8 Best Redis Alternatives: Open Source and Fully Managed Databases

What Is Redis and What Are Common Alternatives?

Redis is an in-memory data structure store often used as a database, cache, or message broker. It was initially open-source, transitioned to a dual-license model with SSPLv1 and RSALv2, and in 2025 added back an open-source license (AGPLv3). It supports various data structures such as strings, hashes, lists, sets, sorted sets, bitmaps, and hyperloglogs.

Redis operates with high performance due to its memory-centric architecture and provides built-in replication, Lua scripting, eviction policies, transactions, and different levels of on-disk persistence.

Due to the lack of clarity about Redis’s open-source status and various limitations related to its single-threaded nature, persistence, and cluster management, many developers and organizations have sought alternatives.

Here are some common Redis alternatives:

Valkey: This open-source fork of Redis (originating from Redis version 7.2.4) emerged as a direct response to recent changes in Redis’s licensing model. Backed by the Linux Foundation and major tech players, Valkey aims to maintain open-source values and compatibility while enhancing features like multi-threaded I/O handling.
Memgraph: A real-time, in-memory graph database that excels where Redis becomes overly complex due to connected data and relationship traversal needs. Memgraph provides sub-millisecond query performance while enabling advanced graph queries beyond simple key-value caching.
Memcached: One of the oldest and most widely used Redis alternatives, Memcached is a lightweight, high-performance distributed memory caching system. It is suitable for simple key-value caching and offers robust speed and efficiency.
Apache Ignite: A distributed, in-memory computing platform that merges the capabilities of a high-performance key-value store with transactional and analytical processing. It supports ACID-compliant transactions and ANSI SQL queries across structured and semi-structured data.

And here are managed services organizations commonly used instead of open-source Redis:

Dragonfly Cloud: The managed version of the Dragonfly in-memory data store, optimized for low-latency, high-throughput workloads in modern cloud environments. It provides Redis-compatible APIs and is designed to deliver much higher performance for the most demanding workloads with less resource consumption compared to traditional Redis services.
Amazon DynamoDB: A managed, serverless NoSQL database service, DynamoDB provides high availability, scalability, and tunable read consistency. It is well-suited for applications requiring high-speed data access and fits well within the AWS ecosystem.
Amazon ElastiCache: A managed, in-memory caching service that supports Redis, Valkey, and Memcached. It offloads the complexity of deploying and managing caching infrastructure, providing automatic scaling, replication, backup, and failover.
Google Cloud Memorystore: An in-memory data store that supports Redis, Valkey, and Memcached. It automates provisioning, patching, failover, and scaling, making it easy to deploy resilient caching layers for applications. Designed for low-latency access and high availability, it integrates natively with Google Cloud’s monitoring and security stack.

This is part of an extensive series of guides about DevOps.

Why Look for Redis Alternatives?

License and Open Source Concerns

Starting with version 7.4, Redis transitioned from the permissive BSD-3-Clause license to a dual-license model using the Redis Source Available License v2 (RSALv2) and the Server Side Public License v1 (SSPLv1).

While the community edition remains accessible for general use, these new licenses impose significant restrictions on managed service providers. This also raises uncertainty about the future open-source status of the Redis project.

Providers such as AWS and Google Cloud, which offer Redis as a hosted service, must now enter commercial agreements to remain compliant or offer alternative solutions like Valkey.

Performance and Scalability Concerns

Redis was designed for single-threaded performance, which helps keep latency low but limits its ability to scale vertically with CPU-bound workloads. As a result, it cannot fully utilize modern, multi-core CPUs.

To address this, Redis introduced clustering to support horizontal scaling. However, this approach comes with its own limitations. For instance, multi-key operations are only permitted if all keys reside on the same hash slot, which can complicate data modeling and application logic. Furthermore, achieving full utilization of a multi-core server often requires setting up a cluster in the first place, adding operational complexity even on a single machine.

Data Durability and Persistence

By default, Redis holds all data in memory, which makes it fast but also exposes it to data loss in the event of system failure. Redis supports two persistence mechanisms: snapshotting and append-only files (AOF). Snapshotting creates periodic memory dumps, while AOF logs each write operation. There is also a hybrid mode, which combines RDB snapshots with AOF logging to balance durability and performance.

However, both methods involve trade-offs between performance, durability, and recovery time. Some workloads require stronger guarantees than Redis provides, especially in systems where data durability is critical. In such cases, users often look for alternatives that offer more robust on-disk storage and built-in consistency mechanisms and use Redis for ephemeral data as a caching layer.

Notable Open Source Redis Alternatives

In light of these limitations, here are some popular open source alternatives to Redis.

