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CRN’s 2025 Big Data 100 names 20 database-system companies, but it is an editorial list—not a ranked comparison, benchmark, or endorsement. Its vendors address very different needs, from distributed transactions and document storage to real-time analytics, graph analysis, time-series data, and AI retrieval. The useful question is not which company is “coolest,” but which architecture fits the workload.
What CRN’s 2025 list means
CRN presented the database-system companies as Part 2 of its 2025 Big Data 100. “Coolest” is CRN’s editorial description. The article does not publish a scoring method or a numbered ranking, and inclusion does not establish that a company is best for every workload.
The 20 named companies are Aerospike, ClickHouse, Cockroach Labs, Couchbase, EDB, Exasol, Fluree, Imply Data, InfluxData, Kinetica, MariaDB, MongoDB, Neo4j, Pinecone, Redis, ScyllaDB, SingleStore, Tessell, TigerGraph, and Yugabyte. They do not all compete in the same market: the list includes database engines, managed services, specialized data platforms, and a multi-engine management platform.
CRN has since published a separate 2026 database-systems list. This article describes the 2025 selection and the developments CRN highlighted at that time; it is not a current ranking of vendors or a claim that every announced capability remains available in the same form.
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Why databases are part of the AI infrastructure conversation
Generative-AI applications need to retrieve relevant information quickly and combine it with the context required to answer a question. That has drawn attention to vector search, which finds items similar to an embedding, as well as to databases that can search across documents, relationships, and other structured information.
Vector search is not the only shift. Graph systems can represent relationships among people, accounts, products, or events; real-time analytical platforms can ingest and query streams of activity; and distributed SQL or NoSQL systems target applications that need availability across regions or high request volumes. Vendors are also combining database functions with managed cloud operations, search, analytics, and AI tooling.
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Those capabilities do not make one platform automatically suitable for every job. “AI-ready” is too broad to guide a purchase: vector indexing, filtering, hybrid keyword search, update behavior, scale, and operating cost can differ substantially. The same is true of claims about compatibility, global availability, or unified transactions and analytics.
The 20 companies, grouped by the problems they target
The groupings below are an editorial way to make CRN’s broad category easier to navigate. Some vendors fit more than one group; their products and positioning are described as CRN highlighted them in 2025.
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Distributed SQL and transactional platforms
- Cockroach Labs: CockroachDB targets distributed SQL transactions for applications requiring resilience and geographic distribution. CRN highlighted a strategic collaboration with AWS and company-reported growth. A distributed design can help with availability and locality, but cross-region writes, replication choices, and failure behavior affect latency and cost.
- EDB: EDB offers commercial PostgreSQL technology, enterprise support, and Oracle-compatibility capabilities. CRN highlighted EDB Postgres AI and expanded channel investment. It is relevant to PostgreSQL adoption and Oracle modernization, but compatibility should be tested against the actual application, extensions, queries, and operating procedures.
- Yugabyte: YugabyteDB is a distributed SQL database with PostgreSQL compatibility, aimed at transactional applications such as payments and order management. CRN highlighted YugabyteDB Aeon, a technology preview of YugabyteDB 2.25 with PostgreSQL 15 compatibility, and Performance Advisor for Aeon. A preview is not the same as a generally available product, and compatibility is not a substitute for workload testing.
- SingleStore: SingleStore positions its distributed database across transactions, analytics, search, and rapid ingestion, with support for relational, JSON, geospatial, key-value, vector, and time-series data. CRN noted the acquisition of BryteFlow and the launch of SingleStore Flow for migration and change-data-capture workflows. Consolidation may reduce the number of systems to operate, but test whether it meets the peak needs of each workload.
High-scale NoSQL and application databases
- Aerospike: Aerospike is a distributed NoSQL system for high-throughput, low-latency operational workloads. CRN highlighted Aerospike 8’s distributed ACID transaction capabilities and work on vector indexing and storage. It is a specialized option rather than a universal replacement for relational or analytical databases.
- Couchbase: Couchbase combines document-oriented database software in Couchbase Server with the managed Capella platform. CRN highlighted Capella AI Services, NVIDIA NIM integration, and Couchbase Edge Server, alongside mobile and edge use cases. Buyers should assess document-model discipline, consistency requirements, and the effort of moving from relational applications.
- MongoDB: MongoDB is a document database and cloud developer platform; CRN highlighted MongoDB Atlas, its AI Applications Program, and the acquisition of Voyage AI for embedding and reranking capabilities relevant to retrieval-augmented generation. Flexible documents can suit evolving application data, but they do not remove the need for deliberate schema design or careful analysis of joins and transactions.
- ScyllaDB: ScyllaDB is a distributed NoSQL database aimed at high-throughput, low-latency applications that need availability and scale. CRN highlighted ScyllaDB 2024.2 and its tablets replication architecture, which the company said improved elasticity and efficiency. Validate performance with realistic data and access patterns, and account for the operational expertise its architecture may require.
