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CRN’s 2025 Cloud 100 names 100 cloud-related companies across five groups of 20: infrastructure, software, security, monitoring and management, and storage. It is a curated editorial list—not a ranking from 1 to 100, a certified list of the “best” companies, or proof that every product is reliable, affordable, or suitable for your organization.
Because this is a 2025 list viewed in 2026, treat company names, product availability, executives, acquisitions, pricing, valuations, and market claims as historical unless independently rechecked. The most useful way to read the list is as a map of the cloud market and its major themes: artificial intelligence, hybrid cloud, cybersecurity, data platforms, resilience, and operational complexity.
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How CRN’s 2025 Cloud 100 is organized
CRN divided the companies equally into five categories. The categories overlap: a cloud security company may protect data stored by a cloud storage provider, while an infrastructure company may supply the hardware used by an AI platform. The groupings are therefore useful for orientation, but they do not make unlike companies directly comparable.
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- Cloud infrastructure: public cloud, servers, networking, data centers, hybrid cloud, edge computing, and GPU infrastructure.
- Cloud software: databases, data platforms, enterprise applications, analytics, workflow, CRM, ERP, and AI-enabled software.
- Cloud security: workload protection, application security, zero trust, data security, posture management, and detection and response.
- Cloud monitoring and management: observability, incident response, AIOps, FinOps, Kubernetes management, governance, and network visibility.
- Cloud storage: backup, disaster recovery, object storage, file services, data mobility, and cyber resilience.
CRN’s feature emphasizes innovation, product activity, market momentum, partner opportunity, and relevance to current enterprise problems. It does not publish a reproducible scoring formula in the list feature. “Coolest” is consequently editorial language, not an independently audited performance measurement.
#1 Best Overall
Read CRN’s main 2025 Cloud 100 feature.
The 20 coolest cloud infrastructure companies
This is broader than a list of public-cloud providers. It includes hyperscalers, semiconductor and server companies, networking vendors, colocation operators, hybrid-cloud platforms, edge providers, managed services, and specialist GPU infrastructure.
| Company | What it represents | Likely buyer and qualification |
|---|---|---|
| Amazon Web Services | Broad public-cloud compute, storage, databases, AI, and infrastructure services. | Developers, IT teams, and enterprises; breadth brings billing and architecture complexity. |
| Broadcom | Semiconductors, networking, virtualization, and infrastructure technology. | Large infrastructure and cloud operators; not a conventional self-service cloud. |
| CoreWeave | Specialized GPU cloud infrastructure for AI and high-performance workloads. | AI developers and enterprises; capacity, geography, contracts, and economics require validation. |
| Cisco Systems | Networking, security, connectivity, and data-center infrastructure. | Enterprise infrastructure teams; integration and licensing can be substantial. |
| Dell Technologies | Servers, storage, data-center hardware, and AI infrastructure. | Organizations operating private or hybrid infrastructure; hardware requires implementation and maintenance. |
| Digital Realty | Data centers, colocation, interconnection, and cloud connectivity. | Enterprises and service providers needing facilities or network access. |
| Flexential | Colocation, data-center, connectivity, and hybrid infrastructure services. | Organizations needing managed physical infrastructure; availability depends on location. |
| Google Cloud | Public cloud, data analytics, AI, and cloud-native services. | Data and AI teams; ecosystem and enterprise fit vary by workload. |
| Hewlett Packard Enterprise | Servers, networking, private cloud, edge, and hybrid infrastructure. | Enterprise and on-premises teams; less relevant to a purely self-service cloud buyer. |
| IBM | Hybrid cloud, regulated-industry infrastructure, and enterprise services. | Large and compliance-heavy organizations; typically sales- and architecture-led. |
| Lenovo | Servers, storage, and data-center infrastructure. | Private and hybrid-cloud operators; hardware is only one part of total cost. |
| Lumen Technologies | Connectivity, edge, networking, and cloud infrastructure services. | Distributed enterprises and service providers; geography and network requirements matter. |
| Microsoft | Azure public cloud, AI, hybrid cloud, and enterprise services. | Microsoft-centric organizations; consumption pricing can be difficult to forecast. |
| Nutanix | Hybrid multicloud and hyperconverged infrastructure. | IT teams seeking private or hybrid-cloud control; may be unnecessary for simple public-cloud workloads. |
| Oracle | Oracle Cloud Infrastructure and database-centered cloud services. | Organizations with Oracle estates or specialized requirements; less compelling without those dependencies. |
| Rackspace Technology | Managed cloud, migration, and multicloud services. | Organizations needing external expertise; managed services add cost but can reduce staffing burden. |
| Red Hat | Enterprise Linux, Kubernetes, and hybrid-cloud platforms. | Enterprise and developer teams; support and platform governance are important. |
| Scale Computing | Edge and simplified hyperconverged infrastructure. | Distributed and smaller organizations; confirm workload and ecosystem fit. |
| TierPoint | Managed cloud, colocation, disaster recovery, and hybrid infrastructure. | Midmarket and enterprise buyers; service availability is location-dependent. |
| Zadara | On-demand enterprise storage and infrastructure deployed near workloads. | Distributed and hybrid-cloud teams; evaluate performance, portability, and consumption costs. |
CRN’s infrastructure category source.
