Alternatives to Zipher
Compare Zipher alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Zipher in 2025. Compare features, ratings, user reviews, pricing, and more from Zipher 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
FinOpsly
FinOpsly
FinOpsly is the Value-Control™ platform for Cloud, Data, and AI economics. It helps enterprises move beyond cost visibility to actively control spend and business outcomes through explainable, policy-governed AI automation. Unlike reporting-only FinOps tools, FinOpsly unifies cloud (AWS, Azure, GCP), data (Snowflake, Databricks, BigQuery), and AI costs into a single system of action — enabling teams to plan spend before it happens, automate optimization safely, and prove value in weeks, not quarters. FinOpsly enables enterprises to: Map spend to business value across products, teams, customers, and workloads Explain cost drivers clearly with AI-generated context and root-cause analysis Automate optimization safely using policy-driven, explainable agents Prevent drift and overages before they impact budgets or performance -
4
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. -
5
Pepperdata
Pepperdata, Inc.
Pepperdata autonomous cost optimization for data-intensive workloads such as Apache Spark is the only solution that delivers 30-47% greater cost savings continuously and in real time with no application changes or manual tuning. Deployed on over 20,000+ clusters, Pepperdata Capacity Optimizer provides resource optimization and full-stack observability in some of the largest and most complex environments in the world, enabling customers to run Spark on 30% less infrastructure on average. In the last decade, Pepperdata has helped top enterprises such as Citibank, Autodesk, Royal Bank of Canada, members of the Fortune 10, and mid-sized companies save over $250 million. -
6
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). -
7
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 -
8
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. -
9
mogenius
mogenius
mogenius combines visibility, observability, and automation in a single platform for comprehensive Kubernetes control. Connect and visualize your Kubernetes clusters and workloads. Provide visibility for the entire team. Identify misconfigurations across your workloads. Take action directly within the mogenius platform. Automate your K8s operations with service catalogs, developer self-service, and ephemeral environments. Leverage developer self-service to simplify deployments for your developers. Optimize resource allocation and avoid configuration drift through standardized and automated workflows. Eliminate duplicate work and encourage reusability with service catalogs. Get full visibility into your current Kubernetes setup. Deploy a cloud-agnostic Kubernetes operator to receive a complete overview of what’s going on across your clusters and workloads. Provide developers with local and ephemeral testing environments in a few clicks that mirror your production setup.Starting Price: $350 per month -
10
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 -
11
Sync
Sync Computing
Sync Computing offers Gradient, an AI-powered compute optimization engine designed to enhance data infrastructure efficiency. By leveraging advanced machine learning algorithms developed at MIT, Gradient provides automated optimization for organizations running data workloads on cloud-based CPUs or GPUs. Users can achieve up to 50% cost savings on their Databricks compute expenses while consistently meeting runtime service level agreements (SLAs). Gradient's continuous monitoring and fine-tuning capabilities ensure optimal performance across complex data pipelines, adapting seamlessly to varying data sizes and workload patterns. The platform integrates with existing data tools and supports multiple cloud providers, offering a comprehensive solution for managing and optimizing data infrastructure. -
12
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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13
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. -
14
Cloudify
Cloudify Platform
Manage all private and public environments from one platform using a single CI/CD plugin that connects to ALL automation toolchains. Including Jenkins, Kubernetes, Terraform, Cloud Formation, Azure ARM and more. No installation, no downloads … and on us for the first 30 days. Built-in integration with infrastructure orchestration domains including AWS Cloud formation, Azure ARM, Ansible and Terraform. Service Composition Domain-Specific Language (DSL) – simplifies the relationship between services, handling cascading workflows, shared resources, distributed life-cycle management and more. Orchestration of cloud native Kubernetes services across multiple clusters: OpenShift, GKE, EKS, AKS and KubeSpray. Access a built-in blueprint to automate cluster setup and configuration. Built-in integration with Jenkins and other CI/CD platforms providing a ‘one-stop-shop’ for integrating all orchestration domains to your CI/CD pipeline. -
15
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. -
16
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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Anyscale
Anyscale
