10 Best Alternatives to ProsperOps

Introduction

Cloud cost optimization has evolved far beyond simple commitment management. While automated Reserved Instance and Savings Plan optimization remains important, modern organizations need comprehensive platforms that provide deep cost visibility, multi-cloud support, team collaboration, and actionable automation across their entire cloud infrastructure.

ProsperOps is a popular platform for automated commitment management across AWS, Azure, and Google Cloud, but it may not be the right fit for every organization. Many companies look for alternatives that offer broader cost optimization capabilities, better visibility into overall cloud spending, or tools that extend beyond commitment management alone.

This guide explains what ProsperOps offers and compares leading alternatives. It will help you understand the differences and choose the cloud cost optimization platform that fits your organization best.

This analysis is based on publicly available information, including customer reviews and insights from platforms such as G2, Gartner, documentation, and similar industry sources.

What is ProsperOps?

ProsperOps is a cloud cost optimization platform focused on automated commitment management across AWS, Azure, and Google Cloud. Its goal is to remove the manual effort and risk involved in commitment purchasing by letting algorithms make decisions automatically.

The platform continuously adjusts commitment coverage based on real usage, using techniques like Adaptive Laddering and its Effective Savings Rate (ESR) to reduce over-commitment and wasted spend.

ProsperOps's Limitations

While ProsperOps excels at automated commitment management, users commonly note several limitations based on customer reviews from G2 and similar platforms.

Initial setup and understanding the platform

Users report that the initial setup and understanding the logic behind ProsperOps' cost-saving approach can be challenging. Additionally, some of the billing and purchase transactions can be difficult to sort out and explain to others, especially for teams needing to justify or communicate cloud spending decisions to finance or leadership.

Limited database and managed service coverage

While ProsperOps lists support for some database services like RDS and ElastiCache, users consistently report that coverage for relational database services is limited or insufficient compared to compute optimization. Users would like to see expanded support for additional AWS managed services such as DynamoDB, RDS, and ElastiCache with more comprehensive optimization for database workloads.

Limited to Regional Reserved Instances only

ProsperOps only includes Regional Reserved Instances and does not support on-demand capacity reservations. Organizations that need specific capacity reservations for availability zones must manage those separately through AWS, creating additional manual work and complexity in their commitment strategy.

Limited observability and third-party integrations

ProsperOps operates as a reselling platform focused exclusively on cloud provider commitments. It only supports AWS, GCP, and Azure native services and does not integrate with third-party platforms such as OpenAI, Datadog, Snowflake, MongoDB Atlas, or other SaaS tools that contribute to cloud costs. This means organizations cannot get a complete view of their total cloud and infrastructure spending in one place. The platform lacks observability features and detailed cost analytics, making it difficult for teams that need comprehensive visibility across their entire technology stack or advanced reporting and customization capabilities.

Pricing transparency concerns

Estimating costs in advance can be challenging due to limited pricing transparency. The percentage-based model makes it difficult to forecast exact costs before seeing results, which can complicate budgeting and ROI projections.

Less effective for spiky or variable workloads

For companies with highly variable or spiky EC2 usage patterns, the automated commitment approach may not deliver expected savings. The platform works best with stable, predictable workloads where long-term commitment patterns can be established.

1. Cloudchipr

Cloudchipr is an enterprise-grade cloud cost management and optimization platform designed to help organizations allocate, manage, and reduce cloud spending.

Cloudchipr goes beyond reporting by connecting costs to business metrics, live resources, and automated actions. Users can track unit costs, manage commitments, identify waste, and automate workflows, all from one platform.

Below are the key features that make Cloudchipr a practical choice for continuous cloud cost control.

Dimensions

Dimensions allow teams to organize and analyze cloud costs in a way that matches how their business is structured. Each Dimension is made up of categories, which are groups of rules that act like virtual tags, grouping resources without requiring perfect tagging.

Categories use attributes to automatically place costs in the right group as environments change. This keeps cost views accurate and consistent without manual updates.

