The 2024 CRN AI 100 was a market map, not a ranked list of the 100 “best” AI companies. CRN’s inaugural selection grouped 100 companies across five parts of the enterprise AI stack: data center and edge, cloud, cybersecurity, software, and data and analytics.
For business buyers—and anyone assessing the financial strength or commercial prospects of AI vendors—the list is useful because it shows how broad the AI market had become by 2024. It includes chipmakers and server companies, hyperscalers, security providers, data platforms, enterprise software vendors and tools aimed specifically at managed service providers (MSPs).
Important date note: CRN’s list was published in 2024. It is a historical editorial snapshot, not a current 2026 ranking, investment recommendation or guarantee that every named product, executive, ownership structure or partner program remains unchanged.
What the CRN AI 100 was—and was not
CRN described the AI 100 as a selection of companies making notable investments in artificial intelligence and generative AI. Its emphasis was the technology channel: solution providers, MSPs, managed security service providers (MSSPs), enterprise technology partners and vendors that help customers deploy AI.
#1 Best Overall
That explains why the list extends well beyond foundation-model developers. Commercial AI requires processors, servers, networking, storage, clean data, cloud services, security controls, application software and operational workflows. CRN’s five categories were designed to reflect that broader ecosystem.
Read the original overview at CRN’s 2024 AI 100 article.
It was not a numbered ranking
CRN did not present a standardized 1-to-100 leaderboard. The available presentation does not establish a common scoring system, weighted methodology, performance benchmark or comparable pricing analysis. “AI 100” means 100 selected companies—not that Nvidia was ranked first or that any other company occupied a particular position.
Descriptions should therefore be read as editorial or vendor-attributed characterizations. Inclusion does not prove that a company is the safest, cheapest, fastest or best fit for a particular buyer.
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The five-part market map
| Category | Companies | What it represents |
|---|---|---|
| Data center and edge | 25 | Compute, servers, storage, networking, AI PCs, edge systems and GPU orchestration |
| Cloud | 20 | Hyperscalers, specialty GPU clouds, AI platforms and cloud-management tools |
| Cybersecurity | 20 | Threat detection, endpoint, cloud, SASE, email and AI-security controls |
| Software | 20 | AI assistants, enterprise applications, developer tools and MSP automation |
| Data and analytics | 15 | Data preparation, databases, vector search, MLOps and analytics |
Together, the categories describe a deployment chain: compute and networking → storage and data → models and platforms → security and governance → applications and operations.
Data center and edge: 25 companies
CRN’s data-center and edge group includes Acer, Alcion, AMD, Cisco Systems, Cohesity, DataDirect Networks, Dell Technologies, Extreme Networks, Hewlett Packard Enterprise, Hitachi Vantara, HP Inc., Intel, Juniper Networks, Lenovo, NetApp, Nutanix, Nvidia, Prosimo, Pure Storage, Run:ai, Scale Computing, Supermicro, Vast Data, Versa Networks and Weka.
Rank #2
These companies cover the physical and infrastructure layer of AI. Nvidia, AMD and Intel represent processors and accelerators. Dell Technologies, HPE, Lenovo and Supermicro supply servers, workstations and related systems. Acer and HP Inc. bring AI-capable PCs and endpoint devices into the picture.
Storage and data-infrastructure roles include NetApp, Pure Storage, Weka, Vast Data, DataDirect Networks, Cohesity and Hitachi Vantara. Cisco, Juniper, Extreme Networks and Versa represent networking and related operational capabilities. Run:ai focuses on GPU resource optimization and orchestration, while Prosimo addresses multicloud networking for AI workloads. Nutanix and Scale Computing are relevant to hybrid, private and edge infrastructure discussions.
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CRN’s full category coverage is available in its data center and edge list.
Why this category matters
It represents the “picks and shovels” side of AI: the hardware and software needed to train, fine-tune or run models. Buyers should not compare these companies using a single idea of speed. Results depend on model architecture, precision, batch size, concurrency, storage protocol, network design and whether the workload is training or inference.