Anyscale is a unified AI platform built around Ray, the world’s leading AI compute engine, designed to help teams build, deploy, and scale AI and Python applications efficiently. The platform offers RayTurbo, an optimized version of Ray that delivers up to 4.5x faster data workloads, 6.1x cost savings on large language model inference, and up to 90% lower costs through elastic training and spot instances. Anyscale provides a seamless developer experience with integrated tools like VSCode and Jupyter, automated dependency management, and expert-built app templates. Deployment options are flexible, supporting public clouds, on-premises clusters, and Kubernetes environments. Anyscale Jobs and Services enable reliable production-grade batch processing and scalable web services with features like job queuing, retries, observability, and zero-downtime upgrades. Security and compliance are ensured with private data environments, auditing, access controls, and SOC 2 Type II attestation.Starting Price: $0.00006 per minute -
18
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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MinIO
MinIO
MinIO's high-performance object storage suite is software defined and enables customers to build cloud-native data infrastructure for machine learning, analytics and application data workloads. MinIO object storage is fundamentally different. Designed for performance and the S3 API, it is 100% open-source. MinIO is ideal for large, private cloud environments with stringent security requirements and delivers mission-critical availability across a diverse range of workloads. MinIO is the world's fastest object storage server. With READ/WRITE speeds of 183 GB/s and 171 GB/s on standard hardware, object storage can operate as the primary storage tier for a diverse set of workloads ranging from Spark, Presto, TensorFlow, H2O.ai as well as a replacement for Hadoop HDFS. MinIO leverages the hard won knowledge of the web scalers to bring a simple scaling model to object storage. At MinIO, scaling starts with a single cluster which can be federated with other MinIO clusters. -
20
Pipeshift
Pipeshift
Pipeshift is a modular orchestration platform designed to facilitate the building, deployment, and scaling of open source AI components, including embeddings, vector databases, large language models, vision models, and audio models, across any cloud environment or on-premises infrastructure. The platform offers end-to-end orchestration, ensuring seamless integration and management of AI workloads, and is 100% cloud-agnostic, providing flexibility in deployment. With enterprise-grade security, Pipeshift addresses the needs of DevOps and MLOps teams aiming to establish production pipelines in-house, moving beyond experimental API providers that may lack privacy considerations. Key features include an enterprise MLOps console for managing various AI workloads such as fine-tuning, distillation, and deployment; multi-cloud orchestration with built-in auto-scalers, load balancers, and schedulers for AI models; and Kubernetes cluster management. -
21
Opsani
Opsani
We are the only solution on the market that autonomously tunes applications at scale, either for a single application or across the entire service delivery platform. Opsani rightsizes your application autonomously so your cloud application works harder and leaner so you don’t have to. Opsani COaaS maximizes cloud workload performance and efficiency using the latest in AI and Machine Learning to continuously reconfigure and tune with every code release, load profile change, and infrastructure upgrade. We accomplish this while integrating easily with either a single app or across your service delivery platform while also scaling autonomously across 1000’s of services. Opsani allows for you to solve for all three autonomously without compromise. Reduce costs up to 71% by leveraging Opsani's AI algorithms. Opsani optimization continuously evaluates trillions of configuration permutations and pinpoints the best combinations of resources and parameter settings.Starting Price: $500 per month -
22
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. -
23
MontyCloud DAY2
MontyCloud
MontyCloud offers an autonomous CloudOps platform designed to simplify cloud management and operations. It enables IT teams to provision, manage, and operate AWS infrastructure efficiently without the need for specialized cloud skills. It provides comprehensive visibility into cloud inventories, security, compliance, and costs, empowering organizations to optimize their cloud environments effectively. Provide cloud project workspaces and sandbox environments for users to access cloud services securely. Implement autonomous governance with invisible guardrails that deliver better cloud security, compliance, cost optimization, and adherence to well-architected best practices. Get comprehensive visibility & control across your cloud infrastructure and services. Gain insights into resource usage, performance, and cost to make data-driven decisions. Effortlessly manage and monitor your cloud environment while maintaining full control over access, security, and compliance. -
24
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. -
25
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 -
26
Amazon SageMaker HyperPod
Amazon
Amazon SageMaker HyperPod is a purpose-built, resilient compute infrastructure that simplifies and accelerates the development of large AI and machine-learning models by handling distributed training, fine-tuning, and inference across clusters with hundreds or thousands of accelerators, including GPUs and AWS Trainium chips. It removes the heavy lifting involved in building and managing ML infrastructure by providing persistent clusters that automatically detect and repair hardware failures, automatically resume workloads, and optimize checkpointing to minimize interruption risk, enabling months-long training jobs without disruption. HyperPod offers centralized resource governance; administrators can set priorities, quotas, and task-preemption rules so compute resources are allocated efficiently among tasks and teams, maximizing utilization and reducing idle time. It also supports “recipes” and pre-configured settings to quickly fine-tune or customize foundation models. -
