1
Aerospike
Mountain View, California, United States
Provides database server so companies can compile and analyze large amounts of data
- Revenue
- $42.8M
- Customers
- -
- Year founded
- 2009
- Funding
- -
- Team size
- 287
- Growth
- -
Top 13 Key Value Databases SaaS Companies in May 2026
As of May 2026, there are 13 SaaS companies in Key Value Databases. They have combined revenues of $1.1B and employ 7.4K people. They have raised $811.2M and serve 500 customers combined.
Key-value databases are a type of non-relational database, commonly categorized under NoSQL databases, that store data as a collection of key-value pairs. This structure provides a simple and efficient way to access and manipulate data, making these databases particularly suited for scenarios requiring high-speed transactions and scalability. They are widely used in applications such as caching, session storage, and real-time analytics, where quick retrieval and storage of data is paramount. Typical features of key-value databases include high availability, partitioning, and various data expiration policies. They often support horizontal scaling, allowing them to handle increased loads seamlessly. Common buyer personas for key-value databases include software developers and data engineers, who seek to implement flexible and scalable data solutions, as well as IT operations teams focused on maintaining performance and availability in dynamic environments.
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Top Key Value Databases Companies
Showing 10 of 3 companies ranked by annual revenue.
2
GigaSpaces
Herziliyah, Central, Israel
GigaSpaces helps enterprises apply AI to real-time, structured operational data — enabling spontaneous, natural language conversations that accelerate decision-making, improve agility in unexpected situations, and surface opportunities and risks in everyday business. With over two decades of experience powering mission-critical systems, GigaSpaces is a pioneer in real-time data technology and a trusted foundation for data-driven services across industries, including finance, travel, telecom, and insurance. Headquartered in the US, with offices in Europe and Israel, GigaSpaces helps organizations turn operational data into decisions that are timely, secure, and scalable.
- Revenue
- $13.8M
- Customers
- -
- Year founded
- 2000
- Funding
- $46M
- Team size
- 125
- Growth
- 35.13%
3
Arango
San Francisco, California, United States
Arango provides a trusted data foundation for Contextual AI — transforming enterprise data into a System of Context that truly represents the business, so LLMs can deliver better outcomes with unlimited scale and cost efficiency. The Arango AI Data Platform gives developers a single, integrated environment to build and scale AI-powered applications without the complexity of stitching together multiple databases and tools. At its core is a massively scalable multi-model database that unifies graph, vector, document, and key-value data with full-text, geospatial, and vector search — creating the System of Context, the bridge between enterprise data and LLMs. The Arango AI Suite includes automated data pipelines, multimodal data ingestion, AIOps and MLOps, LLM integrations, Graph Analytics, agentic frameworks for context-aware Hybrid/GraphRAG, GraphML, natural-language support, and GPU acceleration — enabling repeatable ROI and faster innovation. Trusted by NVIDIA, HPE, the London Stock Exchange, the U.S. Air Force, NIH, and Articul8, Arango powers enterprise AI with context, confidence, and scale. We are a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Learn more at arango.ai, LinkedIn, YouTube, and G2.
- Revenue
- $10.7M
- Customers
- -
- Year founded
- 2015
- Funding
- -
- Team size
- 97
- Growth
- -
Inclusion Criteria
- The product must store data primarily in key-value pairs. - It should support high availability and scalability for large datasets. - The database should allow for quick retrieval and storage of data. - It must include features for data expiration or time-to-live settings. - The system should not be limited to relational data structures; must also support unstructured or semi-structured data.