- Redis: Redis is an in-memory, real-time data platform used for caching, sessions, and low-latency application services. CRN described Redis for AI as combining vector capabilities, integrations, and scalability for applications such as chatbots and agents. Memory use, persistence, durability, and working-set size can determine whether it belongs in a retrieval layer or as a primary store.
Analytical and real-time databases
- ClickHouse: ClickHouse is a column-oriented analytical SQL database available as open-source software and ClickHouse Cloud. It targets OLAP, event analysis, observability, and data-warehouse workloads; CRN highlighted its acquisition of HyperDX to strengthen observability. Its strength in scans and aggregations does not by itself make it the right choice for conventional transaction-heavy applications.
- Exasol: Exasol is an in-memory, column-oriented analytical database for high-performance analytics and data warehousing. Its fit depends on query patterns, concurrency, data volume, infrastructure, and how it works with an organization’s existing warehouse and business-intelligence environment.
- Imply Data: Imply’s real-time analytics platform is based on Apache Druid and is aimed at event analysis, fast ingestion, interactive queries, and user-facing analytics. CRN highlighted Imply Polaris, a managed database service running on Microsoft Azure. Compare it with cloud warehouses, lakehouse engines, and other streaming analytics options for the workload at hand.
- Kinetica: Kinetica is a GPU-accelerated analytical database targeting real-time, spatial, graph, and vector workloads. CRN highlighted real-time vector search and a built-in large language model. GPU acceleration is useful only when the workload benefits enough to justify the infrastructure and operating economics.
Specialized databases for time-series, graph, and trusted data
- InfluxData: InfluxData’s InfluxDB targets timestamped data such as metrics, telemetry, IoT, monitoring, and industrial measurements. CRN highlighted InfluxDB 3 Core and InfluxDB 3 Enterprise, including a Python processing engine and production-oriented capabilities for availability, security, and scale. It is specialized for time-indexed workloads, not a general substitute for a relational system of record.
- Neo4j: Neo4j is a graph database for workloads such as fraud detection, identity resolution, recommendations, knowledge graphs, and supply-chain analysis. Graph structure can add relationship context to vector retrieval and support connected-data analysis; it is most compelling when traversing relationships is central to the application.
- TigerGraph: TigerGraph is a hybrid transactional and analytical graph database for connected-data analysis, customer analytics, fraud detection, and AI or machine-learning workloads. CRN highlighted Savanna, which the company described as a native-parallel-graph design for large connected-data workloads. Its specialization matters most where large-scale graph traversal is a core requirement.
- Fluree: Fluree combines a semantic graph database with immutable-ledger features for verifiable data, provenance, governance-sensitive applications, and trusted data sharing. CRN also highlighted Fluree Sense and Content Auto-Tagging Manager. The architecture may be valuable when integrity and provenance are central, but can be unnecessary for ordinary application storage.
Vector retrieval and database operations
- Pinecone: Pinecone is a purpose-built vector database for embeddings, similarity search, semantic search, and retrieval-augmented generation. CRN highlighted its partner program for independent software vendors embedding vector search in applications. Compare it with vector features already available in PostgreSQL, MongoDB, Redis, cloud databases, or warehouses; the presence of a vector feature alone does not establish equivalent retrieval quality or operating cost.
- Tessell: Tessell provides a multi-engine DBaaS and database-management platform for organizations running different database engines across cloud or multi-cloud environments. CRN listed support for Microsoft SQL Server, Milvus, MongoDB, MySQL, Oracle Database, and PostgreSQL, and reported a $60 million Series B funding round. A management layer may simplify operations, but it also adds a control plane, integration boundaries, and support considerations.
Which vendors to shortlist by workload
This map is a starting point based on the product descriptions above, not a performance ranking. Include existing systems and cloud-provider services in the comparison: a specialist vendor is not automatically preferable to a database the organization already operates well.
