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CRN’s 2024 AI 100 included 15 companies in its data-and-analytics category, spanning distributed data access, databases, data integration, business intelligence, machine-learning operations, and AI application development. The list is best understood as a time-stamped editorial snapshot—not a current ranking, investment recommendation, revenue table, or proof that these are the 15 objectively best vendors in 2026.
That distinction matters because the companies solve very different problems. Some help AI systems access and govern data; others help analysts ask questions, developers build applications, or data-science teams deploy and monitor models. For technology buyers, investors, journalists, and channel partners, the useful question is not simply “Which company is hottest?” but “Which layer of the AI data stack does each company serve?”
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What CRN’s 2024 AI 100 data-and-analytics category covered
CRN’s premise was straightforward: AI systems are only as useful as the data behind them. Data must be accessible, sufficiently fresh, well-prepared, governed, and usable by the people and applications that depend on it.
That creates two overlapping markets:
- Data infrastructure for AI: collecting, moving, storing, preparing, governing, orchestrating, and serving data.
- AI-enabled analytics: natural-language querying, automated insight generation, machine-learning workflows, conversational analytics, and AI-assisted business intelligence.
CRN’s broader AI 100 divided companies into cloud, security, data and analytics, data center and edge, and software categories. Its data-and-analytics feature selected the following 15 companies: Alluxio, Alteryx, Couchbase, Databricks, Dataloop, DataStax, Domino Data Lab, DotData, Informatica, Kinetica, Qlik, SAS, Starburst, ThoughtSpot, and Weights & Biases.
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CRN did not publish a numerical ranking, scoring methodology, market-share comparison, revenue table, or comparative product test. Therefore, “hottest” should be read as CRN’s editorial designation for companies it viewed as gaining importance in AI during 2024.
Read CRN’s original 2024 feature and its broader AI 100 overview for the original context.
The 15 companies at a glance
| Company | Primary role | AI-relevant capability | Best suited to |
|---|---|---|---|
| Alluxio | Data orchestration | High-throughput access to distributed data | Data-intensive AI infrastructure |
| Alteryx | Analytics automation | AI-assisted analytics workflows | Analysts and analytics teams |
| Couchbase | Operational database | Vector and semantic search | AI-enabled applications |
| Databricks | Data and AI platform | Unified analytics, machine learning, and generative AI | Enterprise data and AI teams |
| Dataloop | AI data engine | Annotation and unstructured-data workflows | Computer vision and multimodal AI |
| DataStax | Distributed database | Scalable real-time AI data | Production AI applications |
| Domino Data Lab | MLOps | Model development and governance | Enterprise data-science teams |
| DotData | ML automation | Automated feature discovery | Applied ML teams |
| Informatica | Data management | Integration, governance, and access | Complex enterprise data estates |
| Kinetica | Real-time database | Time-series, spatial, and conversational analytics | Low-latency analytics |
| Qlik | Integration and BI | Data preparation and AI-assisted insights | BI and data-integration buyers |
| SAS | Enterprise analytics | Industry AI, risk, fraud, and modeling | Regulated and analytics-heavy sectors |
| Starburst | Federated analytics | Distributed data access for AI | Multicloud and hybrid estates |
| ThoughtSpot | AI analytics | Search and natural-language BI | Business-user analytics |
| Weights & Biases | MLOps | Experiment, model, and LLM lifecycle tracking | ML and AI developers |
The grouping above is an analytical synthesis of CRN’s entries, not CRN’s formal ranking or subcategory system.
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The company profiles
1. Alluxio: orchestrating access to distributed AI data
CRN highlighted Alluxio’s data-orchestration and provisioning technology as a response to the input/output demands of AI training and machine-learning workloads.
Alluxio sits near the infrastructure layer. It can be relevant when data is spread across cloud object storage, data centers, and other systems, while AI jobs require high-throughput access. The concept is closer to a data-access and orchestration layer than to a conventional business intelligence platform.
