Alternatives to Pepperdata
Compare Pepperdata alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Pepperdata in 2026. Compare features, ratings, user reviews, pricing, and more from Pepperdata competitors and alternatives in order to make an informed decision for your business.
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1
Google Compute Engine
Google
Compute Engine is Google's infrastructure as a service (IaaS) platform for organizations to create and run cloud-based virtual machines. Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications. Integrate Compute with other Google Cloud services such as AI/ML and data analytics. Make reservations to help ensure your applications have the capacity they need as they scale. Save money just for running Compute with sustained-use discounts, and achieve greater savings when you use committed-use discounts. -
2
RunPod
RunPod
RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure. -
3
StarTree
StarTree
StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning: - Data Volume: query performance sustained at petabyte scale - Ingest Rates: millions of events per second, continuously indexed for freshness - Concurrency: thousands to millions of simultaneous users served with sub-second latency With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.Starting Price: Free -
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Amazon CloudWatch
Amazon
Amazon CloudWatch is a monitoring and observability service built for DevOps engineers, developers, site reliability engineers (SREs), and IT managers. CloudWatch provides you with data and actionable insights to monitor your applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services that run on AWS and on-premises servers. You can use CloudWatch to detect anomalous behavior in your environments, set alarms, visualize logs and metrics side by side, take automated actions, troubleshoot issues, and discover insights to keep your applications. CloudWatch alarms watch your metric values against thresholds that you specify or that it creates using ML models to detect anomalous behavior. -
5
AWS Auto Scaling
Amazon
AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost. Using AWS Auto Scaling, it’s easy to setup application scaling for multiple resources across multiple services in minutes. The service provides a simple, powerful user interface that lets you build scaling plans for resources including Amazon EC2 instances and Spot Fleets, Amazon ECS tasks, Amazon DynamoDB tables and indexes, and Amazon Aurora Replicas. AWS Auto Scaling makes scaling simple with recommendations that allow you to optimize performance, costs, or balance between them. If you’re already using Amazon EC2 Auto Scaling to dynamically scale your Amazon EC2 instances, you can now combine it with AWS Auto Scaling to scale additional resources for other AWS services. With AWS Auto Scaling, your applications always have the right resources at the right time. -
6
Datadog
Datadog
Datadog is the monitoring, security and analytics platform for developers, IT operations teams, security engineers and business users in the cloud age. Our SaaS platform integrates and automates infrastructure monitoring, application performance monitoring and log management to provide unified, real-time observability of our customers' entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior and track key business metrics.Starting Price: $15.00/host/month -
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Dynatrace
Dynatrace
The Dynatrace software intelligence platform. Transform faster with unparalleled observability, automation, and intelligence in one platform. Leave the bag of tools behind, with one platform to automate your dynamic multicloud and align multiple teams. Spark collaboration between biz, dev, and ops with the broadest set of purpose-built use cases in one place. Harness and unify even the most complex dynamic multiclouds, with out-of-the box support for all major cloud platforms and technologies. Get a broader view of your environment. One that includes metrics, logs, and traces, as well as a full topological model with distributed tracing, code-level detail, entity relationships, and even user experience and behavioral data – all in context. Weave Dynatrace’s open API into your existing ecosystem to drive automation in everything from development and releases to cloud ops and business processes.Starting Price: $11 per month -
8
Splunk AppDynamics
Cisco
Splunk AppDynamics delivers full-stack observability for hybrid and on-prem environments, linking technical performance directly to business outcomes. It enables teams to detect anomalies, diagnose root causes, and prioritize issues based on their real business impact. With capabilities ranging from network performance correlation to SAP system optimization, the platform offers deep insights across applications, APIs, and infrastructure. Its runtime security features safeguard applications by detecting vulnerabilities, blocking attacks, and highlighting potential risks. AppDynamics also enhances digital experiences with web, mobile, and synthetic monitoring to understand user journeys. By unifying performance, security, and business analytics, Splunk AppDynamics helps enterprises reduce costs, prevent outages, and deliver seamless customer experiences.Starting Price: $6 per month -
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Zipher
Zipher