1. Valkey

Valkey is a fork of Redis. It is an open-source, in-memory data store used as a database, cache, message broker, and streaming engine. It supports a range of data structures and offers both ephemeral and persistent storage options. Valkey is built for low-latency operations, featuring replication, clustering, automatic failover, and support for atomic commands.

Key Features & Considerations of Valkey:

In-memory data store: Optimized for speed with all data held in memory.
Rich data structures: Supports strings, hashes, lists, sets, sorted sets, streams, bitmaps, hyperloglogs, geospatial indexes, and others.
Atomic operations: Enables single-command modifications like list pushes, string appends, and set unions without race conditions.
Persistence options: Offers RDB snapshots and append-only file (AOF) logging, with the option to disable persistence.
Replication and clustering: Includes asynchronous replication, Valkey Sentinel for high availability, and Valkey Cluster for automatic partitioning.

2. Memgraph

Memgraph is an open-source, in-memory graph database designed for real-time applications where relationships between data points are central to the workload. Unlike Redis, which focuses on key-value access and basic data structures, Memgraph models data as nodes and relationships, enabling efficient traversal and pattern matching across connected data.

Memgraph is typically introduced in systems where Redis-based designs become difficult to maintain due to application-side joins, excessive denormalization, or multi-step lookups required to reason over connected entities. Instead of encoding relationship logic in application code, Memgraph executes this logic directly at the database layer using a graph-native query model.

Memgraph uses the Cypher query language, allowing developers to express relationship queries declaratively. It is commonly used alongside Redis, rather than as a direct replacement, in architectures where Redis handles caching or ephemeral state while Memgraph manages relationship-aware queries.

Key Features & Considerations of Memgraph

In-memory graph database: Designed to execute graph queries with low latency by operating primarily in memory.
Relationship-native data model: Data is stored as entities and explicit relationships, making it suitable for workloads involving dependency graphs, recommendations, access control relationships, and network-style queries.
Declarative graph querying with Cypher: Complex traversals and pattern matching can be expressed directly in queries instead of being implemented in application logic.
Architectural alternative to Redis: Memgraph does not implement the Redis API and is not intended for generic key-value caching. It is used when the limitations of Redis’s data model become a bottleneck for relationship-heavy access patterns.

3. Memcached

Memcached is a distributed, in-memory caching system used to reduce database load and speed up dynamic web applications. It works by storing key-value pairs in memory, allowing quick retrieval of frequently accessed data. Memcached is simple and suitable for use cases that require temporary storage with minimal overhead.

Key Features & Considerations of Memcached:

In-memory key-value store: Stores data entirely in RAM for low-latency access. Optional warm restart to speed up recovery on server restarts.
Distributed architecture: Supports horizontal scaling by client-side sharding or server-side proxy.
Independent nodes: Each node operates independently, simplifying deployment. However, because of this simple architecture, online scaling and failover strategies need careful planning and additional work.
Lightweight protocol: Memcached supports multiple protocols, but the recommended ones are basic text and meta text, which are lightweight text-based protocols.

Learn more in our detailed guide to Redis vs. Memcached.

4. Apache Ignite

Apache Ignite is an open-source, distributed database management system designed for high-performance computing with in-memory speed, enabling tiered data storage, access, and processing for transactional, analytical, and streaming workloads.

Key Features & Considerations of Ignite:

In-memory data grid: Stores and processes data in memory to minimize latency.
SQL and ACID support: Enables complex queries and transactional consistency with ANSI SQL and ACID compliance.
Durable storage: Offers native persistence to store data on disk with automatic recovery.
Distributed architecture: Supports clustering, replication, and partitioning for scalability and fault tolerance.
Compute grid: Allows distributed computations and task execution near the data to reduce network overhead.
Machine learning and streaming: Includes APIs for data streaming and model training directly within the data grid.

Notable Managed Redis Services Alternatives

Managed services often provide a superior alternative to Redis.

1. Dragonfly Cloud

Dragonfly is a modern, source-available, multi-threaded, Redis-compatible in-memory data store that stands out by delivering unmatched performance and efficiency. Designed from the ground up to disrupt existing legacy technologies, Dragonfly redefines what an in-memory data store can achieve. With Dragonfly, you get the familiar API of Redis without the performance bottlenecks, making it an essential tool for modern cloud architectures aiming for peak performance and cost savings. Migrating from Redis to Dragonfly requires zero or minimal code changes.

Dragonfly Cloud is a fully managed service from the creators of Dragonfly, handling all operations and delivering effortless scaling so you can focus on what matters without worrying about in-memory data infrastructure anymore.

Key Features of Dragonfly:

Multi-Threaded Architecture: Efficiently leverages modern multi-core processors to maximize throughput and minimize latency.
Unmatched Performance: Achieves 25x better performance than Redis, ensuring your applications run with extremely high throughput and consistent latency.
Cost Efficiency: Reduces hardware and operational costs without sacrificing performance, making it an ideal choice for budget-conscious enterprises.
Redis API Compatibility: A seamless, drop-in replacement that maintains 100% compatibility with existing Redis applications while eliminating performance bottlenecks.
Innovative Design: Built to scale vertically and horizontally, providing a robust solution for rapidly growing data needs.