27
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 -
28
Together AI
Together AI
Together AI provides an AI-native cloud platform built to accelerate training, fine-tuning, and inference on high-performance GPU clusters. Engineered for massive scale, the platform supports workloads that process trillions of tokens without performance drops. Together AI delivers industry-leading cost efficiency by optimizing hardware, scheduling, and inference techniques, lowering total cost of ownership for demanding AI workloads. With deep research expertise, the company brings cutting-edge models, hardware, and runtime innovations—like ATLAS runtime-learning accelerators—directly into production environments. Its full-stack ecosystem includes a model library, inference APIs, fine-tuning capabilities, pre-training support, and instant GPU clusters. Designed for AI-native teams, Together AI helps organizations build and deploy advanced applications faster and more affordably.Starting Price: $0.0001 per 1k tokens -
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NVIDIA Base Command Manager
NVIDIA
NVIDIA Base Command Manager offers fast deployment and end-to-end management for heterogeneous AI and high-performance computing clusters at the edge, in the data center, and in multi- and hybrid-cloud environments. It automates the provisioning and administration of clusters ranging in size from a couple of nodes to hundreds of thousands, supports NVIDIA GPU-accelerated and other systems, and enables orchestration with Kubernetes. The platform integrates with Kubernetes for workload orchestration and offers tools for infrastructure monitoring, workload management, and resource allocation. Base Command Manager is optimized for accelerated computing environments, making it suitable for diverse HPC and AI workloads. It is available with NVIDIA DGX systems and as part of the NVIDIA AI Enterprise software suite. High-performance Linux clusters can be quickly built and managed with NVIDIA Base Command Manager, supporting HPC, machine learning, and analytics applications. -
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Exafunction
Exafunction
Exafunction optimizes your deep learning inference workload, delivering up to a 10x improvement in resource utilization and cost. Focus on building your deep learning application, not on managing clusters and fine-tuning performance. In most deep learning applications, CPU, I/O, and network bottlenecks lead to poor utilization of GPU hardware. Exafunction moves any GPU code to highly utilized remote resources, even spot instances. Your core logic remains an inexpensive CPU instance. Exafunction is battle-tested on applications like large-scale autonomous vehicle simulation. These workloads have complex custom models, require numerical reproducibility, and use thousands of GPUs concurrently. Exafunction supports models from major deep learning frameworks and inference runtimes. Models and dependencies like custom operators are versioned so you can always be confident you’re getting the right results. -
31
nOps
nOps.io
FinOps on nOps We only charge for what we save. ✓Continuous Cloud waste reduction ✓Continuous Container cluster optimization ✓Continuous RI management to save up to 40% over on-demand resources ✓Spot Orchestrator to reduce cost over on-demand resources Most organizations don’t have the resources to focus on reducing cloud spend. nOps is your ML-powered FinOps team. nOps reduces cloud waste, helps you run workloads on spot instances, automatically manages reservations, and helps optimize your containers. Everything is automated and data-driven.Starting Price: $99 per month -
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Red Hat CloudForms
Red Hat
Define a new, scalable cloud infrastructure. Exert control and clear structures in your cloud environment by creating separate organizations, defining relationships between users, tenants, and projects, and managing quotas and services. Provision your systems through cloud and virtualization platforms like Red Hat Enterprise Virtualization, Amazon, and Microsoft Azure, set retirement dates, and scale your environment on your terms. Take your real-world environment and simulate what-if scenarios for proactive resource planning and continuous insights into consumption levels to allow detailed chargeback, quotas, and policy creation. Get a handle on performance, capacity, and workloads through SmartState historical and trend analytics for different aspects of your cloud environment. Define the policy state for your environment, and follow up with automatic alerts and responses as your environment changes. -
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Azure Automation
Microsoft
Automate all of those frequent, time-consuming, and error-prone cloud management tasks. Azure Automation service helps you focus on work that adds business value. By reducing errors and boosting efficiency, it also helps to lower your operational costs. Update Windows and Linux systems across hybrid environments. Monitor update compliance across Azure, on-premises, and other cloud platforms for Windows and Linux. Schedule deployments to orchestrate the installation of updates within a defined maintenance window. Author and manage PowerShell configurations, import configuration scripts, and generate node configurations—all in the cloud. Use Azure Configuration Management to monitor and automatically update machine configuration across physical and virtual machines, Windows, or Linux—in the cloud or on-premises. & more -