The 20 coolest cloud software companies
CRN’s software group spans enterprise SaaS and data infrastructure. Its 2025 themes included data platforms for AI, vector databases and retrieval, embedded copilots and agents, cloud databases, and workflow automation.
| Company | Primary area | Likely buyer and qualification |
|---|---|---|
| Agiloft | Contract lifecycle management. | Legal and procurement teams; value depends on process adoption and integrations. |
| Cloudera | Enterprise data, analytics, and hybrid data platforms. | Data teams with complex or hybrid estates; platform administration can be demanding. |
| Confluent | Event streaming and data in motion. | Data engineering teams; expertise and data volume affect cost. |
| Couchbase | Distributed cloud database and application data services. | Developers needing flexible, distributed applications; not a universal relational replacement. |
| Cribl | Telemetry and observability-data management. | Operations and data teams seeking routing and control; value depends on telemetry architecture. |
| Databricks | Lakehouse data engineering, analytics, and AI. | Data and AI organizations; consumption and platform complexity require governance. |
| dbt Labs | Analytics engineering and transformation workflows. | Modern data teams; works best with disciplined development practices. |
| EDB | Enterprise PostgreSQL and database services. | Organizations standardizing on PostgreSQL; migration and support needs vary. |
| Genesys | Cloud customer-experience and contact-center software. | Customer-service organizations; implementation and change management matter. |
| Intermedia Cloud Communications | Cloud communications and collaboration. | Small and midmarket businesses; assess geography, integrations, and support. |
| MongoDB | Document database and managed Atlas platform. | Application developers; a poor fit for some highly normalized relational workloads. |
| Pinecone | Managed vector database for AI retrieval. | AI developers; vector search is not a general transactional database. |
| Qlik | Data integration, analytics, and business intelligence. | Business and data teams; compare governance and integration requirements. |
| Salesforce | Cloud CRM, automation, data, and AI-enabled enterprise applications. | Sales and service organizations; customization and administration can be expensive. |
| SAP | Cloud ERP, business applications, data, and enterprise AI. | Large enterprises; migration, licensing, and implementation are substantial decisions. |
| ServiceNow | Cloud workflow, IT service management, and automation. | Enterprise operations teams; broad platforms require careful scope control. |
| Snowflake | Cloud data platform, warehousing, analytics, and AI. | Data teams; compute, storage, and usage patterns drive cost. |
| SugarCRM | Cloud CRM. | Sales and marketing teams; compare ecosystem depth and required customization. |
| ThoughtSpot | Search-driven analytics and AI-assisted business intelligence. | Business users and analytics teams; validate data quality and governance. |
| Workday | Cloud human-capital and financial-management software. | Large organizations; implementation and subscription commitments are significant. |
CRN’s software category source. One CRN software article used “$723 million” for a Gartner cloud-spending forecast; the surrounding CRN coverage describes approximately $723 billion, so the unit should not be repeated as millions.
