards and alerting systems to ensure that teams are notified of overspend or untagged resources in real time. Enable regular reviews where engineering and finance teams jointly examine spending data and evaluate whether current usage aligns with budget expectations. Without comprehensive visibility, teams operate blindly and optimization efforts become reactive instead of strategic.

Make FinOps a Continuous Practice

FinOps is not a one-off project—it is an ongoing cycle of measurement, evaluation, and improvement. Organizations must embed cost management into daily operations by integrating FinOps practices into development workflows, deployment pipelines, and infrastructure reviews.

Continuous FinOps involves regular cost reviews, dynamic budgeting based on usage trends, and automated optimization. Teams should perform weekly or monthly cost and usage assessments to ensure alignment with business objectives. Encourage feedback loops where engineers are informed of the financial impact of their infrastructure choices, and finance teams understand the technical constraints of workloads.

Leverage automation to enforce policies—such as turning off idle environments outside working hours, rightsizing underutilized instances, or enforcing data retention policies. Set up KPIs to track optimization progress, such as savings captured per quarter, cost per transaction, or percentage of resources with complete tags.

This practice turns FinOps into a shared responsibility across teams, promoting a culture of cost ownership and continuous improvement.

Set Roles and Responsibilities

Successful FinOps requires a clearly defined organizational structure with distributed accountability. Without clear ownership, cloud cost management becomes fragmented, with no one responsible for overruns or inefficiencies.

Assign specific FinOps roles within finance, engineering, and operations. For example, designate FinOps champions or business partners who collaborate across teams to track spending, resolve anomalies, and drive cost-saving initiatives. Engineers should be responsible for tagging their resources correctly and selecting cost-efficient configurations. Product managers should track the unit cost of features and services.

Establish accountability mechanisms such as showback (sharing cost data with teams) or chargeback (allocating actual costs to cost centers). Define escalation paths for budget deviations and procedures for approving high-cost changes.

By institutionalizing roles and responsibilities, organizations create a framework where every team member understands their role in managing cloud spend—and is empowered to act on it.

Estimate the Total Cost of Open Source and Proprietary Software

Open source software is often perceived as "free," but deploying it in the cloud introduces costs related to compute, storage, networking, and maintenance. Proprietary tools, meanwhile, may have licensing fees, per-user charges, or consumption-based billing that can escalate quickly.

To accurately compare solutions, teams must calculate the total cost of ownership (TCO) for each software choice. This includes not just direct infrastructure costs, but also indirect costs such as patching, scaling, support, integration, monitoring, and compliance.

For example, hosting an open-source database may require high-availability configurations, backup strategies, and manual upgrades—all of which increase operational costs. In contrast, a managed service may cost more in licensing, but eliminate overhead and reduce downtime.

FinOps teams should work with engineering and procurement to analyze usage patterns, licensing models, and operational requirements. Use TCO calculators or models to evaluate cost trade-offs and ensure that the selected tools align with long-term financial and technical objectives.

Cloud Cost Optimization with Finout

Finout is an enterprise-grade FinOps platform designed to provide a single, unified source of truth for all cloud and SaaS expenditures. It enables organizations to transition from reactive cost tracking to proactive financial management by connecting disparate billing data into a cohesive Data Layer.

Key Features for Optimization

Unified Multi-Cloud Visibility: Finout offers agentless integration with major providers, including AWS, GCP, and Azure, as well as Kubernetes and various SaaS tools.
MegaBill and Virtual Tagging: Through its patented Instant Virtual Tagging, Finout allows for detailed cost attribution and business mapping without requiring organizations to have a perfect tagging strategy in place.
Unit Economics: The platform focuses on unit cost analysis, enabling teams to track high-level business metrics such as cost per customer, cost per API call, or cost per feature.
Advanced Governance: Finout provides cost guardrails and anomaly detection based on historical behavior and budget thresholds to prevent unexpected spend.
Operational Accountability: The platform supports automated chargebacks and showbacks, ensuring that shared costs are distributed accurately across departments and cost centers.

Strategic Value

By integrating Finout into your FinOps practice, engineering, finance, and product teams gain access to custom dashboards tailored to their specific needs. This alignment fosters a culture of financial accountability, allowing teams to optimize their cloud footprints while maintaining the speed and agility required for modern development.