Hazelcast vs Apache Ignite vs Infinispan: Self-Hosted In-Memory Data Grid Guide 2026

When your application’s database becomes the bottleneck, adding more disk I/O or query optimization won’t solve the fundamental problem: disk is simply too slow for sub-millisecond data access. In-memory data grids (IMDGs) solve this by keeping your working dataset in RAM across a cluster of nodes, delivering microsecond read latencies and massive horizontal scalability.

This guide compares the three leading open-source IMDGs — Hazelcast, Apache Ignite, and Infinispan — with real deployment configs, feature comparisons, and decision criteria to help you pick the right platform for your self-hosted infrastructure.

For teams managing distributed systems, pairing an IMDG with the right service discovery layer and event streaming backbone creates a complete real-time data architecture.

Why Self-Host an In-Memory Data Grid

Cloud-managed alternatives like Redis Enterprise or AWS MemoryDB come with significant trade-offs: vendor lock-in, unpredictable pricing at scale, and data residency constraints. Self-hosting an IMDG gives you:

• Full data sovereignty — your data never leaves your infrastructure, critical for regulated industries (finance, healthcare, government)
• Predictable costs — no per-GB or per-operation charges; your only cost is the hardware you provision
• Custom configurations — tune eviction policies, persistence strategies, and network topologies to your exact workload
• No vendor lock-in — all three platforms are open-source (Apache 2.0 or equivalent) with active communities
• On-premise deployment — essential for air-gapped environments and edge computing scenarios

Project Overview and GitHub Stats

Hazelcast: Real-Time Data Platform

Hazelcast is the most feature-complete IMDG in terms of data structures. Beyond simple key-value caching, it offers distributed maps, queues, sets, semaphores, topics, and atomic numbers — all accessible from a unified API. The merger of Hazelcast Jet (stream processing) into the core platform means you can process data in motion while simultaneously serving low-latency queries.

Key Strengths

• Rich distributed data structures — 20+ structures including MultiMap, ReplicatedMap, Ringbuffer, PNCounter
• Built-in stream processing — process incoming data streams with fault-tolerant pipelines without a separate engine
• Near Cache support — local caching on application nodes for read-heavy workloads, reducing cluster hops
• Wan Replication — cross-datacenter replication for active-active deployments
• Hot Restart — persistent store that allows full cluster restart with data recovery in minutes
• Python and C# clients — among the widest client language support of any IMDG

Docker Deployment

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# docker-compose.yml — Hazelcast single-node with Management Center
services:
  hazelcast:
    image: hazelcast/hazelcast:5.5.0
    container_name: hazelcast
    ports:
      - "5701:5701"
    environment:
      - HZ_CLUSTERNAME=prod-cluster
      - HZ_NETWORK_PUBLICADDRESS=localhost:5701
      - HZ_JET_ENABLED=true
    deploy:
      resources:
        limits:
          memory: 2G
    networks:
      - imdg-net

  hazelcast-mc:
    image: hazelcast/management-center:5.5.0
    container_name: hazelcast-mc
    ports:
      - "8080:8080"
    environment:
      - MC_DEFAULT_CLUSTER=prod-cluster
      - MC_DEFAULT_CLUSTER_MEMBER=hazelcast:5701
    depends_on:
      - hazelcast
    networks:
      - imdg-net

networks:
  imdg-net:
    driver: bridge

Java Client Example

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import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.map.IMap;

HazelcastInstance hz = Hazelcast.newHazelcastInstance();

// Distributed map — data partitioned across cluster
IMap<String, User> users = hz.getMap("users");
users.put("user-1", new User("Alice", "alice@example.com"));
users.put("user-2", new User("Bob", "bob@example.com"));

// Distributed queue for task coordination
IQueue<String> taskQueue = hz.getQueue("tasks");
taskQueue.offer("process-order-42");

// Near Cache for read-heavy access patterns
NearCacheConfig nearCacheConfig = new NearCacheConfig();
nearCacheConfig.setName("session-cache");
nearCacheConfig.setInvalidateOnChange(true);
nearCacheConfig.setEvictionConfig(
    new EvictionConfig()
        .setMaximumSizePolicy(EvictionConfig.MaxSizePolicy.ENTRY_COUNT)
        .setSize(10000)
);

Apache Ignite: Distributed Database with Compute

Apache Ignite positions itself as a “distributed database for high-performance computing.” Unlike traditional IMDGs that focus primarily on caching, Ignite provides a full in-memory SQL database with distributed joins, ACID transactions, and the ability to colocate computation with data — meaning you can ship compute tasks to the node where the data lives, eliminating network overhead.