Zipher is an autonomous optimization platform specifically designed to improve the performance and cost efficiency of Databricks workloads by eliminating manual tuning and resource management and continuously adjusting clusters in real time. It uses proprietary machine learning models and the only Spark-aware scaler that actively learns and profiles workloads to adjust cluster resources, select optimal configurations for every job run, and dynamically tune settings like hardware, Spark configs, and availability zones to maximize efficiency and cut waste. Zipher continuously monitors evolving workloads to adapt configurations, optimize scheduling, and allocate shared compute resources to meet SLAs, while providing detailed cost visibility that breaks down Databricks and cloud provider costs so teams can identify key cost drivers. It integrates seamlessly with major cloud service providers including AWS, Azure, and Google Cloud and works with common orchestration and IaC tools. -
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CAST AI
CAST AI
CAST AI is an automated Kubernetes cost monitoring, optimization and security platform for your EKS, AKS and GKE clusters. The company’s platform goes beyond monitoring clusters and making recommendations; it utilizes advanced machine learning algorithms to analyze and automatically optimize clusters, saving customers 50% or more on their cloud spend, and improving performance and reliability to boost DevOps and engineering productivity.Starting Price: $200 per month -
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StormForge
StormForge
StormForge Optimize Live continuously rightsizes Kubernetes workloads to ensure cloud-native applications are both cost effective and performant while removing developer toil. As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the Kubernetes horizontal pod autoscaler (HPA) at enterprise scale. Optimize Live addresses both over- and under-provisioned workloads by analyzing usage data with advanced machine learning to recommend optimal resource requests and limits. Recommendations can be deployed automatically on a flexible schedule, accounting for changes in traffic patterns or application resource requirements, ensuring that workloads are always right-sized, and freeing developers from the toil and cognitive load of infrastructure sizing. Organizations see immediate benefits from the reduction of wasted resources — leading to cost savings of 40-60% along with performance and reliability improvements across the entire estate.Starting Price: Free -
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Deliver enterprise-class management for running compute and data-intensive distributed applications on a scalable, shared grid. IBM Spectrum Symphony® software delivers powerful enterprise-class management for running compute-intensive and data-intensive distributed applications on a scalable, shared grid. It accelerates dozens of parallel applications for faster results and better utilization of all available resources. With IBM Spectrum Symphony, you can improve IT performance, reduce infrastructure costs and expenses and quickly meet business demands. Get faster throughput and performance for compute-intensive and data-intensive analytics applications to accelerate time-to-results. Achieve higher levels of resource utilization by controlling and optimizing the massive compute power available in your technical computing systems. Reduce infrastructure, application development, deployment and management costs by gaining control of large-scale jobs.
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13
Zerops
Zerops
Zerops.io is a cloud platform designed for developers building modern applications, offering automatic vertical and horizontal autoscaling, granular control over resources, and no vendor lock-in. It simplifies infrastructure management with features like automated backups and failover, CI/CD integration, and full observability. Zerops.io scales seamlessly with your project’s needs, ensuring optimal performance and cost-efficiency from development to production, all while supporting microservices and complex architectures. Ideal for developers who want flexibility, scalability, and powerful automation without the complexity.Starting Price: $0 -
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Lucidity
Lucidity
Lucidity is a multi-cloud storage management platform that dynamically resizes block storage across AWS, Azure, and Google Cloud without downtime, enabling enterprises to save up to 70% on storage costs. Lucidity automates the expansion and contraction of storage volumes based on real-time data demands, ensuring optimal disk utilization between 75-80%. This autonomous, application-agnostic solution integrates seamlessly with existing applications and environments, requiring no code changes or manual provisioning efforts. Lucidity's AutoScaler is available on the AWS Marketplace, offering enterprises an automated solution to expand and shrink live EBS volumes based on workload without downtime. By streamlining operations, Lucidity enables IT and DevOps teams to reclaim hundreds of hours, allowing them to focus on higher-impact initiatives that drive innovation and efficiency. -
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NVIDIA DGX Cloud Serverless Inference is a high-performance, serverless AI inference solution that accelerates AI innovation with auto-scaling, cost-efficient GPU utilization, multi-cloud flexibility, and seamless scalability. With NVIDIA DGX Cloud Serverless Inference, you can scale down to zero instances during periods of inactivity to optimize resource utilization and reduce costs. There's no extra cost for cold-boot start times, and the system is optimized to minimize them. NVIDIA DGX Cloud Serverless Inference is powered by NVIDIA Cloud Functions (NVCF), which offers robust observability features. It allows you to integrate your preferred monitoring tools, such as Splunk, for comprehensive insights into your AI workloads. NVCF offers flexible deployment options for NIM microservices while allowing you to bring your own containers, models, and Helm charts.