Potential buyers include organizations running large, data-intensive training or analytics workloads across multiple storage environments. The key diligence question is whether the performance and data-access problem is substantial enough to justify another infrastructure layer. Compare it with native cloud storage, distributed file systems, caching technologies, and the organization’s lakehouse architecture. Deployment, operations, data locality, security, and failure recovery should be evaluated before assuming that orchestration will improve every workload. See Alluxio’s official site.
2. Alteryx: analytics automation for analysts and data teams
CRN described Alteryx as positioning AI to make analytics more productive and accessible. Its 2024 entry highlighted AiDIN, introduced in 2023, as a generative-AI engine integrated with Alteryx Analytics Cloud.
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Buyers should separate automation from autonomous decision-making. Ask which tasks are generated, which are merely assisted, how results are validated, and what permissions and audit controls apply. Alteryx may be a practical fit where repeatable analyst workflows matter; it may be excessive for a small team with clean data and only occasional analysis. Product details should be checked against Alteryx’s current materials.
3. Couchbase: operational data and vector search for AI applications
CRN highlighted vector search in Couchbase Server and Capella for applications such as chatbots, recommendation engines, and semantic search.
Couchbase is an operational database option for applications that need conventional application data alongside vector or semantic retrieval capabilities. That can be useful for retrieval-augmented generation and other AI-enabled products that must combine live application state with relevant documents or embeddings.
Vector search may improve retrieval grounding, but it does not eliminate hallucinations. Results still depend on embedding quality, chunking, metadata, retrieval settings, source quality, prompt design, and application controls. Evaluate transactional requirements, consistency, latency, scale, hybrid-search maturity, and whether an existing cloud database or specialized vector system is a better fit. Consult Couchbase Capella’s product information.
4. Databricks: the broad data-and-AI platform
Databricks was the broadest platform in CRN’s group. CRN pointed to its Data Intelligence Platform, which brought data engineering, analytics, machine learning, and generative-AI work into a unified environment. CRN also highlighted Databricks’ 2023 MosaicML acquisition, reported at $1.3 billion, and its relevance to large-language-model development and training.
For buyers, Databricks is less a narrow AI feature than a platform choice. It may suit organizations standardizing data engineering, analytics, model development, governance, and AI applications around one ecosystem. The trade-off is breadth: a broad platform can reduce integration work but introduce cost, architectural complexity, and platform dependence.
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Ask whether the organization needs a lakehouse-scale platform or a focused tool. Map existing cloud, warehouse, notebook, BI, and ML investments before adding it. Also distinguish model training, fine-tuning, inference, retrieval, and analytics; they have different infrastructure, cost, and governance requirements. Start with Databricks’ product overview.
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CRN highlighted Dataloop’s AI-development platform and data engine for managing relevant, high-quality data, especially computer-vision and other unstructured-data workloads involving video, images, audio, and text.
Dataloop is most relevant when model performance depends on labeling, reviewing, curating, and versioning large datasets. Human-in-the-loop workflows and quality control can be as important as the model itself.
Evaluate supported annotation formats, reviewer workflows, quality measurement, dataset versioning, multimodal and video support, privacy controls, and integration with the existing model-development stack. It is likely unnecessary for straightforward structured-data machine learning. See Dataloop’s platform page.
6. DataStax: distributed operational data for real-time AI
CRN positioned DataStax Astra DB, based on Apache Cassandra, as a scalable real-time data engine for responsive generative-AI applications. CRN also noted DataStax’s AWS Generative AI Competency Partner designation at the time.
DataStax belongs to the operational-data and application-backend segment. Its strongest potential fit is an AI application that needs fast access to changing data at scale, including vector retrieval or other context-serving workloads.
Cassandra’s distributed architecture has implications for consistency, data modeling, operations, and failure handling. Compare managed Astra DB with self-managed Cassandra and with the organization’s existing cloud database. Check supported clouds, deployment options, latency expectations, backup and recovery, and vector-search requirements. Product information is available from DataStax Astra DB.
7. Domino Data Lab: MLOps, governance, and controlled AI access
CRN described Domino as an MLOps provider for building, deploying, and managing models. It highlighted the Domino Enterprise AI Platform and Domino AI Gateway, which addressed risks associated with broad access to external large language models.