The Shared Cost feature then distributes shared expenses across teams or projects based on manual, even split, or telemetry allocation, ensuring complete and reliable cost attribution.

Telemetry Upload & Unit Cost Economics

Teams can upload their data, like users, customers, API calls, revenue, etc.

Uploaded data can be used for weighted-based cost allocation in Dimensions, allowing shared costs to be distributed based on usage instead of just percentages.

Teams can use uploaded data to calculate unit cost economics on the widget, which shows trends and forecasts over time, helping teams understand how cloud spend scales with growth.

Commitments

Cloudchipr provides a single, clear view of all cloud commitments, including Reserved Instances and Savings Plans, in one place. Teams can easily track coverage, utilization, real cost, net savings, and effective savings rate across selected time ranges.

Cloudchipr shows which resource types and accounts are covered, provides visibility into actual usage, and highlights where commitments are underutilized or missing.

Cloudchipr also provides AI recommendations to improve coverage and reduce on-demand spend.

Billing Explorer

Cloudchipr's Billing Explorer gives users a fast, flexible way to understand and manage multi-cloud costs in one place. It provides a complete view of cloud spend, including Kubernetes container costs, and allows users to group and filter data by account, service, region, tag, workload, or any preferred dimension.

From high-level reports, users can drill down to individual resources to see usage metrics, tags, and related savings opportunities, making it easy to understand what is driving costs and where action is needed.

Users can see forecasted costs, set budgets directly from reports, and get notified before limits are exceeded. AI-powered anomaly detection and plain-language explanations help quickly understand cost spikes without manual investigation.

Dashboards

Users can create dashboards directly from billing reports or custom filters, making it easy to track key metrics over time and tailor views for different teams or stakeholders. Dashboards are fully customizable with a wide range of widgets, including Billing Explorer, Cost Anomalies, Commitment Utilization, Coverage, Normalized Cost, Savings Opportunities, Carbon Emissions, Unit Cost, and more, giving finance, engineering, and leadership clear visibility into the metrics that matter most.

Users can share dashboards across teams or subscribe to them, receiving scheduled updates automatically via email or Slack.

Budgets and Alerts

Cloudchipr provides powerful and flexible budgets and alerts that help teams stay in control of cloud spending before issues become problems.

Budgets can be created from billing reports or custom filters, aligning them with how cloud spend is organized across the environment. Users can track both actual and forecasted spend and receive alerts before limits are exceeded.

Anomaly alerts detect unusual changes by comparing current costs with previous periods. This helps teams quickly spot unexpected spikes and investigate issues before they grow into larger problems.

Commitment alerts monitor Reserved Instances and Savings Plans, sending early warnings to avoid underutilization, missed renewals, and unnecessary on-demand spend.

Live Resources

Cloudchipr's Live Resources gives a real-time, unified view of all cloud resources across AWS, Azure, GCP, and Kubernetes. Users can see usage metrics in one place, without switching between cloud consoles. Each resource is fully actionable; users can stop, start, reboot, snapshot, delete, or protect resources, and see related costs, usage history, commitment coverage, and savings opportunities.

Live Resources also includes utilization policies that automatically identify idle or underutilized resources. Users can take action immediately, collaborate via Slack or tasks, or automate cleanups with workflows.

Kubernetes Cost Optimization

Cloudchipr provides deep visibility and optimization for Kubernetes across all major cloud providers. Users can monitor clusters, namespaces, nodes, pods, and workloads in real time, with clear views of CPU, GPU, and memory usage compared to what is actually consumed.

Cloudchipr highlights underutilized Kubernetes resources and delivers AI-powered optimization recommendations to reduce waste early.

Automation

Cloudchipr helps companies automate cost-saving actions without writing code. It offers two types of automation: Workflows and Off-Hours. Workflows monitor resources and automatically take actions based on custom conditions.

Off-Hours automations schedule resources to stop and start at defined times to avoid paying for unused capacity outside business hours.