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The 20 coolest cloud security companies
The security group reflects the convergence of cloud posture management, application security, workload protection, identity and zero trust, data security, endpoint protection, and detection and response. CRN cited a Check Point survey in which 61% of surveyed organizations reported cloud-security incidents in 2024. That is a survey result, not a universal incident rate.
| Company | Primary area | Typical use and qualification |
|---|---|---|
| Aqua Security | Cloud-native application and container security. | DevSecOps and cloud teams; requires integration into development and runtime processes. |
| Check Point Software Technologies | Network, cloud, and enterprise cybersecurity. | Security teams; broad portfolios can require substantial administration. |
| Cloudflare | Edge networking, CDN, application security, and zero-trust services. | Organizations needing edge and security controls; not a full replacement for every cloud service. |
| CrowdStrike | Endpoint, cloud, and security operations. | Enterprise security teams; broad platform scope and sales-led pricing may not suit small buyers. |
| Cyera | Data-security posture management and data discovery. | Security and privacy teams; outcomes depend on classification and remediation processes. |
| Fortinet | Network, firewall, and cloud security. | Network and security teams; architecture and appliance or subscription choices matter. |
| Illumio | Segmentation and ransomware containment. | Security teams; policy design and asset visibility are prerequisites. |
| Netskope | SASE, secure access, and cloud application security. | Distributed-workforce and enterprise security teams; deployment can be complex. |
| OpenText Cybersecurity | Enterprise security and risk products. | Large organizations; product portfolio and integration requirements need assessment. |
| Orca Security | Agentless cloud security posture and workload visibility. | Cloud security teams; agentless approaches may provide less runtime depth in some cases. |
| Palo Alto Networks | Network, cloud, application, and security operations protection. | Enterprise security teams; broad consolidation can increase scope and cost. |
| Qualys | Vulnerability, compliance, and cloud security management. | Security and compliance teams; coverage depends on asset inventory quality. |
| SentinelOne | Endpoint, cloud, and autonomous security operations. | Security operations teams; automation still requires human response processes. |
| Skyhigh Security | Secure access, data protection, and cloud security. | Enterprise security teams; assess policy integration and product fit. |
| Snyk | Developer security for code, open source, containers, and cloud applications. | Development and application-security teams; remediation capacity is essential. |
| Sophos | Endpoint, network, and managed cybersecurity. | Small and midmarket organizations; confirm required integrations and service model. |
| Tenable | Exposure, vulnerability, and cloud-risk management. | Security and risk teams; prioritization depends on accurate asset context. |
| Trend Micro | Cloud, workload, endpoint, and network security. | Enterprise and hybrid-cloud teams; evaluate coverage against existing controls. |
| Wiz | Cloud exposure and security posture management. | Cloud security teams; generally an enterprise purchase rather than a simple low-budget signup. |
| Zscaler | Zero-trust access, secure web gateway, and cloud security. | Distributed enterprises; migration and identity integration are central to success. |
CRN’s security category source.
The 20 coolest cloud monitoring and management companies
This category is wider than application-performance monitoring. It includes observability, AIOps, incident response, network mapping, cloud governance, FinOps, Kubernetes, service-level objectives, telemetry, and edge management. CRN cited a Gartner forecast that 90% of organizations would adopt a hybrid-cloud approach by 2027; that is a forecast, not a settled measurement.
