Hazelcast offers a unified in‑memory data platform that combines distributed caching, stream processing, and compute into a single runtime, delivering sub‑millisecond data access and high‑throughput event handling for real‑time applications. It supports flexible consistency models, geo‑WAN replication, and cloud‑native deployment with full Kubernetes integration, available as self‑managed software or a managed cloud service.
Funding
$28.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
5OEVFounders
Product
Problem
Enterprises building real‑time, data‑intensive applications often rely on multiple separate systems for caching, stream processing, and data storage. Managing these disparate components increases architectural complexity, latency, and operational overhead, while limiting scalability and consistent performance across cloud and on‑premise environments.
Solution
Hazelcast provides a unified in‑memory data platform that combines distributed caching, stream processing, and compute into a single runtime. The platform delivers sub‑millisecond data access and high‑throughput event processing, enabling applications to react instantly to data in motion. It supports flexible consistency models (CP for strong consistency or AP for high availability) and offers geo‑WAN replication for disaster recovery and multi‑cloud deployments. Built with cloud‑native architecture and full Kubernetes support, Hazelcast can be self‑managed on any public or private cloud or consumed as a managed service. Integrated security features, high‑density off‑heap memory, and a thread‑per‑core execution model ensure predictable latency and resilience at scale.
Target Audience
Primary customers are software engineering teams building low‑latency, real‑time applications such as financial trading platforms, online gaming, IoT analytics, and AI‑driven services, as well as enterprises seeking a single platform to replace separate caching, streaming, and compute layers.
Features
- Distributed in‑memory cache with sub‑millisecond read/write latency
- Integrated stream processing engine for real‑time event handling and instant action
- Flexible consistency options (CP subsystem for strong consistency, AP for availability)
- Geo‑WAN replication and automatic disaster‑recovery failover for multi‑cloud and hybrid deployments
- High‑density off‑heap memory store that eliminates Java garbage‑collection pauses
- Full Kubernetes orchestration and cloud‑agnostic deployment on AWS, GCP, Azure, or on‑premise
- Built‑in security suite with TLS encryption and JAAS role‑based access control
- Vector search and embedding capabilities via JVector 2.0 for AI/ML workloads