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ServiceNow IT Operations Management
ServiceNow
Predict issues, reduce user impact, and automate resolutions with AIOps. Move away from reactive IT operations with insights and automation. Identify anomalies and solve issues before they occur with cross-team automation workflows. Deliver proactive digital operations with AIOps. Stop chasing false positives and identify anomalies with less guesswork. Collect and analyze telemetry data for enhanced visibility and reduced noise. Find the root cause of incidents and share actionable insights across teams. Reduce outages by taking action based on guided recommendations. Shorten recovery times by rapidly implementing solutions based on insights. Simplify repetitive tasks with pre-built playbooks and knowledge base resources. Create a performance-driven culture across teams. Give DevOps and Site Reliability Engineers (SREs) visibility into microservices to improve observability and speed up incident response. Go beyond IT operations to manage the entire digital lifecycle. -
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IBM Turbonomic
IBM
Cut infrastructure spend by 33%, reduce data center refresh costs by 75%, and get back 30% of your engineering time with smarter resource management. Increasingly, complex applications run your business. And they can run your teams ragged trying to stay ahead of dynamic demand. When application performance drops, teams are often reacting at human speed, after the fact. To avoid disruption, you may overprovision resource allocations, making estimates that are often costly and don’t always pay off. The IBM® Turbonomic® Application Resource Management (ARM) platform allows you to eliminate this guesswork, saving both time and money. You can continuously automate critical actions in real time—and without human intervention—that proactively deliver the most efficient use of compute, storage and network resources to your apps at every layer of the stack. -
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Syself
Syself
Managing Kubernetes shouldn't be a headache. With Syself Autopilot, both beginners and experts can deploy and maintain enterprise-grade clusters with ease. Say goodbye to downtime and complexity—our platform ensures automated upgrades, self-healing capabilities, and GitOps compatibility. Whether you're running on bare metal or cloud infrastructure, Syself Autopilot is designed to handle your needs, all while maintaining GDPR-compliant data protection. Syself Autopilot integrates with leading DevOps and infrastructure solutions, allowing you to build and scale applications effortlessly. Our platform supports: - Argo CD, Flux (GitOps & CI/CD) - MariaDB, PostgreSQL, MySQL, MongoDB, ClickHouse (Databases) - Grafana, Istio, Redis, NATS (Monitoring & Service Mesh) Need additional solutions? Our team helps you deploy, configure, and optimize your infrastructure for peak performance.Starting Price: €299/month -
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Xosphere
Xosphere
Xosphere Instance Orchestrator automatically performs spot optimization by leveraging AWS Spot instances to optimize the cost of your infrastructure while maintaining the same level of reliability as on-demand instances. Spot instances are diversified amongst family, size, and availability zones to minimize any impact when Spot instances are reclaimed. Instances utilizing reservations will not be replaced by Spot instances. Automatically respond to Spot termination notifications and fast-track replacement on-demand instances. EBS volumes can be configured to be attached to new replacement instances enabling stateful applications to work seamlessly. -
20
Elastigroup
Spot by NetApp
Provision, manage and scale compute infrastructure on any cloud. Save up to 80% on your costs while ensuring SLA and high-availability. Elastigroup is a cluster software, designed to optimize performance and costs. It enables companies of all sizes and verticals to reliably leverage Cloud Excess Capacity to optimize and accelerate workloads and save up to 90% on infrastructure compute costs. Elastigroup makes use of proprietary price prediction technology to deploy reliably onto Spot Instances. By predicting interruptions and fluctuations Elastigroup is able to offensively rebalance clusters to prevent interruption. Elastigroup reliably leverages excess capacity across all major cloud providers such as EC2 Spot Instances (AWS), Low-priority VMs (Microsoft Azure) and Preemptible VMs (Google Cloud), while removing risk and complexity, providing simple orchestration and management at scale. -
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Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
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Amazon EMR
Amazon
Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting. -
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Azure Databricks
Microsoft
Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO). -
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Exostellar
Exostellar