| Company | Primary area | Typical use and qualification |
|---|---|---|
| AlertOps | Incident response and alert orchestration. | Operations teams; requires well-designed escalation and on-call processes. |
| AppDynamics | Application performance monitoring and observability. | Application and reliability teams; telemetry volume can affect cost. |
| Atlassian | Developer, service-management, and collaboration workflows. | Software and IT teams; platform sprawl is a governance risk. |
| Devo Technology | Cloud-scale security analytics and observability. | Security and operations teams; data ingestion and retention drive economics. |
| Gigamon | Network visibility and traffic intelligence. | Network and security teams; requires appropriate network architecture. |
| Gurucul | Security analytics and risk-focused machine learning. | Security operations; model quality depends on data and tuning. |
| Hydrolix | High-volume observability and telemetry data. | Large-scale data and operations teams; validate retention and query economics. |
| InsightFinder | AIOps, anomaly detection, and predictive operations. | Reliability teams; automation does not remove the need for usable telemetry. |
| Kion | Cloud governance, financial management, and compliance. | FinOps and cloud-governance teams; savings depend on organizational follow-through. |
| LogicMonitor | Hybrid infrastructure monitoring and observability. | IT teams and MSPs; generally more sales-led than self-service. |
| Nerdio | Microsoft cloud management for businesses and MSPs. | Microsoft-centric organizations; poor fit for non-Microsoft estates. |
| NetBrain | Network automation and topology mapping. | Network operations; accurate discovery and integration are required. |
| Nobl9 | Service-level objectives and reliability management. | SRE and platform teams; useful only when teams define and act on service objectives. |
| Rafay | Kubernetes management and platform operations. | Platform and DevOps teams; Kubernetes expertise remains necessary. |
| RapidScale | Managed cloud and IT services. | Midmarket organizations seeking external operations; managed fees trade for reduced staffing burden. |
| ScienceLogic | IT operations, observability, and automation. | Enterprise and service-provider teams; platform scope requires implementation discipline. |
| Spectro Cloud | Kubernetes management across cloud, edge, and data centers. | Distributed platform teams; operational complexity varies by deployment. |
| Tigera | Kubernetes networking and security. | Container and platform teams; best suited to Kubernetes-centered environments. |
| Unravel Data | Data-platform performance and cost management. | Data engineering teams; benefits depend on workload visibility. |
| Zebra Technologies | Edge, asset, and operational intelligence. | Field, logistics, and distributed operations; hardware and deployment context matter. |
CRN’s monitoring and management category source.
The 20 coolest cloud storage companies
Cloud storage is no longer only about capacity. The category includes immutable backup, ransomware recovery, SaaS protection, object storage, global file systems, storage virtualization, data mobility, and high-performance infrastructure for AI and analytics.
Rank #3
| Company | Primary area | Typical use and qualification |
|---|---|---|
| Arcserve | Backup, disaster recovery, and data protection. | Small and midmarket IT teams; recovery testing is as important as backup creation. |
| Cohesity | Data security, backup, and hybrid-cloud data management. | Enterprise infrastructure teams; consolidation still requires migration planning. |
| Commvault | Backup, recovery, and cyber resilience. | Enterprise and regulated organizations; licensing and implementation can be complex. |
| DataCore | Storage virtualization and software-defined storage. | Hybrid and private-cloud teams; assess existing hardware and operational skills. |
| DDN | High-performance storage for AI and HPC. | AI and research infrastructure teams; performance comes with specialized cost and capacity needs. |
| Hammerspace | Global data orchestration and mobility. | Distributed data and AI teams; portability depends on architecture and network performance. |
| HYCU | Backup for SaaS, cloud, and virtual environments. | SMB, midmarket, and cloud-first teams; confirm application coverage and retention needs. |
| Infinidat | Enterprise storage and data infrastructure. | Large organizations; typically an enterprise infrastructure purchase. |
| Komprise | Unstructured-data management and data movement. | Storage and data teams; value depends on data discovery and policy quality. |
| MinIO | S3-compatible object storage. | Developers and private-cloud operators; support, operations, and hardware remain responsibilities. |
| MSP360 | Backup and managed-service tooling. | MSPs and smaller organizations; evaluate management, support, and underlying storage costs. |
| Nasuni | Cloud file services and global file data. | Distributed businesses; connectivity and access patterns matter. |
| NetApp | Hybrid-cloud storage and data management. | Enterprise storage teams; broad capabilities can increase design complexity. |
| Panzura | Global file systems and cloud data services. | Distributed organizations; validate workload latency and collaboration requirements. |
| Pure Storage | Enterprise flash, cloud, and AI storage. | Performance-sensitive organizations; compare acquisition and subscription economics. |
| Quantum | Backup, archive, unstructured data, and high-performance storage. | Media, research, and enterprise teams; match platform to retention and access patterns. |
| Rubrik | Backup, data security, and cyber recovery. | Enterprise security and infrastructure teams; recovery planning and integration are essential. |
| VAST Data | AI and high-performance data infrastructure. | AI and analytics organizations; assess scale, power, networking, and workload economics. |
| Veeam Software | Backup and resilience across cloud and on-premises environments. | Businesses and channel partners; licensing and deployment choices can be complicated. |
| Wasabi | Cloud object storage. | SMBs, backup users, media teams, and archives; consider transfer, retrieval, replication, and support costs. |
CRN’s storage category source.