Cloud: 20 companies
The cloud category contains Altair, Amazon Web Services, Cirrascale Cloud Services, Dataminr, Dynatrace, Google Cloud, H2O.ai, HashiCorp, IBM, Lambda Labs, Microsoft, MongoDB, Nerdio, Oracle, PagerDuty, Red Hat, Salesforce, Snowflake, Spectro Cloud and VMware by Broadcom.
AWS, Microsoft, Google Cloud, IBM and Oracle represent broad cloud infrastructure, AI services, model access and enterprise platforms. Cirrascale and Lambda Labs represent more specialized GPU-cloud options. H2O.ai and Red Hat are associated with AI platforms and deployment ecosystems, while MongoDB and Snowflake provide data capabilities used in AI applications.
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See CRN’s 20 cloud companies for its original descriptions.
The key point is that “cloud AI” is broader than training a large language model. This layer can provide compute, model hosting, data access, governance, observability, application development and cost controls. A hyperscaler’s inclusion reflects ecosystem breadth, not proof that it is technically or financially superior for every workload.
Cybersecurity: 20 companies
CRN listed Abnormal Security, CrowdStrike, Darktrace, Deep Instinct, Fortinet, Halcyon, Lacework, Netskope, Orca Security, Palo Alto Networks, SentinelOne, SlashNext, Splunk, Tanium, Tenable, Trend Micro, Vectra AI, Veracode, Wiz and Zscaler.
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- Endpoint and autonomous security: CrowdStrike, SentinelOne, Deep Instinct and Tanium.
- Cloud, network and SASE security: Netskope, Palo Alto Networks, Orca Security, Wiz, Zscaler and Lacework.
- Threat detection and response: Darktrace, Vectra AI, Splunk and Fortinet.
- Email and phishing protection: Abnormal Security and SlashNext.
- Ransomware defense: Halcyon.
- Exposure and application security: Tenable and Veracode.
The complete source is CRN’s AI cybersecurity list.
Rank #4
- Author: Guillebeau, Chris.
- Publisher: Currency
- Pages: 304
- Publication Date: 2012-05-08
- Edition: NO-VALUE
“AI-powered cybersecurity” can mean classical machine learning, behavioral analytics, a generative-AI assistant, automated remediation or security controls for AI systems. Those are not interchangeable. A tool that summarizes alerts is not necessarily a tool that reliably detects or remediates threats. Buyers should request false-positive and false-negative information, response latency, data-retention details and human-approval controls.
Software: 20 companies
The software group includes Anaconda, ConnectWise, CrushBank, Cynomi, Dataiku, DataRobot, Hatz AI, Intermedia, Kaseya, LogicMonitor, MSPbots, N-able, OpenText, Pia, Qualtrics, Rewst, SAP, ServiceNow, SuperOps AI and Ternary.
This is the most channel-specific category. It includes enterprise applications, data-science platforms, AI assistants and tools that automate repetitive work for MSPs and other service providers.
- Data science and AI platforms: Anaconda, Dataiku and DataRobot.
- MSP and IT-service automation: ConnectWise, Kaseya, MSPbots, N-able, Rewst, SuperOps AI and Pia.
- AI knowledge and service tools: CrushBank, Cynomi and Hatz AI.
- IT operations and observability: LogicMonitor.
- Enterprise application AI: OpenText, Qualtrics, SAP and ServiceNow.
- Cloud financial operations: Ternary.
CRN’s software category focuses heavily on assistants, administrative automation and potential new service-provider revenue streams.
CRN also cited an IDC forecast that worldwide enterprise spending on generative-AI software and related infrastructure hardware and services would exceed $38 billion and reach $151.1 billion in 2027. That was a dated 2024 forecast, not a current 2026 market measurement.
Data and analytics: 15 companies
The data and analytics companies are Alluxio, Alteryx, Couchbase, Databricks, Dataloop, DataStax, Domino Data Lab, DotData, Informatica, Kinetica, Qlik, SAS, Starburst, ThoughtSpot and Weights & Biases.
This layer covers the information AI systems depend on: collecting, preparing, governing, querying, monitoring and analyzing data.
- Data orchestration and infrastructure: Alluxio and Starburst.
- Analytics and business intelligence: Alteryx, Qlik, SAS and ThoughtSpot.