Exostellar is a self-managed AI infrastructure orchestration platform built to simplify how enterprises run heterogeneous CPU and GPU environments. It intelligently handles scaling, scheduling, and optimization so AI developers and IT teams don’t have to manage infrastructure complexity manually. Exostellar unifies orchestration, optimization, and scalability into a single adaptive layer designed for hybrid and multi-cloud environments. The platform supports advanced CPU and GPU resource management, including just-in-time provisioning and AI-assisted scheduling. With autonomous right-sizing and smart workload tuning, Exostellar helps organizations maximize infrastructure utilization. It is vendor-agnostic and avoids lock-in, giving teams full control across clusters and clouds. By boosting efficiency and reducing costs, Exostellar significantly improves ROI for enterprise AI infrastructure. -
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Amazon EC2 Auto Scaling
Amazon
Amazon EC2 Auto Scaling helps you maintain application availability and lets you automatically add or remove EC2 instances using scaling policies that you define. Dynamic or predictive scaling policies let you add or remove EC2 instance capacity to service established or real-time demand patterns. The fleet management features of Amazon EC2 Auto Scaling help maintain the health and availability of your fleet. Automation is vital to efficient DevOps, and getting your fleets of Amazon EC2 instances to launch, provision software, and self-heal automatically is a key challenge. Amazon EC2 Auto Scaling provides essential features for each of these instance lifecycle automation steps. Use machine learning to predict and schedule the right number of EC2 instances to anticipate approaching traffic changes. -
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Oracle Cloud Infrastructure (OCI) Data Flow is a fully managed Apache Spark service to perform processing tasks on extremely large data sets without infrastructure to deploy or manage. This enables rapid application delivery because developers can focus on app development, not infrastructure management. OCI Data Flow handles infrastructure provisioning, network setup, and teardown when Spark jobs are complete. Storage and security are also managed, which means less work is required for creating and managing Spark applications for big data analysis. With OCI Data Flow, there are no clusters to install, patch, or upgrade, which saves time and operational costs for projects. OCI Data Flow runs each Spark job in private dedicated resources, eliminating the need for upfront capacity planning. With OCI Data Flow, IT only needs to pay for the infrastructure resources that Spark jobs use while they are running.Starting Price: $0.0085 per GB per hour
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Alibaba Auto Scaling
Alibaba Cloud
Auto Scaling is a service to automatically adjust computing resources based on your volume of user requests. When the demand for computing resources increase, Auto Scaling automatically adds ECS instances to serve additional user requests, or alternatively removes instances in the case of decreased user requests. Automatically adjusts computing resources according to various scaling policies. Supports manual scale-in and scale-out, which offer you flexibility to control resources manually. During peak periods, automatically adds additional computing resources to the pool. When user requests decrease, Auto Scaling automatically releases ECS resources to cut down your costs -
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Convox
Convox
Convox is a powerful platform-as-a-service (PaaS) that simplifies deploying, scaling, and managing cloud applications by abstracting infrastructure complexity and letting teams focus on shipping code. It runs directly within your cloud account and integrates with major cloud providers such as AWS, Google Cloud, Azure, and DigitalOcean, giving you full control and cost efficiency while avoiding extra hosting fees. Convox supports seamless continuous integration and delivery pipelines, auto-scaling policies, and zero-downtime deployments, with tools for environment configuration, role-based access controls, and secure workflows. It includes a developer-friendly CLI, flexible deployment configuration, and integration with common tools like GitHub, GitLab, Slack, and monitoring services, streamlining workflows and boosting productivity. Convox also offers real-time monitoring, detailed logs, and one-click rollbacks for reliable performance and easier troubleshooting.Starting Price: Free -
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Spark Streaming
Apache Software Foundation
Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability. -
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MLlib
Apache Software Foundation
Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem. -
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Apica
Apica
