GridGain
GridGain is an enterprise in-memory computing platform built on Apache Ignite, delivering millisecond performance, horizontal scale, and real-time AI/ML analytics for mission-critical applications.
Score
50
User Rating
1.0
Free/Trial Support
Supported
Starting Price
--
Last Updated
Oct 01, 2026
Overview
What is GridGain?
GridGain is an enterprise-grade in-memory computing platform built on the open-source Apache Ignite foundation by the team that created Ignite. It provides lightning-fast performance with millisecond query times, horizontal scale-out without downtime, and flexible data access via SQL, key-value, or compute APIs. GridGain adds enterprise reliability features like failover, encryption, RBAC, 24/7 SLA-backed support, real-time analytics, and data center replication, enabling applications that requi
What is GridGain used for?
Top Features
- In-memory computing with millisecond query times
- Horizontal scale-out without downtime
- Flexible data access: SQL, key-value, and compute APIs
- Enterprise reliability with failover, encryption, and RBAC
- 24/7 SLA-backed expert support
- Real-time analytics and AI/ML on live data
- GridGain Control Center for cluster monitoring
- Data center replication with active-active failover
How to use GridGain?
Start with a free trial or the quick start guide to add enterprise capabilities to your Ignite cluster. Deploy GridGain in your environment (cloud, on-premise, or hybrid), then use GridGain Control Center to monitor, troubleshoot, and optimize clusters in real time. Leverage SQL, key-value, or compute APIs for data access, and enable enterprise security features like RBAC and encryption. Free training courses taught by the engineers who created Apache Ignite are available to get your team up to
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Pros & Cons
Pros
- Built by the original creators of Apache Ignite
- Enterprise-grade security and support
- High performance with low latency
- Scalable without downtime
- Strong for real-time AI/ML workloads
- Comprehensive monitoring and management tools
Cons
- Enterprise pricing may be costly for small teams
- Requires technical expertise to deploy and manage
- Primarily targeted at large enterprises
- Dependent on Apache Ignite ecosystem
Use Cases
- Real-time risk management with streaming data
- Smart decisioning using AI/ML models and business rules
- Low-latency data hub consolidating systems of record
- Accelerating AI/ML workloads with feature stores and vector search
- Cross-asset risk and analytics in capital markets
- Real-time data hub for airlines with single-digit ms latency
GridGain Pricing
Free PlanSubscription Plan
No detailed pricing information available
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