Key Strengths

• Distributed SQL with joins — full SQL engine supporting distributed joins across partitions, a capability most IMDGs lack
• Compute colocation — IgniteCompute.execute() routes code to the data-bearing node, avoiding data transfer
• Native persistence — durable memory architecture that persists data to disk while keeping hot data in RAM; survives full cluster restarts
• ML and indexing — built-in machine learning library and Lucene-based text search indexes
• Thin client mode — lightweight clients that don’t hold data, ideal for web application servers
• Spring and Hibernate integration — first-class Spring Boot starters and Hibernate cache adapter
• Schema-on-read and schema-on-write — flexible approach to data modeling

Docker Deployment

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# docker-compose.yml — Apache Ignite cluster (3 nodes)
services:
  ignite-1:
    image: apacheignite/ignite:2.16.0
    container_name: ignite-1
    ports:
      - "10800:10800"
      - "47100:47100"
      - "47500:47500"
    environment:
      - OPTION_LIBS=ignite-rest-http,ignite-kubernetes
      - CONFIG_URI=https://raw.githubusercontent.com/apache/ignite/master/examples/config/example-cache.xml
      - IGNITE_WORK_DIR=/storage
    volumes:
      - ignite-data-1:/storage
    deploy:
      resources:
        limits:
          memory: 2G

  ignite-2:
    image: apacheignite/ignite:2.16.0
    container_name: ignite-2
    environment:
      - IGNITE_INSTANCE_NAME=ignite-2
      - OPTION_LIBS=ignite-kubernetes
    depends_on:
      - ignite-1
    deploy:
      resources:
        limits:
          memory: 2G

  ignite-3:
    image: apacheignite/ignite:2.16.0
    container_name: ignite-3
    environment:
      - IGNITE_INSTANCE_NAME=ignite-3
      - OPTION_LIBS=ignite-kubernetes
    depends_on:
      - ignite-1
    deploy:
      resources:
        limits:
          memory: 2G

volumes:
  ignite-data-1:

Java Client with Distributed SQL

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import org.apache.ignite.Ignite;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.QueryEntity;
import org.apache.ignite.cache.QueryIndex;

Ignite ignite = Ignition.start("default-config.xml");

// Create cache with SQL schema
CacheConfiguration<Long, Person> ccfg = new CacheConfiguration<>("PersonCache");
ccfg.setIndexedTypes(Long.class, Person.class);
IgniteCache<Long, Person> cache = ignite.getOrCreateCache(ccfg);

// Insert data
cache.put(1L, new Person("Alice", 30, "Engineering"));
cache.put(2L, new Person("Bob", 25, "Engineering"));
cache.put(3L, new Person("Charlie", 35, "Sales"));

// Distributed SQL query with join
SqlFieldsQuery sql = new SqlFieldsQuery(
    "SELECT p.name, p.age FROM Person p WHERE p.department = ? ORDER BY p.age"
);
sql.setArgs("Engineering");

try (QueryCursor<List<?>> cursor = cache.query(sql)) {
    for (List<?> row : cursor) {
        System.out.println(row);
    }
}

// Compute colocation — ship computation to data node
IgniteCompute compute = ignite.compute();
long avgAge = compute.call(
    () -> {
        Ignite localIgnite = Ignition.localIgnite();
        // This code runs on the data-bearing node
        IgniteCache<Long, Person> localCache = localIgnite.cache("PersonCache");
        return localCache.values().stream()
            .mapToInt(Person::getAge)
            .average()
            .orElse(0);
    }
);

Infinispan: Java Ecosystem Data Grid

Infinispan originated as the JBoss caching layer and has evolved into a full-featured data grid with deep Java ecosystem integration. If your stack runs on Quarkus, WildFly, or Spring Boot with Hibernate, Infinispan offers the tightest integration of any IMDG — including the ability to serve as a second-level Hibernate cache out of the box.