Apica is the observability cost optimization leader helping IT teams gain complete control over their telemetry data economics. Apica Ascent processes all observability data types including metrics, logs, traces, and events while optimizing observability costs by 40% compared to traditional approaches. Unlike solutions that lock users into proprietary formats, Ascent offers true flexibility with support for any data lake of choice, on-premises or cloud deployment options, and elimination of expensive tool sprawl through modular solutions. Built to handle high-cardinality data that overwhelms competitive solutions, Ascent includes the patented InstaStore™ optimized storage technology for maximum efficiency and advanced root cause analysis capabilities. Organizations choose us to make observability investments that reduce costs instead of spiraling them out of control. -
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Sedai
Sedai
Sedai is an autonomous cloud management platform powered by AI/ML delivering continuous optimization for cloud operations teams to maximize cloud cost savings, performance and availability at scale. Sedai enables teams to shift from static rules and threshold-based automation to modern ML-based autonomous operations. Using Sedai, organizations can reduce cloud cost by up to 50%, improve performance by up to 75%, reduce failed customer interactions (FCIs) by 75% and multiply SRE productivity by up to 6X for their modern applications. Sedai can perform work equivalent to a team of cloud engineers working behind the scenes to optimize resources and remediate issues, so organizations can focus on innovation.Starting Price: $10 per month -
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Akamas
Akamas
Customers need to keep delivering high-quality services at minimum costs and at business speed. Today's applications, whether on-prem or on cloud, monolithic or microservices-based, are very complex with thousands of different parameters and hundreds of instance types to be tuned to find the optimal configuration to achieve the expected tradeoff among performance, resilience and cost efficiency. Thanks to Akamas, customers can state their custom optimization goals and constraints (e.g. SLOs) and get their applications and IT stacks tuned. Akamas customers can achieve tangible benefits: 60% decrease in infrastructure/cloud cost with the same application performance, 30% increase in transactions/sec with the same resources, 70% decrease in response time with lower peaks and fluctuations and 80% time saved for tuning. Akamas AI-powered optimization enables enterprises and online businesses to maximize service quality, resilience and cost savings. -
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VMware Avi Load Balancer
Broadcom
Simplify application delivery with software-defined load balancers, web application firewall, and container ingress services for any application in any data center and cloud. Simplify administration with centralized policies and operational consistency across on-premises data centers, and hybrid and public clouds, including VMware Cloud (VMC on AWS, OCVS, AVS, GCVE), AWS, Azure, Google, and Oracle Cloud. Free infrastructure teams from manual tasks and enable DevOps teams with self-service. Application delivery automation toolkits include Python SDK, RESTful APIs, Ansible and Terraform integrations. Gain unprecedented insights, including network, end users and security, with real-time application performance monitoring, closed-loop analytics and deep machine learning. -
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UbiOps
UbiOps
UbiOps is an AI infrastructure platform that helps teams to quickly run their AI & ML workloads as reliable and secure microservices, without upending their existing workflows. Integrate UbiOps seamlessly into your data science workbench within minutes, and avoid the time-consuming burden of setting up and managing expensive cloud infrastructure. Whether you are a start-up looking to launch an AI product, or a data science team at a large organization. UbiOps will be there for you as a reliable backbone for any AI or ML service. Scale your AI workloads dynamically with usage without paying for idle time. Accelerate model training and inference with instant on-demand access to powerful GPUs enhanced with serverless, multi-cloud workload distribution. -
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Chaos Genius
Chaos Genius
Chaos Genius is a DataOps Observability platform for Snowflake. Enable Snowflake Observability to reduce Snowflake costs and optimize query performance.Starting Price: $500 per month -
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BMC AMI Ops Automation for Capping. Automate workload capping to avoid risk and optimize costs. BMC AMI Ops Automation for Capping (formerly Intelligent Capping for zEnterprise) applies automated intelligence to manage business-critical MSU capacity settings to avoid operational risk, optimize costs, and meet the needs of digital demand. Automatically manage capping limits to prioritize workloads and optimize mainframe software license costs which can consume 30-50% of the IT budget. Dynamically automate defined capacity MSU settings to optimize your monthly software costs by 10% or more. Mitigate business risk by analyzing, simulating, and automatically managing changes to defined capacity settings based on workload profile. Align capacity to business demand by ensuring MSUs are allocated to highest priority workloads. Patented technology drives capping adjustments, ensuring the most business-critical services are unaffected.