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Lumen Cloud Application Manager
Lumen Technologies
Innovative Lumen Cloud Application Manager helps you easily orchestrate the delivery of infrastructure, applications, and services across multiple technologies through one platform providing you with greater agility, flexibility, and control of application workloads. Our centralized platform creates a simplified workload management process across a multitude of hosting environments, helping you clearly understand where you stand. The result? Faster reaction times and smarter decisions. Oversee modeling, deployment, and orchestration for the entire application lifecycle. Take advantage of our Interactive visualization tools to help you scale, migrate and update running applications more effectively. Simplify management of your hybrid IT environment for faster application delivery, lower costs, and full visibility of your progress to help inform your next moves–all with support from a dedicated technical account manager. -
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Azure CycleCloud
Microsoft
Create, manage, operate, and optimize HPC and big compute clusters of any scale. Deploy full clusters and other resources, including scheduler, compute VMs, storage, networking, and cache. Customize and optimize clusters through advanced policy and governance features, including cost controls, Active Directory integration, monitoring, and reporting. Use your current job scheduler and applications without modification. Give admins full control over which users can run jobs, as well as where and at what cost. Take advantage of built-in autoscaling and battle-tested reference architectures for a wide range of HPC workloads and industries. CycleCloud supports any job scheduler or software stack—from proprietary in-house to open-source, third-party, and commercial applications. Your resource demands evolve over time, and your cluster should, too. With scheduler-aware autoscaling, you can fit your resources to your workload.Starting Price: $0.01 per hour -
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DataNimbus
DataNimbus
DataNimbus is an AI-powered platform that streamlines payments and accelerates AI adoption through innovative, cost-efficient solutions. By seamlessly integrating with Databricks components like Spark, Unity Catalog, and ML Ops, DataNimbus enhances scalability, governance, and runtime operations. Its offerings include a visual designer, a marketplace for reusable connectors and machine learning blocks, and agile APIs, all designed to simplify workflows and drive data-driven innovation. -
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Horovod
Horovod
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.Starting Price: Free -
38
Stacktape
Stacktape
Stacktape is a DevOps-free cloud framework that’s both powerful and easy at the same time. It allows you to develop, deploy and run applications on AWS. With 98% less configuration and without the need for DevOps or Cloud expertise. Unlike with other solutions, you can deploy both serverless (AWS lambda-based) and more traditional (container-based) applications. Stacktape also supports 20+ infrastructure components, including SQL databases, Load balancers, MongoDB Atlas clusters, Batch-jobs, Kafka topics, Redis clusters & more. Besides infrastructure management, Stacktape handles source code packaging, deployments, local/remote development, and much more. It also comes with a VScode extension and local development studio (GUI). Stacktape is a IaaC tool. A typical production-grade REST API is ~30 lines of config (compared to ~600-800 lines of CloudFormation/Terraform). The deployment can be done using a single command - from local machine or a CI/CD pipeline.Starting Price: $450/month -
39
Azure Arc
Microsoft
Azure Arc is Microsoft’s hybrid and multicloud solution that extends Azure services across on-premises, edge, and other cloud environments. It enables organizations to manage servers, Kubernetes clusters, and applications anywhere with consistent tools and APIs. With Arc, businesses can modernize SQL Server and Windows Server, deploy containerized apps, and access Azure services like security, observability, and governance across diverse infrastructures. Its agentless multicloud connector streamlines management while maintaining embedded compliance with over 100 certifications. Azure Arc also integrates with existing tools such as GitHub and Visual Studio Code, allowing developers to innovate without disrupting workflows. By bridging traditional infrastructure and cloud-native services, it gives enterprises the flexibility to innovate anywhere while staying secure and cost-efficient. -
40
Oracle Cloud Infrastructure (OCI) Resource Manager is an Oracle-managed service that automates deployment and operations for all Oracle Cloud Infrastructure resources. Unlike Infrastructure-as-Code (IaC) offerings from other cloud vendors, the service is based on Terraform, a widely used, open source industry standard that allows DevOps engineers to develop and deploy their infrastructure anywhere. IaC allows repeatable deployments of configurations, increasing developer productivity. For auditing, Resource Manager tracks changes to infrastructure by users and timestamps. Explore an architecture and Terraform configuration for using Oracle Autonomous Data Warehouse and Oracle Analytics Cloud to optimize data management.