Key Strengths

• Hibernate second-level cache — replace your ORM cache layer with a distributed Infinispan cache for seamless scaling
• Quarkus native integration — first-class Quarkus extension with GraalVM native image support
• Hot Rod protocol — language-agnostic binary protocol with smart routing (client knows partition topology, minimizing hops)
• Cross-site replication — built-in support for active-active multi-datacenter topologies with conflict resolution
• Server mode — standalone Infinispan Server with REST and Memcached-compatible endpoints, usable without Java clients
• Counter and lock services — distributed atomic counters and non-blocking lock primitives
• Embedded vs Client-Server — run Infinispan embedded in your JVM or as a separate server cluster

Docker Deployment

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# docker-compose.yml — Infinispan Server cluster
services:
  infinispan-1:
    image: infinispan/server:15.0.0
    container_name: infinispan-1
    ports:
      - "11222:11222"
    environment:
      - USER=admin
      - PASS=changeme123
      - JAVA_OPTS=-Djgroups.dns.query=infinispan-1
    volumes:
      - infinispan-data-1:/opt/infinispan/server/data
    deploy:
      resources:
        limits:
          memory: 1G

  infinispan-2:
    image: infinispan/server:15.0.0
    container_name: infinispan-2
    ports:
      - "11223:11222"
    environment:
      - USER=admin
      - PASS=changeme123
      - JAVA_OPTS=-Djgroups.dns.query=infinispan-1
    depends_on:
      - infinispan-1
    deploy:
      resources:
        limits:
          memory: 1G

volumes:
  infinispan-data-1:

Java Client with Hot Rod

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import org.infinispan.client.hotrod.RemoteCacheManager;
import org.infinispan.client.hotrod.configuration.ConfigurationBuilder;
import org.infinispan.commons.api.BasicCache;

ConfigurationBuilder builder = new ConfigurationBuilder();
builder.addServer()
    .host("localhost")
    .port(11222)
    .security()
    .authentication()
    .username("admin")
    .password("changeme123")
    .realm("default")
    .saslMechanism("DIGEST-MD5");

try (RemoteCacheManager manager = new RemoteCacheManager(builder.build())) {
    BasicCache<String, String> cache = manager.getCache("default");
    cache.put("session:user-1", "{\"role\":\"admin\",\"lastSeen\":1713744000}");
    cache.put("session:user-2", "{\"role\":\"viewer\",\"lastSeen\":1713744060}");

    // Retrieve with Hot Rod smart routing — single hop to correct node
    String session = cache.get("session:user-1");
    System.out.println(session);
}

REST API Usage

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# Create a cache via REST
curl -X POST http://localhost:11222/rest/v2/caches/orders \
  -u admin:changeme123 \
  -H "Content-Type: application/json" \
  -d '{
    "distributed-cache": {
      "owners": 2,
      "mode": "SYNC",
      "encoding": {
        "key": { "media-type": "application/json" },
        "value": { "media-type": "application/json" }
      }
    }
  }'

# Put and get data
curl -X PUT http://localhost:11222/rest/v2/caches/orders/order-42 \
  -u admin:changeme123 \
  -H "Content-Type: application/json" \
  -d '{"id":42,"customer":"Alice","total":199.99,"status":"pending"}'

curl http://localhost:11222/rest/v2/caches/orders/order-42 \
  -u admin:changeme123

Performance and Architecture Comparison

Read/Write Throughput

Based on community benchmarks and published test results across comparable hardware (8-core, 32GB RAM, 10GbE network):

When to Choose Each Platform

Choose Hazelcast when:

• You need stream processing alongside caching (Jet pipeline)
• Your application uses diverse distributed data structures (queues, topics, semaphores)
• You require Python or C# client SDKs with full feature parity
• Cross-datacenter active-active replication (Wan Replication) is required
• You want the simplest cluster discovery (TCP multicast works out of the box)

Choose Apache Ignite when:

• You need distributed SQL with complex joins across partitions
• Compute colocation is important — shipping computation to data nodes
• ACID transactions across cache entries are required
• You want built-in persistence that survives full cluster restarts
• Your team already uses Apache Spark and wants a complementary in-memory layer

Choose Infinispan when:

• Your stack is heavily invested in the Java ecosystem (Quarkus, WildFly, Hibernate)
• You need a drop-in Hibernate second-level cache replacement
• REST or Memcached protocol compatibility is required for non-Java clients
• You want GraalVM native image support for fast cold starts
• Your infrastructure runs on Red Hat / OpenShift and you want supported enterprise options

Kubernetes Deployment

All three platforms offer native Kubernetes operators for production deployments:

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# Hazelcast Kubernetes CRD (using Helm)
apiVersion: hazelcast.com/v1alpha1
kind: Hazelcast
metadata:
  name: hazelcast-prod
  namespace: default
spec:
  clusterSize: 3
  repository: "hazelcast/hazelcast"
  version: "5.5.0"
  persistence:
    enabled: true
    size: 10Gi
  resources:
    limits:
      memory: 4Gi
      cpu: "2"

---
# Apache Ignite Kubernetes (using operator)
apiVersion: "ignite.apache.org/v1alpha1"
kind: Ignite
metadata:
  name: ignite-cluster
spec:
  nodes:
    - name: server
      replicas: 3
      resources:
        limits:
          memory: 4Gi
          cpu: "2"
      storage:
        volumes:
          - name: ignite-storage
            claimTemplate:
              accessModes: ["ReadWriteOnce"]
              resources:
                requests:
                  storage: 20Gi

---
# Infinispan Kubernetes (using Infinispan Operator)
apiVersion: infinispan.org/v2alpha1
kind: Infinispan
metadata:
  name: infinispan-prod
spec:
  replicas: 3
  container:
    image: infinispan/server:15.0.0
  service:
    type: DataGrid
  security:
    endpointAuthentication: true
    endpointSecretName: infinispan-secret
  container:
    resources:
      requests:
        memory: 2Gi
        cpu: "1"
      limits:
        memory: 4Gi
        cpu: "2"

Monitoring and Observability

All three platforms expose JMX metrics that integrate with standard monitoring stacks:

• Hazelcast — JMX metrics + Prometheus exporter (hazelcast-metrics), Grafana dashboards available
• Apache Ignite — JMX metrics, Ignite Persistent Store metrics, Prometheus integration via ignite-metrics-exporter
• Infinispan — JMX metrics, Micrometer support, native Prometheus endpoints on /metrics

For comprehensive monitoring, combine IMDG metrics with your existing VictoriaMetrics, Thanos, or Cortex setup to track cache hit rates, partition distribution, and cluster health alongside your other infrastructure metrics.

Frequently Asked Questions

What is the difference between an IMDG and a traditional cache like Redis?

An in-memory data grid goes beyond simple key-value caching. While Redis is primarily a data structure server with caching use cases, IMDGs like Hazelcast, Ignite, and Infinispan provide distributed computing capabilities — including distributed SQL queries, compute colocation, stream processing, and complex distributed data structures (locks, semaphores, atomic counters) across a cluster. IMDGs also typically offer stronger consistency guarantees and more flexible partitioning strategies.

Can I use an IMDG as my primary database?

Apache Ignite is designed for this use case with its native persistence layer and ACID transaction support. You can use Ignite as a primary database with in-memory performance while data is simultaneously persisted to disk. Hazelcast and Infinispan are better suited as caching layers alongside a traditional database, though Hazelcast’s Hot Restart and Infinispan’s persistent cache stores provide varying degrees of durability.

How do these platforms handle data partitioning and rebalancing?

All three use consistent hashing to distribute data across cluster nodes. When a node joins or leaves, data is automatically rebalanced. Hazelcast and Infinispan allow you to configure backup count (number of synchronous replicas), while Ignite offers both partitioned and replicated cache modes. Rebalancing is typically non-blocking — the cluster continues serving requests during the process.

Which IMDG has the best Kubernetes support?

All three provide Kubernetes operators: Hazelcast Platform Operator, Apache Ignite Kubernetes Operator, and Infinispan Operator. Hazelcast’s operator is the most mature with auto-scaling and backup/restore features. Infinispan’s operator integrates well with OpenShift. Ignite’s operator supports stateful cluster management with persistent volumes. For a full Kubernetes infrastructure comparison, see our container management platforms guide.

Can I migrate from Redis to an IMDG?

Partial migration is possible. Both Hazelcast and Infinispan support the Redis protocol — Hazelcast via the hazelcast-enterprise Redis compatibility module and Infinispan via its Memcached/Redis-compatible Hot Rod endpoints. For a complete migration, you would need to rewrite your application’s data access layer to use the IMDG’s native client API. The effort varies by workload complexity.

What are the memory requirements for production deployments?

Minimum production recommendations: Hazelcast (2GB per node, 3 nodes), Apache Ignite (4GB per node, 3 nodes), Infinispan (1GB per node, 2 nodes). Actual requirements depend on your dataset size, backup count, and whether you use off-heap storage. Ignite’s native persistence is particularly memory-efficient since only hot data needs to be in RAM. Plan for 2-3x your working dataset size in total cluster memory (accounting for backups and JVM overhead).