Domino is aimed at organizations that need reproducibility, collaboration, deployment controls, governance, and lifecycle management across data-science and AI teams. It may complement existing notebooks, cloud infrastructure, registries, and deployment systems rather than replace every one of them.
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Diligence should cover experiment reproducibility, supported infrastructure and clouds, model governance, approval workflows, identity controls, and how AI Gateway policies monitor or restrict LLM access. Buyers should determine whether Domino fills a genuine lifecycle gap or overlaps heavily with native cloud ML services. See Domino’s Enterprise AI Platform.
8. DotData: automated feature discovery and ML operations
CRN highlighted DotData’s automated feature-discovery and machine-learning-operations tools, including Feature Factory, DotData Ops, and DotData Insight.
DotData represents automation around one of applied machine learning’s hardest steps: turning raw data into useful, model-ready signals. Automated discovery can help teams explore relationships and accelerate workflow development, but it does not remove the need for domain expertise.
Ask how discovered features are explained, validated, versioned, and tested for leakage or spurious correlations. Time-based validation is especially important when a feature accidentally includes information that would not have been available at prediction time. Compare its integration with existing warehouses, notebooks, and MLOps tools. See DotData’s product information.
9. Informatica: integration, quality, metadata, and governance
CRN presented Informatica as an enterprise data-management company using CLAIRE, described by the company as a cloud-centric, AI-backed, real-time engine. CRN also highlighted Cloud Data Access Management for automating data-access policy enforcement.
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Informatica is the clearest data-integration and governance representative in the group. Its AI value proposition depends on metadata, data quality, lineage, privacy, access management, and the ability to connect complex legacy and modern systems.
It may suit large heterogeneous estates, but implementation and licensing complexity should be treated as central buying questions. Evaluate lineage, policy enforcement, auditability, identity integration, cloud coverage, data quality, and the work required before AI applications can trust the resulting data. Review Informatica’s current product portfolio.
10. Kinetica: real-time, spatial, and time-series analytics
CRN highlighted Kinetica’s database for real-time analytics and generative-AI tasks involving time-series and spatial data. It also described natural-language-to-SQL querying through ChatGPT integration and a native LLM for ad-hoc analysis.
Kinetica is most relevant when data is continuously changing, geographically complex, time-sensitive, or operationally urgent. A specialized database may be justified where general-purpose warehouses or lakehouses cannot meet the workload’s latency or query requirements.
Natural-language SQL needs safeguards. Generated queries can be wrong, ambiguous, expensive, or unsafe if users lack appropriate permissions. Test query validation, cost controls, source freshness, spatial functionality, and failure handling. Avoid treating “real-time” as a universal performance guarantee; define the actual latency and volume requirements. See Kinetica’s product page.
11. Qlik: integration, preparation, BI, and AI-assisted insights
CRN described Qlik’s portfolio as combining data integration, quality, preparation, analytics, and AI capabilities. It highlighted Qlik Staige, AI-assisted script generation, AI-generated insights, and the acquisition of Kyndi natural-language-processing technology.
Qlik spans the pipeline from moving and preparing data to exposing it through business intelligence and natural-language interactions. That breadth makes it relevant to both data-integration buyers and BI teams, but it also means the evaluation should identify the primary problem rather than treating the entire portfolio as necessary.
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12. SAS: mature enterprise analytics for regulated and complex sectors
CRN highlighted SAS Viya and its Composite AI portfolio, including natural-language processing, computer vision, deep learning, fraud detection, risk management, and model development. It also mentioned SaaS products such as SAS App Factory.
SAS is the mature enterprise analytics and industry-solution vendor in this group. It can be especially relevant to banking, insurance, health care, government, fraud, and risk use cases where validation, explainability, and established analytical processes matter.
Buyers should examine Viya deployment options, migration paths for legacy SAS estates, governance, explainability, industry functionality, and total implementation effort. SAS may be a poor fit for a lightweight developer-first project that needs only a narrow API or open-source model workflow. See SAS Viya’s official page.
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CRN described Starburst’s data-lakehouse platform as supporting AI and machine-learning workloads over petabyte-scale data distributed across on-premises and cloud systems. It also cited collaboration with Dell Technologies for AI and ML workloads.