What the 2025 list reveals about cloud computing
AI is reshaping every layer
AI appears in infrastructure through GPUs and high-performance systems; in software through lakehouses, warehouses, databases, vector search, and copilots; in security through protection of models and data; in operations through anomaly detection and automation; and in storage through high-throughput data infrastructure.
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That does not make every listed company an AI company. Distinguish generally available products from previews, announcements, partnerships, and road maps. AI costs also include model usage, GPU capacity, data movement, storage, privacy controls, and operations.
Hybrid and multicloud remain operational realities
Organizations may keep systems on premises for latency, regulation, existing investments, or specialized performance while using multiple clouds. The benefit is flexibility; the cost is duplicated tooling, skills, networking, identity, governance, and troubleshooting.
Security and storage are converging
Immutable backups, recovery testing, data discovery, identity controls, and ransomware response increasingly form one resilience strategy. Buying a security or backup product does not automatically create resilience: teams must define recovery-time and recovery-point objectives, protect credentials, isolate copies, and regularly test restoration.
Data platforms are becoming AI infrastructure
Databases, streaming systems, transformation tools, analytics platforms, and vector stores increasingly support AI pipelines. The right choice depends on data shape, latency, consistency, retrieval method, portability, governance, and total consumption cost—not simply whether a vendor advertises an AI feature.
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Rackspace Technology, TierPoint, RapidScale, MSP360, Nerdio, HYCU, and similar companies can be relevant when an organization lacks cloud or backup specialists. Managed services trade direct control and additional fees for expertise, support, and operational capacity.
Best Value
Which companies may matter most for AI?
This is a category-level interpretation, not an independent AI ranking:
- AI infrastructure: AWS, CoreWeave, Google Cloud, Microsoft, Oracle, IBM, Dell Technologies, HPE, Broadcom, DDN, VAST Data, and Nutanix.
- AI data platforms: Databricks, Snowflake, MongoDB, Cloudera, Confluent, Couchbase, Pinecone, dbt Labs, and EDB.
- AI security: Aqua Security, CrowdStrike, Cyera, Palo Alto Networks, SentinelOne, Wiz, and Zscaler.
- AI operations: AppDynamics, InsightFinder, LogicMonitor, ScienceLogic, Unravel Data, Rafay, and Spectro Cloud.
The relevant question is not “which company wins AI?” It is whether the product solves your bottleneck: compute availability, data preparation, retrieval, security, deployment, observability, or cost control.
How to choose among these companies
Evaluate each candidate against the same questions:
- Define the workload: application hosting, analytics, AI training, SaaS protection, backup, file sharing, compliance, or network operations.
- Identify the buyer and operator: developer, data team, security team, infrastructure group, MSP, or business department.
- Check deployment: SaaS, public cloud, private cloud, on premises, hybrid, edge, or managed service.
- Map dependencies: AWS, Azure, Google Cloud, Microsoft, Oracle, Kubernetes, PostgreSQL, existing storage, identity provider, and network.
- Model total cost: include compute, storage, transfer and egress, support, seats, minimum commitments, migration, implementation, professional services, compliance, and internal labor.
- Test portability: understand proprietary APIs, export options, open standards, data mobility, and the practical cost of leaving.
- Validate operations: request service-level terms, support escalation, security documentation, recovery procedures, status history, and references relevant to your workload.
- Pilot the failure case: restore data, revoke credentials, survive an outage, move workloads, or investigate an incident before signing a large commitment.
By organization type
- Startup or small business: favor manageable services, clear support, simple billing, and minimal administration. Managed cloud providers, SMB-oriented backup tools, and consumption-based storage may be easier than assembling a large platform.
- Midmarket company: compare managed services with hiring specialists. Prioritize Microsoft or existing-cloud compatibility, predictable support, backup testing, and practical migration effort.
- Large enterprise: assess governance, identity, data residency, procurement, resilience, integration, and contractual terms—not just features.
- MSP or reseller: examine multitenancy, delegated administration, margins, APIs, support escalation, and whether the platform reduces operational effort across customers.