Cloudchipr AI Agents

Cloudchipr AI Agents act as on-demand FinOps and DevOps experts inside the platform. Users can ask questions in plain language to understand costs, explain reports, analyze trends, and generate charts or reports.

Explain with AI highlights cost trends, top drivers, unusual spikes in any report and suggested next steps.

Built-in collaboration and task management

Cloudchipr treats cloud cost optimization as teamwork, not just a FinOps task.

When an issue is found, users can create tasks directly from live resources or savings opportunities, assign owners, give priorities and due dates, and track until completion. This makes it easy for teams to collaborate with clear accountability and shared visibility.

2. Finout

Finout is a cloud cost management and FinOps platform that helps organizations gain clear visibility into their cloud spending and allocate costs accurately across teams, products, and customers. It aggregates cost and usage data from multiple cloud providers such as AWS, Azure, and Google Cloud, along with Kubernetes and selected SaaS tools, into a single unified cost ledger.

The platform emphasizes cost allocation and business context. It enables companies to map cloud spend to real business units like services, features, customers, or environments rather than relying only on accounts or basic tags. Through customizable dashboards, anomaly detection, and unit economics metrics such as cost per customer or cost per request, Finout helps engineering, finance, and product teams understand where cloud costs originate and how they change over time.

Pros

• Multi-cloud cost visibility across AWS, Azure, and GCP
• Strong cost allocation using Virtual Tags and business context
• Unit economics metrics to track cost per customer or service
• Anomaly detection and cost alerts
• Unified view of cloud and some SaaS tool costs
• Helpful for chargeback and showback workflows

Cons

• Steep learning curve for advanced features
• UI can feel complex with many filters and options
• Limited automation and execution of optimization actions
• Commitment management features could be more accurate and automated
• API capabilities are basic, limiting deep integrations
• Performance can slow with very large datasets

Best For

Organizations that need strong cost allocation, business context mapping, and visibility across multiple cloud providers and want to connect cloud costs to unit economics.

3. Anodot

Anodot is an AI-driven analytics and cost management platform that focuses on real-time anomaly detection, forecasting, and alerts. It analyzes metrics and cost data to detect unusual behavior and notify teams before issues grow into larger problems.

In cloud cost management, Anodot provides visibility into cloud spend, usage, and trends across multiple cloud providers, Kubernetes environments, and some SaaS tools.

Pros

• AI-based anomaly detection for costs and metrics
• Real-time alerts that help teams catch issues early
• Multi-cloud visibility
• Forecasting and trend analysis capabilities
• Recommendations for identifying waste and inefficiencies
• Helps reduce manual monitoring effort

Cons

• User interface can feel complex or cluttered, especially for new users
• Steep learning curve for non-technical users
• Limited role-based access control and governance features
• Custom alert configuration can be restrictive
• Pricing can be high for smaller teams
• Execution and automation of cost optimization actions are limited
• Better at detection than at driving cost-saving actions to completion

Best For

Anodot is best suited for organizations that need advanced anomaly detection and real-time monitoring of cloud costs and business metrics. It works well for teams that want to detect cost spikes, usage changes, or unusual patterns quickly and rely on AI-driven insights.

4. Zesty

Zesty is a cloud cost optimization platform focused mainly on AWS compute savings. Its core strength is automated commitment management, especially for EC2 Reserved Instances. Zesty uses machine learning to automatically buy, sell, and manage commitments on behalf of customers, helping them reach high coverage without long-term lock-in.

While Zesty delivers strong results for commitment optimization, it is not a full FinOps platform. Cost visibility, reporting, and broader optimization workflows are more limited compared to end-to-end cloud cost management tools.

Pros

• Automation for EC2 Reserved Instance management
• Minimal ongoing effort after setup
• Responsive customer support
• Clear value for AWS compute-heavy workloads
• Complements existing Savings Plans well

Cons

• Focused mainly on AWS compute, with limited support for other services
• Limited visibility into overall cloud spend and cost drivers
• Dashboards and reporting lack depth and customization
• Little support for budgeting, forecasting, or collaboration
• Not designed for multi-cloud environments

Best For

Zesty is best suited for AWS-focused organizations that want to maximize EC2 savings with minimal effort.