Starburst is a federated data-access and analytics option for organizations that cannot—or do not want to—centralize all data before making it available to analysts and AI systems.
Federation can reduce copying and improve access to existing sources, but it can introduce query-performance, source-compatibility, freshness, and governance challenges. Understand query pushdown, permissions, caching, data contracts, and which workloads still require physical data movement or specialized storage. Compare Starburst with a central lakehouse and existing query engines. See Starburst Galaxy.
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14. ThoughtSpot: search-driven and natural-language analytics
CRN positioned ThoughtSpot as an AI-powered analytics platform with natural-language search and ThoughtSpot Sage, which used GPT and large-language-model technology to generate answers. CRN also noted ThoughtSpot’s $200 million acquisition of Mode Analytics in 2023.
ThoughtSpot is designed to make analytics more accessible through search and natural-language interaction. The convenience, however, depends on trustworthy semantic models, business definitions, joins, permissions, and source data.
A fluent answer can still be analytically wrong. Buyers should ask whether users can inspect queries and source data, how ambiguous questions are handled, and what controls govern semantic models and embedded analytics. Compare ThoughtSpot with Qlik, Alteryx, SAS, and existing BI tools. Current capabilities should be verified at ThoughtSpot’s product page.
15. Weights & Biases: tracking experiments, models, prompts, and AI behavior
CRN highlighted Weights & Biases tools for machine-learning development, experiment tracking, model lifecycle management, workflow automation, interactive ML application development, and LLM monitoring. The 2024 entry named Launch, Models, Weave, and Prompts.
Weights & Biases belongs to the developer and MLOps layer. It is relevant when teams need visibility into experiments, datasets, model versions, evaluations, prompts, traces, and production behavior across traditional ML and generative-AI systems.
Before adoption, verify integrations with cloud, notebook, deployment, and identity systems. Pay particular attention to telemetry governance: prompts, responses, traces, datasets, or personally identifiable information may be captured depending on configuration. Confirm retention, regional processing, access controls, deletion, and training-use policies directly with the vendor. See Weights & Biases’ official site.
How the 15 companies differ by AI workload
These vendors are not interchangeable. A practical shortlist should begin with the problem rather than the brand:
- Need to move and govern enterprise data: Informatica and Qlik.
- Need a broad data-and-AI platform: Databricks.
- Need real-time operational AI data: Couchbase, DataStax, or Kinetica.
- Need distributed data access: Alluxio or Starburst.
- Need business-user analytics: Alteryx, Qlik, ThoughtSpot, or SAS.
- Need ML lifecycle management: Domino Data Lab or Weights & Biases.
- Need AI dataset and annotation workflows: Dataloop.
- Need automated feature discovery: DotData.
These are overlapping editorial groupings, not exclusive classifications. Databricks also supports AI development; Informatica and Qlik span integration and analytics; Couchbase and DataStax combine databases with AI application features; and Domino and Weights & Biases overlap in MLOps while emphasizing different parts of the lifecycle.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A buyer’s framework for evaluating the list
1. Identify the stack layer
Decide whether the bottleneck is data integration, storage, federation, preparation, governance, BI, annotation, model development, deployment, monitoring, or application serving. A platform selected for the wrong layer may add cost without solving the underlying problem.
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2. Define the workload
Separate model training, retrieval-augmented generation, real-time application serving, batch analytics, business intelligence, computer vision, fraud analytics, and model monitoring. Each has different latency, data, compute, and control requirements.
3. Describe the data
Document whether the data is structured or unstructured, batch or streaming, centralized or distributed, time-series or geospatial, sensitive or regulated, and operational or historical.
4. Check deployment constraints
Determine whether SaaS, public cloud, hybrid, on-premises, sovereign, or tightly controlled deployment is required. Managed services reduce operational burden but may limit portability, residency choices, or infrastructure control.
5. Test governance before convenience
Require access controls, lineage, auditability, data residency, model explainability, prompt and response logging where appropriate, human review, and clear failure-handling procedures. Natural-language interfaces and automated features increase the need for validation rather than removing it.