- AI developer: measure GPU availability, model and vector-database costs, data movement, latency, privacy, region availability, and lock-in.
- Security team: map coverage across identity, code, cloud posture, workloads, data, endpoints, and response. No single product automatically solves all of them.
- Data engineering team: compare ingestion, transformation, storage, query performance, governance, lineage, portability, and consumption pricing.
- Compliance-heavy organization: verify residency, certifications, audit access, encryption, key management, retention, recovery, subcontractors, and contractual obligations.
Main trade-offs
| Choice | Benefit | Cost or risk |
|---|---|---|
| Hyperscaler breadth | Many integrated services and global options. | Complex architecture, billing, permissions, and potential lock-in. |
| Managed service | External expertise and operational support. | Additional fees and less direct control. |
| Best-of-breed tools | Strong functionality for a specific problem. | More vendors, integrations, contracts, and telemetry silos. |
| Platform consolidation | Fewer tools and potentially simpler procurement. | Broader dependency on one vendor and possible feature compromises. |
| Agentless security | Faster deployment and less endpoint software. | Potentially less runtime depth than agent-based monitoring. |
| Multicloud portability | Choice and reduced dependence on one provider. | Duplicated operations, skills, networking, and governance. |
| AI performance | Faster training, inference, or analytics. | GPU capacity, power, data transfer, and usage costs. |
| Immutable backup | Better protection against tampering and ransomware. | Does not replace recovery testing, identity controls, or recovery planning. |
| Open source or open standards | Flexibility and potential portability. | Commercial support, governance, security, and operational responsibility. |
Important limitations of the list
- It is not a numerical ranking and does not identify a single winner.
- CRN does not disclose a transparent scoring methodology for comparing all 100 companies.
- A hyperscaler, SaaS vendor, GPU provider, backup company, and observability startup solve different problems and should not be compared as though they were substitutes.
- Announcements, acquisitions, funding, partnerships, and product launches do not prove reliability, customer satisfaction, lower total cost, or production success.
- Product names, ownership, executive roles, pricing, availability, regions, and corporate strategy may have changed since 2025.
- Vendor claims such as “fastest,” “lowest cost,” or “most secure” require attribution and independent context.
The original list is most useful as a starting point for research. It should be followed by architecture review, security and compliance diligence, a total-cost model, contract review, customer references, and a realistic proof of concept.
Full list of all 100 companies
| Category | Companies |
|---|---|
| Cloud infrastructure | Amazon Web Services; Broadcom; CoreWeave; Cisco Systems; Dell Technologies; Digital Realty; Flexential; Google Cloud; Hewlett Packard Enterprise; IBM; Lenovo; Lumen Technologies; Microsoft; Nutanix; Oracle; Rackspace Technology; Red Hat; Scale Computing; TierPoint; Zadara. |
| Cloud software | Agiloft; Cloudera; Confluent; Couchbase; Cribl; Databricks; dbt Labs; EDB; Genesys; Intermedia Cloud Communications; MongoDB; Pinecone; Qlik; Salesforce; SAP; ServiceNow; Snowflake; SugarCRM; ThoughtSpot; Workday. |
| Cloud security | Aqua Security; Check Point Software Technologies; Cloudflare; CrowdStrike; Cyera; Fortinet; Illumio; Netskope; OpenText Cybersecurity; Orca Security; Palo Alto Networks; Qualys; SentinelOne; Skyhigh Security; Snyk; Sophos; Tenable; Trend Micro; Wiz; Zscaler. |
| Cloud monitoring and management | AlertOps; AppDynamics; Atlassian; Devo Technology; Gigamon; Gurucul; Hydrolix; InsightFinder; Kion; LogicMonitor; Nerdio; NetBrain; Nobl9; Rafay; RapidScale; ScienceLogic; Spectro Cloud; Tigera; Unravel Data; Zebra Technologies. |
| Cloud storage | Arcserve; Cohesity; Commvault; DataCore; DDN; Hammerspace; HYCU; Infinidat; Komprise; MinIO; MSP360; Nasuni; NetApp; Panzura; Pure Storage; Quantum; Rubrik; VAST Data; Veeam Software; Wasabi. |
Category details are based on CRN’s infrastructure, software, security, monitoring and management, and storage features.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
