Redis on Azure: Service Options, Pricing, Pros & Cons

How Can You Run Redis on Azure?

Azure makes it possible to deploy Redis on its cloud platform. Redis, known for its speed and support for versatile data structures such as strings, hashes, and sorted sets, is commonly used for caching, session management, and real-time analytics.

On Azure, Redis can be accessed via managed services or self-hosted options, integrating with Microsoft’s infrastructure for scalability, availability, and security. Using Redis on Azure means organizations can quickly provision and operate Redis instances without handling hardware or complex setups. Azure-based services also provide built-in security features such as virtual networks and authentication integration, ensuring Redis deployments meet enterprise compliance requirements.

This is part of our Redis tutorial series.

Why Use Redis on Azure?

Redis on Azure provides significant benefits in terms of scalability, reliability, and ease of management. By leveraging Azure’s cloud infrastructure, organizations can integrate Redis into their existing applications with minimal setup. This approach eliminates the complexities of managing hardware, ensuring resources are dynamically scaled to meet demand without manual intervention.

Azure’s global network of data centers ensures high availability and low-latency access to Redis instances, making it suitable for applications with stringent performance requirements. Additionally, Azure’s built-in security features, such as encryption, network isolation, and access control, ensure that Redis deployments are secure and compliant with industry standards.

Options for Running Redis on Azure

Azure Managed Redis

Azure Managed Redis is a fully managed version of Redis offered by Microsoft. It is based on a collaboration between Azure and Redis Ltd. to deliver Redis Software (the enterprise version of Redis) in the cloud. It abstracts the underlying infrastructure, providing automated scaling, patching, and high availability features. This service allows developers to deploy Redis with minimal setup, enabling them to focus on application development rather than server management.

Azure Managed Redis supports automatic backups, data persistence, and built-in security. It also offers enterprise Redis features like active geo-replication, NVMe tiered storage, and all Redis modules (JSON, search, time series, etc.).

Azure Cache for Redis

Azure Cache for Redis is a legacy service that will be retired in September 2028. It is still supported for general workloads running on Redis. Azure customers are encouraged to migrate to Azure Managed Redis.

The service offers different pricing tiers based on features such as data persistence, replication, and memory size. It also integrates well with other Azure services like Azure App Services, Azure Functions, and Azure Kubernetes Service (AKS).

Redis on Azure Pricing

This section provides pricing for some Azure service options, which is correct as of the time of this writing in the Central US region. Cloud pricing is subject to change, please see the official pricing page for up-to-date pricing and additional options.

Azure Managed Redis Pricing

Azure Managed Redis provides several configurations based on resource allocation, allowing users to select the optimal service depending on their workload. The pricing is calculated based on the memory size, availability, and network performance of the instance:

• Memory optimized tiers are suitable for medium to large caches, providing a high memory-to-core ratio. Prices start from $173.74/month for 12 GB (M10) and scale up to $22,200.76/month for 1,920 GB (M2000).
• Balanced (Memory + Compute) configurations are designed for most standard workloads, offering a balanced CPU-to-memory ratio. Prices range from $13.14/month for 1 GB (B0) to $16,034.45/month for 960 GB (B1000).
• Compute optimized instances, with a high CPU-to-memory ratio, are best for mission-critical workloads that require maximum throughput. Pricing begins at $175.93/month for 3 GB (X3) and reaches $18,720.85/month for 720 GB (X700).
• Flash optimized instances leverage high-speed NVMe storage alongside RAM, making them more cost-effective while offering reduced throughput performance. Pricing starts at $1,252.68/month for 256 GB (A250) and goes up to $20,037.77/month for 4,723 GB (A4500).

Azure Cache for Redis Pricing

Azure Cache for Redis also offers several pricing tiers, including Basic, Standard, Premium, Enterprise, and Enterprise Flash, each designed for different caching requirements:

• Basic tier is intended for non-critical workloads and development/test environments, with prices starting at $16.06/month for 250 MB (C0).
• Standard tier includes replication for high availability, with pricing starting at $40.15/month for 250 MB (C0), scaling up to $1,533/month for 53 GB (C6).
• Premium tier provides advanced features like persistence, clustering, and higher availability. Prices start at $404.42/month for 6 GB (P1) and can reach $7,329.20/month for 120 GB (P5).
• Enterprise tier includes powerful Redis Labs features such as Redis Modules, active geo-replication, and higher availability, with prices starting at $81.176/month for 1 GB (E1) and going up to $35,631.30/month for 400 GB (E400).
• Enterprise Flash tier, which combines RAM and flash storage for massive cache sizes at a lower cost per GB, is priced from $7,920.57/month for 384 GB (F300) to $31,682/month for 1,455 GB (F1500).

It is important to note that pricing may vary depending on the region, chosen options (such as geo-replication or zone redundancy), and the number of nodes in use.

Limitations of Running Redis on Azure

While Redis on Azure offers convenience and scalability, several limitations have been identified by users that can impact its effectiveness in production environments. These limitations were mentioned in reviews on Microsoft and G2.