5. nOps

nOps is an AWS-focused cloud cost optimization platform designed to automate savings and reduce manual FinOps work. It continuously analyzes AWS usage and applies cost-saving strategies using Reserved Instances, Savings Plans, and Spot Instances. The platform is built to help teams reduce on-demand usage and improve commitment utilization with minimal ongoing effort.

A key part of nOps is automation. It manages Savings Plans and RI lifecycles, selects cost-effective compute options, and identifies idle or underutilized resources. nOps also includes basic security and compliance monitoring, making it a combined cost and operations tool for AWS environments.

Pros

• Automation for Savings Plans and Reserved Instances
• Effective use of Spot Instances to reduce compute costs
• Reduces manual work around commitment management
• Visibility into AWS cost savings and utilization
• Useful for EC2, EKS, and some RDS optimization

Cons

• Limited to AWS-only environments
• Some automation can feel heavy or hard to control
• Reporting and forecasting features are basic
• Customization options are limited compared to larger FinOps platforms
• UI and features can feel inconsistent or less polished
• Advanced features come at a higher cost

Best For

nOps is best suited for AWS-centric organizations that want automated, hands-off cost optimization, especially for EC2 and compute-heavy workloads.

6. Cast.ai

CastAI is a Kubernetes automation and cost optimization platform focused on reducing cloud costs while improving cluster efficiency. It automatically manages node provisioning, autoscaling, rightsizing, and Spot Instance usage across Kubernetes clusters running on AWS, Azure, and Google Cloud.

Instead of only showing recommendations, CastAI actively takes control of cluster optimization. It analyzes workloads in real time and replaces nodes, rebalances clusters, and selects the most cost-effective instance types without manual intervention.

Pros

• Automation for Kubernetes cost optimization
• Automatic node selection, scaling, and rebalancing in real time
• Supports AWS EKS, Azure AKS, and Google GKE
• Works with Infrastructure as Code tools like Terraform and Helm
• Visibility into Kubernetes costs at cluster, namespace, and workload level
• Built-in security scanning and policy controls

Cons

• Focused almost entirely on Kubernetes, not full cloud cost management
• Initial setup and governance features can have a learning curve
• Policy configuration and permissions are limited for large, shared environments
• UI and reporting can feel basic for financial or executive users
• Pricing may feel high for small clusters or teams
• Less suitable for organizations that want manual control instead of automation

Best For

CastAI is suited for Kubernetes-heavy organizations that want hands-off, automated cost optimization without managing cluster scaling and node selection manually.

7. CloudCheckr

CloudCheckr is an enterprise-focused cloud governance and optimization platform designed to help organizations manage cloud costs, security, compliance, and resource efficiency across AWS, Azure, and Google Cloud. It is now part of the Flexera / Spot FinOps portfolio and is widely used by large enterprises, public sector organizations, and managed service providers.

CloudCheckr combines cost optimization with policy-driven governance. It identifies idle resources, supports rightsizing, manages commitments, and enables automated remediation for security and compliance issues. The platform includes a large set of built-in best-practice rules, detailed reporting, and cross-account visibility, making it suitable for complex and regulated environments.

Pros

• Multi-cloud visibility across AWS, Azure, and GCP
• Broad cost optimization features, including idle resource detection and rightsizing
• Automated remediation for security and compliance issues
• Supports many compliance frameworks and audit requirements
• Combines cost, security, and governance in one platform

Cons

• Steep learning curve due to the number of features and options
• Navigation and UI are often described as hard to use or slow
• Setup and configuration require time and process
• Automated rightsizing and commitment calculations are not always precise
• Can feel like overkill for small or fast-moving engineering teams

Best For

CloudCheckr is best suited for large enterprises, public sector teams, and MSPs that need strong governance, compliance, and standardized cost controls across multi-cloud environments.