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List the current cloud provider, warehouse or lakehouse, Kubernetes environment, databases, BI tools, MLOps platform, identity provider, and security systems. Integration work often determines the real cost of an enterprise AI purchase.
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7. Match the buyer and user
Data engineers, analytics engineers, business analysts, data scientists, ML engineers, application developers, and compliance teams have different success criteria. A tool that improves one group’s productivity may create governance or operating burdens for another.
Important trade-offs
- Broad platform versus focused specialist: Broad platforms such as Databricks, SAS, Informatica, and Qlik can consolidate capabilities, while specialists may be easier to deploy for a narrow bottleneck.
- Centralization versus federation: Centralization simplifies control and optimization but can be expensive or slow. Federation preserves distributed data access but can complicate performance and governance.
- Automation versus control: Generated SQL, features, insights, and workflows can improve productivity while making testing, review, and auditability more important.
- Managed versus self-managed: Managed databases and platforms reduce operations but may constrain portability, residency, or customization.
- Vector search versus a complete AI architecture: Vector retrieval is only one component. Production systems also need source-quality controls, evaluation, identity, monitoring, cost management, and application logic.
- Open ecosystem versus integrated platform: Open tools can support composability and reduce lock-in; integrated platforms may reduce procurement and integration work.
- Governance versus experimentation speed: Regulated organizations may value lineage, reproducibility, and policy enforcement more than the fastest prototype.
Failure modes buyers should test
Fluent but incorrect AI answers
Conversational analytics can produce plausible answers from incorrect joins, incomplete data, ambiguous business terms, or stale sources. Require query visibility, source references, semantic definitions, permissions, and human review for material decisions.
Poor data quality
Duplicate, missing, stale, biased, or incorrectly labeled data can undermine every product in this category. Data-quality controls are prerequisites, not optional add-ons.
Data leakage during model development
Automated feature discovery and ML workflows can accidentally use future information or the target variable. Test for leakage and use temporal validation where the business problem involves time.
Sensitive prompts and telemetry
Model-development and LLM-monitoring systems may process prompts, responses, traces, datasets, and personal information. Verify retention, regional processing, access controls, deletion, and whether submitted data can be used for vendor training.
Stale 2024 product references
Names such as AiDIN, Qlik Staige, ThoughtSpot Sage, Domino AI Gateway, and Weights & Biases Prompts describe the 2024-era CRN context. Product names, packaging, availability, leadership, pricing, and strategic positioning may have changed. Check current first-party documentation before making a purchase or publishing a current recommendation.
What this list does—and does not—tell investors and buyers
The CRN list shows the breadth of the enterprise AI data market. Most of these companies are not foundation-model providers. They enable AI through databases, orchestration, integration, governance, analytics, annotation, MLOps, and application workflows.
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Enterprise pricing is commonly affected by data volume, compute, users, deployment model, support, implementation, and negotiated contract terms. Do not infer current prices, free-trial availability, or plan limits from the 2024 article. Use the linked official product pages and request current commercial terms directly from the vendors.
Frequently Asked Questions
Did CRN rank these companies from first to fifteenth?
No. CRN selected 15 companies for its 2024 data-and-analytics category, but the feature did not disclose a numerical ranking or comparative scoring methodology.
Are these companies all AI model providers?
No. Most provide infrastructure or software around AI, including databases, data integration, governance, analytics, annotation, MLOps, and AI application development.
Which vendors are most relevant for a broad data-and-AI platform?
Databricks is the broadest platform in this group, but the right choice depends on existing cloud, warehouse, BI, ML, governance, and deployment investments.
Can vector search eliminate large-language-model hallucinations?
No. Vector search may improve retrieval grounding, but answer quality also depends on source data, embeddings, chunking, retrieval settings, prompts, permissions, evaluation, and application controls.
The Bottom Line
Bottom line: CRN’s 2024 AI 100 data-and-analytics list is valuable as a historical map of the AI data stack, not as a current leaderboard. Choose among these companies based on workload, data architecture, governance requirements, deployment constraints, and existing tools—and independently recheck product status, pricing, integrations, support, and company fundamentals before acting.
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