Hazelcast
Unified real-time data platform for instant action
About Hazelcast
Hazelcast is a unified real-time data platform that combines a distributed in-memory data store with a stream processing engine in a single runtime. It lets organizations cache, integrate, and process data with sub-millisecond latency, enabling real-time and AI-driven applications at scale. Available as the self-managed Hazelcast Platform (Community and Enterprise editions) and as the fully managed, pay-as-you-go Hazelcast Cloud service.
Pros & Cons
Pros
- Combines caching, stream processing, and compute in a single unified runtime, reducing architectural complexity
- Sub-millisecond latency with proven scale (billions of events per second in benchmarks)
- Free open-source Community Edition lowers the barrier to adoption
- Cloud-agnostic with managed and self-managed deployment options
- Strong adoption among major banks and enterprises for mission-critical workloads
- Broad language and connector support including Kafka and major cloud providers
Cons
- Enterprise Platform pricing is not public and requires contacting sales
- Steep learning curve for teams new to distributed in-memory systems
- Advanced features like WAN replication and the security suite are gated behind the paid Enterprise edition
- In-memory architecture can be memory-intensive and costly at large data volumes
- Primarily aimed at developers and architects rather than non-technical users
Key Features
Distributed In-Memory Data Store
High-density, sub-millisecond caching and data storage spread across a cluster for ultra-fast access and horizontal scale.
Stream Processing Engine
Built-in engine (Jet) for real-time event processing, enrichment, and analytics on data in motion before it lands in a database or data lake.
Distributed Compute
Parallel in-memory computation across cluster nodes for high-throughput, low-latency processing workloads.
CP Subsystem & Flexible Consistency
Choose between AP (availability) and CP (strong consistency) modes, with Raft-based primitives for consistent distributed coordination.
WAN / Geo Replication
Replicate data across data centers and regions for high availability and disaster recovery (Enterprise).
Real-Time AI / ML Inference
Deploy and run machine learning models inline with streaming data for fast, low-latency predictions in event-driven and edge use cases.
Vector Search
Native vector search support for similarity and retrieval workloads alongside the in-memory data store.
Multi-Language Clients
Official clients for Java, .NET, C++, Node.js, Python, and Go, plus connectors for Apache Kafka, AWS, Azure, and GCP.
Cloud-Native & Kubernetes Ready
Cloud-agnostic deployment with full Kubernetes support and availability on AWS, GCP, and Azure marketplaces.
Security Suite
Enterprise security including TLS, authentication, authorization, and auditing for mission-critical production workloads.
Pricing
- Open-source under Apache 2.0 + Hazelcast Community License
- Core in-memory data store and stream processing engine
- Management Center (up to 3 cluster members)
- Multi-language clients and connectors
- Self-managed download
- Fully managed, serverless, auto-scaling clusters
- Free trial available
- Pay only for resources consumed (storage-based billing)
- Runs on AWS, GCP, and Azure
- Pre-paid credits option available
- Everything in Community Edition
- Security Suite, WAN Replication, and automatic disaster recovery failover
- Blue-green and zero-downtime deployments
- High-Density Memory Store and advanced RAS features
- Professional support (annual subscription, contact sales)
Best For
Real-Time Payment & Fraud Detection
Process and enrich transaction streams in milliseconds to detect fraud and authorize payments as events occur.
Distributed Caching
Offload databases and accelerate applications with a sub-millisecond, horizontally scalable in-memory cache.
Streaming Analytics
Run continuous analytics and event-driven pipelines on data in motion before storing it in a database or data lake.
Real-Time AI / ML Serving
Serve machine learning models inline with live data streams for instant, low-latency predictions in event-driven and edge scenarios.
High-Availability Microservices
Provide a fast, resilient shared data layer with flexible consistency and geo-replication for distributed microservice architectures.