1. High cost: Many users report that Azure Redis services are significantly more expensive compared to self-hosting. This is especially true for larger cache sizes or higher-tier plans, where costs can rise quickly. Some reviewers noted hidden fees and unpredictable billing as additional concerns.
2. Version lag: Azure-managed Redis often lags behind the latest official Redis releases. For instance, users have expressed frustration over the unavailability of newer Redis versions like 6.2 or 7.0, making it harder to access newer features or bug fixes.
3. Reliability issues: Service instability is a common complaint. Users describe frequent timeouts, delayed allocations, and slow startup times—sometimes taking over 15 minutes. In severe cases, Redis instances become unresponsive or fail altogether, requiring forced scaling or redeployment.
4. Limited flexibility and troubleshooting: Some users mention the inability to reboot services or delete stuck deployments, especially when using infrastructure-as-code tools like Terraform. This lack of control makes it difficult to recover from deployment failures or make quick adjustments.
5. Performance bottlenecks: Performance issues can arise with large payloads or under concurrent load. For example, read operations on keys with large values (e.g., over 350 KB) have shown noticeable slowdowns. Additionally, Redis integration with services like Azure Functions may result in stale or inconsistent data being returned.
6. Complexity in scaling and configuration: Though marketed as simple to scale, users note that peak load management often requires manual DevOps intervention. Configuring clusters or elastic services isn’t always intuitive, especially when compared to platforms like AWS.
7. Limited SLAs in lower tiers: The Basic tier lacks service-level agreements, forcing some teams to adopt higher-cost tiers even for development or testing environments, increasing overall spend.

Best Practices for Using Redis on Azure

Here are some of the practices that organizations should consider when working with Redis on Azure.

1. Enable Persistence to Protect Data

By persisting snapshots or logs to disk, Redis can recover from failures without losing vital information, which is critical for applications that cannot tolerate data loss. Different service tiers typically offer the flexibility to configure how often snapshots are taken, balancing between performance and data durability.

This practice is especially valuable for session stores, queue backends, or other scenarios where stateful recovery is necessary. Strictly in-memory Redis can provide ultra-fast performance but introduces risk if cache data represents the canonical source or if application state must be preserved beyond process lifetimes.

2. Use Clustering for Horizontal Scaling and to Handle Large Datasets

Redis clustering enables horizontal scaling by partitioning data across multiple nodes, boosting both storage capability and read/write throughput. In Azure, clustering is widely supported, letting applications work with datasets that exceed the memory limits of a single node.

This approach maximizes resource utilization and helps maintain low latency under heavy workloads, ensuring continued performance as application data grows. Clustering also provides higher availability since cluster nodes can independently handle read or write operations and recover from node failures gracefully. As data volume or traffic loads increase, teams can add more nodes to scale out effectively without restructuring applications.

3. Implement Virtual Network (VNet) Integration and Use TLS Encryption

Integrating Redis deployments with Azure Virtual Networks (VNets) isolates the cache from the public internet, reducing the attack surface and improving security. VNet integration allows connections only from designated subnets or trusted resources within the organization’s private network.

Access control is further strengthened with firewalls, subnet rules, and private endpoints, ensuring only approved applications and users can reach the cache. Secure transmission of data in transit is essential, especially for sensitive or regulated workloads. TLS encryption should always be enabled to protect cache data over the wire, regardless of the applications or services connecting to Redis.

4. Use Azure Monitor for Real-Time Diagnostics and Alerts

Azure Monitor provides integrated logs, metrics, and alerts for Redis instances, allowing teams to track cache performance, resource usage, and error rates in real time. Proactive monitoring helps detect issues such as high latency, memory pressure, or connection failures before they impact end users.

Azure Monitor's customizable dashboards and alerting policies let operators respond quickly, minimizing downtime or disruption. Continuous observability is essential for optimizing cost efficiency and maintaining service reliability. By analyzing performance trends and usage spikes, teams can right-size Redis deployments and determine when scaling or architectural changes are warranted.

5. Choose the Appropriate Pricing Tier Based on Workload Requirements

Lower-tier offerings may be adequate for development, dev/test, or small production workloads, but larger applications or mission-critical systems often require features exclusive to higher tiers, such as clustering, persistence, and VNet integration. Matching the specification to workload demand prevents overspending on unnecessary features while avoiding the risks of under-provisioning.

Regularly reviewing application patterns—such as data set size, concurrent connection counts, and required throughput—ensures ongoing alignment with the most suitable tier. Azure allows for tier upgrades as requirements change, giving organizations the flexibility to adapt as applications grow or usage spikes.

Dragonfly: Next-Gen In-Memory Data Store with Limitless Scalability

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 legacy technologies, Dragonfly redefines what an in-memory data store can achieve.

Dragonfly Scales Both Vertically and Horizontally

Dragonfly's architecture allows a single instance to fully utilize a modern multi-core server, handling up to millions of requests per second (RPS) and 1TB of in-memory data. This high vertical scalability often eliminates the need for clustering—unlike Redis, which typically requires a cluster even on a powerful single server (premature horizontal scaling). As a result, Dragonfly significantly reduces operational overhead while delivering superior performance.

For workloads that exceed even these limits, Dragonfly offers a horizontal scaling solution: Dragonfly Swarm. Swarm seamlessly extends Dragonfly's capabilities to handle 100 million+ RPS and 100 TB+ of memory capacity, providing a path for massive growth.

Key Advancements 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: Offers seamless integration with existing applications and frameworks running on Redis while overcoming its limitations.
• Innovative Design: Built to scale vertically and horizontally, providing a robust solution for rapidly growing data needs.

Dragonfly Cloud

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.