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Reviews on G2 Capterra
15 reviews tracked

The Bottom Line

Price
Free plan available, paid tiers above. Compare plans

Key facts

  • Hazelcast is a unified real-time data platform that combines a distributed compute engine and a fast data store for high-performance, scalable, and cloud-native applications.
  • It offers capabilities like distributed caching, in-memory computing, stream processing, and geo/WAN replication for real-time data access, event response, and high availability.
  • The platform includes a Management Center for centralized monitoring, diagnostics, and administration of Hazelcast clusters, supporting various integrations and security features.

Pros

  • Ultra-low latency
  • Unified platform
  • Strong Java ecosystem
  • Auto-scaling

Cons

  • Complex configuration
  • Resource intensive
  • Steep learning curve

What is Hazelcast?

Hazelcast is a real-time data platform combining a stream processing engine with a fast data store. It enables low-latency queries and analytics on streaming and stored data.

Available on: Web

Ratings Across the Web

4.5 (15 reviews)

Hazelcast holds an aggregate rating of 4.5 out of 5 from 15 reviews across G2 and Capterra, last checked September 17, 2026.

Ratings aggregated from independent review platforms. Learn more

Key Features

  • In-memory computing
  • Stream processing
  • Distributed caching
  • SQL over streams
  • Kubernetes native
  • Tiered storage

Pricing Plans

Pricing checked Oct 7, 2026

Hazelcast plans and prices, checked October 2026
PlanPriceDetails
Platform (OSS)

$0

  • Open source
  • Community support
Platform Enterprise

Custom

  • Enterprise features
  • Support
Cloud Serverless

Pay as you go

  • Managed service

Is Hazelcast worth the price?

Good value

Hazelcast's pricing model is fair, offering a robust open-source option at $0 and flexible enterprise solutions.

The 'Pay as you go' Cloud Serverless tier provides cost-effective scalability for managed services. This structure is best for organizations ranging from startups leveraging open source to large enterprises requiring custom, supported solutions.

Full Hazelcast pricing: every plan, hidden costs and alternatives

Reviews

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4.5/5

Across 15 verified user reviews on G2, Capterra

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Hazelcast FAQ

How does Hazelcast support real-time analytics?

Hazelcast supports real-time analytics by combining a stream processing engine with a fast data store. This unified platform enables low-latency queries and analysis directly on both streaming and stored data. Its architecture is designed for immediate data insights.

Which teams would benefit most from using Hazelcast?

Teams requiring ultra-low latency data processing and real-time analytics would benefit most from Hazelcast. It is particularly well-suited for developers and data engineers building applications that need to react instantly to streaming data. Its strong Java ecosystem also makes it a good fit for Java-centric development environments.

How is Hazelcast priced?

Hazelcast includes a free tier for basic usage. For organizations requiring more extensive features or higher usage limits, paid plans are available. These paid plans offer additional capabilities beyond the free tier.

What kind of trade-offs should users consider when adopting Hazelcast?

Users adopting Hazelcast should be aware of its complex configuration and steep learning curve. Additionally, it can be resource intensive, which may require careful planning for infrastructure. These aspects are important considerations for deployment and management.

How does Hazelcast compare to Memcached?

Hazelcast differs from Memcached by offering a unified real-time data platform that combines stream processing with a fast data store. While Memcached primarily focuses on caching, Hazelcast provides capabilities for low-latency queries and analytics on both streaming and stored data. It also boasts auto-scaling capabilities not typically found in Memcached.

Can Hazelcast automatically scale to handle increased data loads?

Yes, Hazelcast is designed with auto-scaling capabilities. This feature allows the platform to adjust its resources dynamically in response to varying data loads. It helps maintain performance and availability even as data volume fluctuates.

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