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41
IBM Analytics Engine provides an architecture for Hadoop clusters that decouples the compute and storage tiers. Instead of a permanent cluster formed of dual-purpose nodes, the Analytics Engine allows users to store data in an object storage layer such as IBM Cloud Object Storage and spins up clusters of computing notes when needed. Separating compute from storage helps to transform the flexibility, scalability and maintainability of big data analytics platforms. Build on an ODPi compliant stack with pioneering data science tools with the broader Apache Hadoop and Apache Spark ecosystem. Define clusters based on your application's requirements. Choose the appropriate software pack, version, and size of the cluster. Use as long as required and delete as soon as an application finishes jobs. Configure clusters with third-party analytics libraries and packages. Deploy workloads from IBM Cloud services like machine learning.Starting Price: $0.014 per hour
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42
Spot Ocean
Spot by NetApp
Spot Ocean lets you reap the benefits of Kubernetes without worrying about infrastructure while gaining deep cluster visibility and dramatically reducing costs. The key question is how to use containers without the operational overhead of managing the underlying VMs while also take advantage of the cost benefits associated with Spot Instances and multi-cloud. Spot Ocean is built to solve this problem by managing containers in a “Serverless” environment. Ocean provides an abstraction on top of virtual machines allowing to deploy Kubernetes clusters without the need to manage the underlying VMs. Ocean takes advantage of multiple compute purchasing options like Reserved and Spot instance pricing and failover to On-Demand instances whenever necessary, providing 80% reduction in infrastructure costs. Spot Ocean is a Serverless Compute Engine that abstracts the provisioning (launching), auto-scaling, and management of worker nodes in Kubernetes clusters. -
43
Azure Kubernetes Fleet Manager
Microsoft
Easily handle multicluster scenarios for Azure Kubernetes Service (AKS) clusters such as workload propagation, north-south load balancing (for traffic flowing into member clusters), and upgrade orchestration across multiple clusters. Fleet cluster enables centralized management of all your clusters at scale. The managed hub cluster takes care of the upgrades and Kubernetes cluster configuration for you. Kubernetes configuration propagation lets you use policies and overrides to disseminate objects across fleet member clusters. North-south load balancer orchestrates traffic flow across workloads deployed in multiple member clusters of the fleet. Group any combination of your Azure Kubernetes Service (AKS) clusters to simplify multi-cluster workflows like Kubernetes configuration propagation and multi-cluster networking. Fleet requires a hub Kubernetes cluster to store configurations for placement policy and multicluster networking.Starting Price: $0.10 per cluster per hour -
44
Cluster.dev
Cluster.dev
Cluster.dev, the only manager for cloud-native infrastructures. Combine the power of all your infrastructure tools. Create platform-as-a-service for your teams. Customize your projects and infrastructures. Observe changes and the state of your infrastructure in a single place, your Git repo. Use a common solution for all types of changes. Forget about manual runbooks and CI/CD magic. You deserve to have an overview of all your infrastructure in a single place! Confidence in infrastructure changes. Be sure that your tools are doing what you expect them to do. Everything in Cluster.dev has a state. Even scripts and k8s manifest. Security, and independence from third-party vendors. Pass secrets to third-party tools even if they don't support your secrets store. Customization for every piece of infrastructure. With its amazing template engine, Cluster.dev allows you to customize configs, scripts, manifests, Terraform code, and whole infrastructures. -
45
OtterTune
OtterTune
Choosing from 100s of database tuning knobs is no problem for our AI. Let it do the work for you so you can have your life back. Monitor your database performance to gain insights and automatically adjust your configuration as workloads change. Built for today’s cloud databases, OtterTune doesn’t touch any user data, but fine-tunes your DBaaS operations behind the scenes. OtterTune is not just a fancy frontend to a few tools - but a revolutionary machine learning and machine training platform that makes the database backend run in the best possible scenario, now and in the future. OtterTune uses machine learning to actively tune your database. Simply select a target objective to optimize (e.g., throughput, latency, cost) and OtterTune automatically updates 100+ configuration settings to improve that target. Don't get surprised by outages or performance drops. OtterTune monitors your database and collects metrics about your workload.Starting Price: $550/per month -