8. Datadog

Datadog is a comprehensive observability and monitoring platform that unifies metrics, logs, traces, and application performance monitoring (APM) in a single interface. It provides real-time visibility across infrastructure, applications, and microservices architectures. The platform integrates seamlessly with major cloud providers and supports container-based, server-based, and web-based applications.

Datadog also includes a Cloud Cost Management module, which allows teams to view cost data alongside performance metrics.

Pros

• Combines observability and cost data in one platform
• Easy to link cost changes with performance issues
• Strong Kubernetes and container-level cost visibility
• Built-in anomaly detection and cost alerts
• Works well for engineering-led teams already using Datadog

Cons

• Cost management features are secondary to monitoring
• Limited FinOps workflows, budgeting, and forecasting
• Optimization and execution capabilities are basic
• Pricing grows quickly as data ingestion increases
• Overkill for teams that need only cloud cost management

Best For

Organizations already using Datadog for observability and wanting basic cost visibility inside engineering workflows.

9. CloudKeeper

CloudKeeper is a cloud cost optimization platform that combines a software platform with hands-on cloud expertise. It focuses mainly on AWS and Google Cloud and helps organizations reduce cloud spend through discounts, commitment management, cost optimization insights, and ongoing support.

Many teams use CloudKeeper not just as a tool, but as an extension of their cloud or FinOps team. In addition to dashboards and cost visibility, CloudKeeper provides guidance, technical help, and billing support, making it easier for teams to manage cloud costs without deep in-house FinOps expertise.

Pros

• Focus on cost savings, including commitment-based discounts
• Good visibility into AWS usage and spending
• Optimization recommendations for unused or underutilized resources
• Responsive support team
• Helpful for billing, invoicing, and cost justification

Cons

• Primarily focused on AWS and Google Cloud, with limited broader multi-cloud support
• Execution of recommendations often requires manual effort
• Dashboards and reports have limited customization
• Optimization recommendations cannot always be filtered or dismissed
• UI and reporting experience can be improved
• Initial setup may feel complex without guidance

Best For

CloudKeeper is best suited for organizations that want hands-on support, especially teams running mainly on AWS.

10. DoiT

DoiT is a cloud intelligence and FinOps platform that helps organizations manage cloud costs, improve performance, and optimize reliability across AWS and Google Cloud, with growing multi-cloud support. DoiT is often used not only as a tool, but also as a cloud partner, providing access to experienced engineers and architects.

The platform focuses on cost visibility, reporting, anomaly detection, and optimization insights.

Pros

• Cost allocation and anomaly detection
• Easy onboarding and fast integration
• Strong technical support and cloud expertise
• Helpful for rightsizing, performance tuning, and architecture guidance
• Combines tooling with expert, on-demand assistance

Cons

• Platform UI and navigation can feel confusing
• Reporting filters and dashboards take time to get used to
• Cost and anomaly data may not always be near real time
• Forecasting accuracy is limited
• Automation is more advisory than execution-driven
• Some features depend heavily on human involvement
• Billing and contract structure can feel complex

Best For

DoiT is best suited for organizations that value expert support alongside tooling, especially teams running on AWS or GCP that need help with cost optimization, architecture decisions, and cloud provider interactions.

Conclusion

The alternatives covered in this guide address different needs, from specialized commitment automation tools to comprehensive FinOps platforms with multi-cloud support, deep visibility, and execution capabilities.

While ProsperOps excels at hands-off AWS commitment optimization, organizations often need more than just automated RI and Savings Plan management. Choosing the right solution depends on whether you need broader cost visibility, multi-cloud support, Kubernetes optimization, team collaboration features, and the ability to identify and act on savings opportunities beyond commitments.

For teams looking to move beyond narrow commitment automation toward complete cloud cost control, platforms like Cloudchipr offer a more comprehensive approach, combining visibility, allocation, forecasting, alerts, automated actions, and team collaboration in a single unified workflow.