2. Amazon DynamoDB

DynamoDB is a managed, serverless NoSQL database developed by AWS, designed to deliver consistent fast performance at scale. It supports key-value and document data models, and automatically handles tasks like hardware provisioning, replication, software patching, scaling, and backups.

Key Features of DynamoDB:

Serverless architecture: No server provisioning or management; scales automatically based on traffic.
Single-digit millisecond latency: Delivers consistent low-latency performance at millions of requests per second.
NoSQL data model: Supports both key-value and document-based access patterns.
Fully managed: Handles infrastructure, scaling, patching, and backups automatically.
ACID transactions: Supports atomic, consistent, isolated, and durable operations across single or multiple tables.

3. Amazon ElastiCache

Amazon ElastiCache is a managed, in-memory caching service to improve application performance by reducing data access latency. It supports popular open source engines like Redis OSS, Memcached, and Valkey, and offers two operational modes: serverless caching for simplified deployment, and self-designed clusters for advanced control.

Key Features of Amazon ElastiCache:

Supports multiple engines: Compatible with Redis OSS, Memcached, and Valkey.
Serverless caching: Automatically handles capacity management, scaling, and infrastructure with no instance provisioning required.
Fast setup: Allows users to deploy a managed serverless cache using console, CLI, or SDK.
Automatic scaling: Monitors and adjusts compute, memory, and bandwidth usage to match application demands.
Simplified endpoint access: Provides a single endpoint for cache access.

Learn more in our detailed guide to Redis vs ElastiCache (coming soon)

4. Google Cloud Memorystore

Google Cloud Memorystore is a managed, in-memory data store that supports Redis, Redis Cluster, Valkey, and Memcached. Designed for low latency and high throughput, it simplifies the deployment and scaling of caching layers for cloud applications. It automates provisioning, replication, failover, and patching, reducing operational complexity.

Key Features of Google Cloud Memorystore:

Open source compatibility: Supports Valkey, Redis, Redis Cluster, and Memcached with no code changes required.
Managed service: Handles setup, replication, monitoring, scaling, failover, and patching to minimize DevOps overhead.
Scaling: Memorystore for Redis Cluster and Valkey supports scaling to 250 nodes and terabytes of keyspace without downtime.
High availability SLAs: Offers up to 99.99% SLA with automatic failover and cross-zone replication for Redis Cluster deployments.
Vector search (preview): Supports ANN and KNN search for generative AI applications requiring fast and flexible similarity lookups.

Key Considerations for Choosing Redis Alternatives

When evaluating alternatives to Redis, it’s important to consider how each tool aligns with your application’s performance needs, data durability requirements, and operational constraints. Below are some critical factors to guide your decision:

Performance profile: Assess latency, throughput, and concurrency. Look for alternatives that can either match Redis’s speed or offer better multicore utilization for CPU-bound workloads.
Scalability model: Evaluate how the system scales—vertically with more resources per node, or horizontally through clustering. Check support for automatic sharding and replication.
Persistence and durability guarantees: Consider the durability model. If data loss on crash is unacceptable, look for systems with stronger disk persistence or built-in consensus mechanisms.
License and compliance: Ensure the license is compatible with your usage model, especially for SaaS or embedded products. Some alternatives remain under OSI-approved licenses, while Redis has adopted source-available terms.
Querying and data structure needs: If the application relies on Redis capabilities like sorted sets, hyperloglogs, and streams, select a solution with comparable data structures or extensibility. Evaluate whether the system supports complex queries, indexing, or analytical processing if those are required for your use case.
Cost model: Consider both infrastructure and licensing costs. Memory usage, instance size, and vendor pricing models can significantly affect total cost of ownership.
Ecosystem and integration with cloud platforms: Ensure the tool has client libraries, monitoring support, and integration with your stack. Popularity can influence the quality of tooling and community support.
Operational complexity: Review management overhead. Fully managed services can reduce maintenance but may trade off flexibility or vendor lock-in.
Feature compatibility: Check if your workload depends on specific Redis features (e.g., Lua scripting, modules, pub/sub) and whether the alternative supports them.

Conclusion

Choosing an alternative to Redis requires a clear understanding of your application’s current and future demands. Factors like performance under load, ease of scaling, operational complexity, and licensing model all influence the decision. By aligning these requirements with the strengths of alternative technologies, teams can adopt a solution that not only meets technical needs but also aligns with organizational goals and constraints.

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Best Redis Alternatives: Top 8 OSS and Managed Solutions