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38
ProsperOps
ProsperOps
ProsperOps is a fully automated, multi-cloud cost optimization platform for AWS, Azure, and Google Cloud. Eliminating cloud waste and managing spend is one of the most persistent challenges for FinOps teams. Cloud usage is inherently elastic, but the financial instruments used to manage cost like Reserved Instances, Savings Plans, and Committed Use Discounts are rigid. ProsperOps bridges this gap by integrating automated rate optimization with intelligent workload optimization, allowing organizations to continuously align cost with usage without manual oversight. Founded in 2018, ProsperOps helps FinOps and engineering teams reduce costs, mitigate financial risk, and eliminate the operational burden of cloud cost management. The platform is governed by customer-defined controls and operates autonomously in the background to execute thousands of real-time optimizations that respond to evolving usage patterns, architectural changes, and business demands. -
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As digitization increases, so does the complexity of managing mainframe capacity and costs. BMC AMI Capacity and Cost portfolio increases availability, predicts capacity bottlenecks before they occur, and optimizes mainframe software costs that can consume 30-50 percent of the mainframe budget. Balance risk and efficiency to achieve operational resilience with visibility into workload changes that may impact your mainframe availability and business demand. Demystify the management of mainframe software license costs and pricing models, giving quantifiable business insights into technical cost data and their drivers. Diagnose capacity issues before they impact your business with intelligent workflows based on nearly half a century of BMC experience – empowering the next generation of mainframe. Intelligently manage capacity settings of less critical workloads to optimize costs while protecting service levels.
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40
Kloudfuse
Kloudfuse
Kloudfuse is an AI‑powered unified observability platform that scales cost‑effectively, combining metrics, logs, traces, events, and digital experience monitoring into a single observability data lake. It integrates with over 700 sources, agent‑based or open source, without re‑instrumentation, and supports open query languages like PromQL, LogQL, TraceQL, GraphQL, and SQL while enabling custom workflows through webhooks and notifications. Organizations can deploy Kloudfuse within their VPC using a simple single‑command install and manage it centrally via a control plane. It automatically ingests and indexes telemetry data with intelligent facets, enabling fast search, context‑aware ML‑based alerts, and SLOs with reduced false positives. Users gain full‑stack visibility, from frontend RUM and session replays to backend profiling, traces, and metrics, allowing navigation from user experience down to code‑level issues. -
41
E-MapReduce
Alibaba
EMR is an all-in-one enterprise-ready big data platform that provides cluster, job, and data management services based on open-source ecosystems, such as Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is a big data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS instances and is based on open-source Apache Hadoop and Apache Spark. EMR allows you to use the Hadoop and Spark ecosystem components, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, to analyze and process data. You can use EMR to process data stored on different Alibaba Cloud data storage service, such as Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). You can quickly create clusters without the need to configure hardware and software. All maintenance operations are completed on its Web interface. -
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IBM Analytics for Apache Spark is a flexible and integrated Spark service that empowers data science professionals to ask bigger, tougher questions, and deliver business value faster. It’s an easy-to-use, always-on managed service with no long-term commitment or risk, so you can begin exploring right away. Access the power of Apache Spark with no lock-in, backed by IBM’s open-source commitment and decades of enterprise experience. A managed Spark service with Notebooks as a connector means coding and analytics are easier and faster, so you can spend more of your time on delivery and innovation. A managed Apache Spark services gives you easy access to the power of built-in machine learning libraries without the headaches, time and risk associated with managing a Sparkcluster independently.