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CloudWize
CloudWize
With CloudWize, cloud teams can regain visibility and control over their ever-changing cloud environment, creating an optimized, problem-free cloud architecture. Teams can troubleshoot faster, prevent incidents from reoccurring, detect divergence from best practices, optimize cloud related costs and ensure that all security and compliance policies are met. Get alerts on changes with significant cost implications before it’s too late, and enjoy an enhanced ability to avoid budget overruns. Provide your FinOps team with the ability to query and search for misconfigurations that impact costs. Avoid recurring cloud configuration errors. Continuously implement CloudOps & FinOps accumulated knowledge. Analyze your architecture with our advanced multi-service querying capabilities. Use our unique, easy to use graphic language to look for potential cost savings, improve configurations or detect policy breaches to avoid downtime or exposure. -
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BidElastic
BidElastic
It isn’t always straightforward to benefit from the rich features of cloud services. To make it easier for businesses to use the cloud, we developed BidElastic as a resource provisioning tool with two components: BidElastic BidServer cuts computational costs; BidElastic Intelligent Auto Scaler (IAS) streamlines management and monitoring of your cloud provider. The BidServer uses simulation and advanced optimization routines to anticipate market movements and to design a robust infrastructure for cloud providers’ spot instances. To match demand in volatile workloads, you need to scale your cloud infrastructure dynamically. But that’s easier said than done. There’s a traffic spike and only 10 minutes later are new servers online. In the meantime you’ve lost customers who may never come back. To scale your resources properly you need to be able to predict computational workloads. CloudPredict does exactly that; it uses machine learning to predict computational workloads. -
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Quali Torque
Quali
Seamlessly connect cloud automation, cost control, and security into your platform, and make the power of environments as a service accessible to any user through self-service, even if they are not cloud experts. Deliver the infrastructure & application resources your teams need with velocity and control. Give developers on-demand access to the application environments they need through their CI/CD tools, GUI, and CLI. Empower developers to build in a unified, standardized way without introducing unnecessary friction. Reusable building blocks make it easier to deliver application-aware resources developers really need. Eliminate uncertainty, avoid over-spending, and tie cloud costs back to your business. Torque is a SaaS platform delivering Infrastructure automation at scale for complex, application-centric environments on cloud technologies including AWS, Azure, and Kubernetes. IT leaders and DevOps innovators around the world trust Quali to enable self-service automation. -
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IBM Spectrum LSF Suites is a workload management platform and job scheduler for distributed high-performance computing (HPC). Terraform-based automation to provision and configure resources for an IBM Spectrum LSF-based cluster on IBM Cloud is available. Increase user productivity and hardware use while reducing system management costs with our integrated solution for mission-critical HPC environments. The heterogeneous, highly scalable, and available architecture provides support for traditional high-performance computing and high-throughput workloads. It also works for big data, cognitive, GPU machine learning, and containerized workloads. With dynamic HPC cloud support, IBM Spectrum LSF Suites enables organizations to intelligently use cloud resources based on workload demand, with support for all major cloud providers. Take advantage of advanced workload management, with policy-driven scheduling, including GPU scheduling and dynamic hybrid cloud, to add capacity on demand.
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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.