| Need | Companies to examine first | What to validate |
|---|---|---|
| Distributed transactional applications | Cockroach Labs, Yugabyte, Aerospike, ScyllaDB | Transaction scope, consistency, geographic latency, failure recovery, and operational skill requirements. |
| Document-oriented application development | MongoDB, Couchbase | Schema evolution, query patterns, transaction needs, migration effort, and edge or mobile requirements. |
| Real-time analytics and event analysis | ClickHouse, Imply Data, SingleStore, Kinetica | Ingestion rate, interactive query latency, concurrency, workload shape, and whether GPU acceleration or a unified system is justified. |
| Time-series and telemetry | InfluxData | Retention, write rates, query patterns, and integration with monitoring or industrial systems. |
| Graph and relationship analysis | Neo4j, TigerGraph, Fluree | Traversal patterns, graph size, provenance needs, and whether relationships are central rather than incidental. |
| Vector search and AI retrieval | Pinecone, Redis, MongoDB, ClickHouse, Kinetica | Recall, latency, filtering, hybrid search, update behavior, metadata, scale, and total cost against incumbent database capabilities. |
| PostgreSQL modernization | EDB, Yugabyte | Extensions, SQL behavior, drivers, transaction semantics, operational tools, and application-level compatibility. |
| MySQL/MariaDB ecosystem | MariaDB | Application compatibility, licensing, enterprise support, and migration requirements. |
| Multiple database engines under one operating model | Tessell | Supported engines and versions, integrations, control-plane dependency, and who handles support across layers. |
| Analytical warehousing | Exasol, ClickHouse, Kinetica | Representative query performance, concurrency, infrastructure economics, and fit with the current warehouse or lakehouse. |
| Reducing the number of systems | SingleStore, EDB Postgres AI, Yugabyte, Cockroach Labs | Whether consolidation satisfies each workload’s needs without sacrificing required specialization. |
How to evaluate a database before choosing it
Start with the application’s actual requirements rather than a feature checklist. The same product can be a good fit for one workload and a poor fit for another, even within the same organization.
- Workload: Identify whether the system is OLTP, OLAP, real-time streaming, graph, time-series, vector retrieval, or a combination. Record read/write ratios, peak concurrency, retention, and latency targets, including tail latency if it matters.
- Data model: Decide whether the application needs relational tables, documents, key-value records, graph relationships, time-series measurements, embeddings, or several models together.
- Transactions and consistency: Specify ACID scope, strong or eventual consistency, cross-region behavior, read-after-write guarantees, and conflict handling. Test the exact transaction patterns the application uses.
- Availability and recovery: Set recovery point and recovery time objectives. Test failover, backups and restoration, disaster recovery, online schema changes, and dependencies on particular regions or availability zones.
- AI retrieval: Measure retrieval quality and latency using representative data. Check vector indexes, metadata filtering, hybrid search, update frequency, reranking, provenance, and monitoring—not just whether a vendor advertises vector search.
- Deployment and operations: Compare managed service, self-managed software, cloud marketplace deployment, hybrid or on-premises support, Kubernetes needs, upgrades, monitoring, and the specialist skills required.
- Total cost and exit: Model compute, storage, memory, replication, backups, data transfer, support, migration, and professional services. Assess portability, export paths, and the cost of moving away; do not infer cost from a headline compute rate alone.
What to test in a proof of concept
A useful proof of concept should reproduce the production workload closely enough to expose trade-offs. A demo dataset or a single happy-path query is not enough to establish suitability.
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- Load representative data: Use realistic volume, record shape, indexes, retention, and data skew.
- Replay real access patterns: Test the production read and write mix, peak concurrency, batch loads, and the most important queries.
- Measure under pressure: Record latency distributions and throughput at expected load, then test higher load and sustained operation. Treat vendor performance figures as claims unless independently reproduced under comparable conditions.
- Exercise failure and recovery: Trigger failover where possible, restore a backup, and test the recovery objectives the application requires. For distributed systems, include realistic network conditions and cross-region behavior.
- Test change: Evolve the schema, update indexes, and check whether migrations or maintenance interrupt service.
- For retrieval workloads, evaluate retrieval: Use a representative question set and ground truth. Measure recall and response latency with the filters, updates, and hybrid search the application will actually use.
- Model operating cost: Include compute, storage, memory, replication, backup, and transfer under sustained load, as well as support and engineering effort.
- Check visibility and exit: Confirm that operators can diagnose slow queries and failures, and verify how data, indexes, and application behavior can be exported or migrated.
Trade-offs the shortlist should not hide
- Distributed does not mean latency-free: Cross-region writes, data locality, replication, and failure conditions can change latency and cost. Test them rather than relying on a scalability label.
- PostgreSQL or MySQL compatibility is not identity: Validate extensions, query plans, drivers, transaction behavior, and operational workflows against the specific application.
- Managed does not mean frictionless: A DBaaS can reduce infrastructure work while adding service-region limits, restricted tuning, provider-specific recovery, egress costs, or migration constraints.
- One platform may not be best at every job: Unified transaction-and-analytics or multimodel products can reduce sprawl, but a specialist engine may better suit a demanding workload.
- Memory and GPU have economic consequences: In-memory and GPU-accelerated systems can be compelling for suitable access patterns, but the working set, hardware, persistence, and replication shape cost.
- Open source and commercial editions can differ: Community software, enterprise editions, managed services, and support contracts may vary in security, availability features, tooling, and licensing. Compare the exact edition proposed.
- Existing platforms are valid alternatives: PostgreSQL, MySQL-compatible services, cloud-provider databases, warehouses, lakehouses, and existing enterprise licenses may meet the need with less migration or operational change.
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.
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