- Databases and vector search: Couchbase, DataStax and Kinetica.
- Lakehouse and unified data/AI platforms: Databricks.
- Training-data operations: Dataloop.
- MLOps and model governance: Domino Data Lab and Weights & Biases.
- Data integration and quality: Informatica.
- Feature engineering and machine-learning automation: DotData.
CRN’s complete data and analytics category helps explain why AI projects often fail after the pilot stage. Poor data quality, weak lineage, incomplete access, stale information, inadequate monitoring and unclear ownership can matter more than the choice of model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a company from the list
- Define the workload. Is the need training, inference, retrieval-augmented generation, analytics, security operations or workflow automation?
- Choose the deployment model. Compare public cloud, private cloud, on-premises, edge, SaaS and hybrid requirements.
- Check data compatibility. Review structured and unstructured sources, vector search, data lakes, SaaS connectors and legacy systems.
- Test governance. Ask about access controls, audit trails, model monitoring, data residency, privacy, retention and regulatory requirements.
- Measure integration effort. Identify dependencies on identity, IT service management, CRM, ERP, security and observability systems.
- Model total cost. Include GPUs, storage, tokens, inference, data transfer, licensing, implementation, support and ongoing operations.
- Assess the channel model. For partners, examine margins, certifications, training, marketplaces, managed-service opportunities and white-label rights.
- Demand production evidence. Look for measurable changes in resolution time, alert volume, compute waste, manual work or deployment time—not merely a chatbot demonstration.
- Check lock-in and exit options. Review proprietary APIs, model dependencies, data formats, hardware requirements and migration paths.
Trade-offs buyers should expect
- Hyperscaler breadth versus neutrality: Integrated identity, data and procurement can be valuable, but platform dependence may increase.
- Specialty GPU cloud versus enterprise integration: A specialist may suit compute-heavy teams, while a hyperscaler may fit organizations already invested in its governance and identity stack.
- Proprietary versus open models: Proprietary services may be simpler to launch; open models can offer more control and portability.
- Cloud versus edge: Cloud provides centralized scale; edge can reduce latency, bandwidth use and data movement.
- Best-of-breed versus consolidation: Specialized products may excel in one use case, but multiple tools increase integration and procurement complexity.
- Automation versus oversight: High-impact actions need approvals, logging, rollback and clearly assigned responsibility.
- Startup innovation versus durability: Startups may move quickly, while larger vendors often provide broader support and procurement channels.
What the CRN list does not tell you
The list does not provide standardized benchmarks, customer references, pricing comparisons, implementation estimates or a current-status update. It also does not establish that the companies are equally mature. Some entries are large platform vendors, some are infrastructure specialists and some are startups highlighted for potential or channel relevance.
Corporate events can change the meaning of an entry. Acquisitions, renamed or discontinued products, ownership changes and altered partner programs may affect a company’s present position. Executive names and product descriptions should be treated as details CRN listed in 2024, not as current facts without separate verification.
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Commercial and financial perspective
For a business buyer, the practical question is not “Which company won?” It is “Which layer of the AI stack creates value for this organization, and what will it cost to operate?” A cloud API may be economical for intermittent use but expensive at sustained scale. On-premises infrastructure may offer control and predictable utilization but requires capital, power, cooling and specialist staff. A broad enterprise platform may simplify procurement while creating lock-in or implementation expense.
Potential products to investigate include Amazon Bedrock, Microsoft Azure AI Foundry, Google Cloud Vertex AI, NVIDIA AI Enterprise, Databricks, Informatica, CrowdStrike Falcon, Palo Alto Networks Prisma Cloud, ServiceNow AI and MSP automation platforms such as Rewst. These are starting points for due diligence, not endorsements.
The CRN source supplies no standardized prices. Buyers should use current vendor pricing pages and official calculators, then build a model covering compute, storage, requests, tokens, data transfer, support, licensing and implementation.
2026 reader’s note
Status of this source: CRN’s AI 100 was published in 2024. It remains useful as a historical map of how the enterprise AI market was organized at that point. Product availability, pricing, ownership, leadership, partner programs and company status may have changed by August 18, 2026, so verify those details before making a purchase, partnership or investment decision.
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