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43
Costimizer
Costimizer
Costimizer is an Agentic FinOps platform designed to simplify and optimize multi-cloud cost management. With real-time visibility, automation, and AI-driven insights, the platform empowers enterprises and system integrators to reduce waste, improve governance, and scale operations securely and efficiently. Our mission is to build a transparent, autonomous FinOps ecosystem where CXOs, finance teams, and DevOps can make proactive, data-backed decisions. Our vision is to help businesses save smarter, operate faster, and focus on innovation — not cost firefighting. -
44
Apache Mahout
Apache Software Foundation
Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark. -
45
WebSparks
WebSparks.AI
WebSparks is an AI-powered platform that enables users to transform ideas into production-ready applications swiftly and efficiently. By interpreting text descriptions, images, and sketches, it generates complete full-stack applications featuring responsive frontends, robust backends, and optimized databases. With real-time previews and one-click deployment, WebSparks streamlines the development process, making it accessible to developers, designers, and non-coders alike. WebSparks is a full-stack AI software engineer.Starting Price: $15/month -
46
Azure HDInsight
Microsoft
Run popular open-source frameworks—including Apache Hadoop, Spark, Hive, Kafka, and more—using Azure HDInsight, a customizable, enterprise-grade service for open-source analytics. Effortlessly process massive amounts of data and get all the benefits of the broad open-source project ecosystem with the global scale of Azure. Easily migrate your big data workloads and processing to the cloud. Open-source projects and clusters are easy to spin up quickly without the need to install hardware or manage infrastructure. Big data clusters reduce costs through autoscaling and pricing tiers that allow you to pay for only what you use. Enterprise-grade security and industry-leading compliance with more than 30 certifications helps protect your data. Optimized components for open-source technologies such as Hadoop and Spark keep you up to date. -
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Increment
Increment
Using our insights and recommendation suite, controlling and optimizing costs becomes a walk in the park. Our models calculate cost to the lowest granularity. Understand what a single query costs, or a table. Aggregate data workloads to understand their collective cost over time. Understand which actions will yield which results. Keep your team focused and tackle only the tech debt worth tackling. Understand how to configure your data workloads in a cost-optimal way. Drive efficient savings without having to re-write queries or drop tables. Educate your team members through query recommendations. Balance effort and impact and ensure your work has an optimal ROI. Teams save up to 30% of their costs with increments. -
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IBM® Storage Insights provides an unparalleled level of visibility across your storage environment to help you manage complex storage infrastructures and make cost-saving decisions. It combines proven IBM data management leadership with proprietary analytics from IBM Research. As a cloud-based service, it enables you to deploy quickly and save storage administration time while optimizing your storage. It also helps automate aspects of the support process to enable faster resolution of issues. Two editions enable you to select the capabilities that serve your needs best. Take the guesswork out of capacity planning with increased visibility into data growth rates and available capacity. Delay future purchases by identifying and reclaiming provisioned but unused storage. Easily track down the source of performance issues and preempt future service disruptions.
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Espresso AI
Espresso AI
Espresso AI is a data-warehouse optimization system built to reduce the compute and query costs of platforms like Snowflake and Databricks SQL by deploying machine-learning agents that manage scaling, scheduling, and query rewriting in real time. It layers three core agents; an autoscaling agent that predicts workload spikes and minimizes idle compute, a scheduling agent that routes queries dynamically across clusters to maximize utilization and significantly reduce idle time, and a query agent that rewrites SQL using large language models combined with formal verification to ensure equivalent results while improving efficiency. It offers fast deployment (minutes rather than months) and a pricing model tied to savings, so that if it does not reduce your bill, you don’t pay. By automating hundreds of thousands of optimization decisions per day, Espresso AI provides dramatic cost reductions while enabling engineering teams to focus on value-add features. -
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Rocket TMON One
Rocket Software
Rocket® TMON® One is a comprehensive monitoring solution designed to optimize mainframe performance, availability, and capacity planning across hybrid cloud environments. It enables enterprises to effectively monitor IBM zSystems and connected distributed systems from a single platform. With real-time visibility into applications, middleware, databases, and network components, teams can quickly identify and resolve performance issues. TMON® One leverages AI-driven analytics to proactively detect anomalies before they impact operations. The platform offers fast implementation and a low total cost of ownership compared to traditional monitoring tools. It integrates seamlessly with observability platforms through data streaming capabilities. Rocket® TMON® One helps organizations ensure application reliability while reducing operational costs.
