# Redis introduces new capabilities to help you operate, build, and scale with confidence

**Tagline:** News & Media | **Authors:** Jeff Mills | **Categories:** Tech | **Published:** 2026-09-22 | **Updated:** 2026-09-22

Your apps are growing more distributed, data-intensive, and business-critical. As Redis has become a larger part of your architecture, managing deployments, connecting data sources, and responding to changing demand can introduce operational friction.

We’re excited to share our latest capabilities across three areas that matter most to enterprise teams: operating Redis more effectively, building a unified data layer, and scaling easier than ever. These brand new capabilities are designed for organizations already running Redis across cloud, on-premises, or mixed environments.

Today we’re launching the following:

- **[Redis Radar](http://redis.io/blog/introducing-redis-radar/)** helps teams understand and manage deployments across their entire Redis footprint.

- **[Datadog integration](http://redis.io/blog/monitor-redis-cloud-with-datadog/)** connects Redis to established monitoring and observability workflows.

- **[Multi-source and multi-pipeline RDI](http://redis.io/blog/multi-source-and-multi-pipeline-rdi/)** helps unify data from multiple systems in Redis.

- **[Smooth Scaling](http://redis.io/blog/smooth-scaling-in-redis-cloud-pro/)** makes Redis capacity changes more efficient and less disruptive.

- **[Search on Flex](http://redis.io/blog/search-on-flex-comes-to-redis-cloud-pro/)** extends search to large-scale workloads with indexes stored on SSD.

## Operate Redis with greater visibility

As Redis deployments grow across teams, apps, and environments, it can become difficult to understand where Redis is running, how much capacity is available, and where additional resources may be needed.

Redis Radar provides a centralized view of Redis deployments across environments and domains—including open source deployments—so teams can gain visibility from a single interface. It also helps identify excess capacity and areas where additional capacity may be required.

For enterprise architects and platform teams, this means a clearer understanding of the Redis footprint and a stronger foundation for capacity planning, licensing, governance, and operational decision-making.

We’re also making it easier to incorporate Redis into existing observability practices. New integrations with Datadog allow teams to connect Redis with monitoring tools they already use, helping operators bring Redis into established workflows instead of creating separate operational processes.

## Build a unified data layer for applications and AI

Enterprise data rarely resides in one place. It is distributed across transactional databases, warehouses, legacy systems, and other specialized data stores.

Redis Data Integration (RDI) helps synchronize data from existing databases into Redis in real time. With multi-source and multi-pipeline capabilities, teams can bring data from multiple systems into a unified Redis layer while managing each pipeline on its own schedule.

This gives apps and agents faster access to the context they need, without requiring every workload to connect directly to every underlying system. For architects, the result is a more flexible approach to data movement and a practical way to consolidate frequently accessed data for real-time use cases.

## Scale with changing demand

Internet-facing apps rarely experience perfectly predictable demand. A product launch, seasonal event, promotion, or unexpected traffic spike can require additional Redis capacity quickly—while demand may later decline.

Smooth Scaling improves the underlying scaling process for Redis Cloud Pro, making resource provisioning more efficient and less disruptive to database clients. Customers continue to use the same scaling workflow while Redis manages the underlying process more efficiently.

The result is a more practical way to align Redis capacity with demand across a deployment—helping teams get the resources they need without retaining unnecessary capacity when demand subsides. Smooth Scaling does not mean automatic or scheduled scaling; scaling remains user-initiated.

## Bring search to large-scale data workloads in Redis with Redis Flex

Redis Flex is designed for large workloads by utilizing RAM and SSD by keeping the hottest data in memory and storing the rest on SDD. This architecture has enabled use cases such as fraud detection, large feature stores, and session stores at petabyte scale.

This launch brings Search to Redis Flex, allowing indexes that are too large to fit in RAM to reside on SSD. That expands the potential for large-scale search workloads on Redis while helping organizations manage the economics of growing data volumes.

For architects, this creates new possibilities for applying search to large datasets without treating memory capacity as the only constraint.

## Designed for the next stage of your Redis journey

These capabilities address the challenges that emerge as organizations expand their use of Redis:

- Redis Radar helps teams understand and manage deployments across their estate.

- Datadog and Grafana integrations connect Redis to established monitoring and observability workflows.

- Multi-source and multi-pipeline RDI helps unify data from multiple systems in Redis.

- Smooth Scaling makes Redis Cloud capacity changes more efficient and less disruptive.

- Search on Flex extends search to large-scale workloads with indexes stored on SSD.

Together, these enhancements help enterprise teams reduce operational friction and build a more scalable foundation for real-time applications and AI-enabled workloads.

*To learn more about our expanded capabilities, click through to each supporting article that goes deeper into each new release. And don’t forget to give them a try and reach out to *[*chat with us*](https://redis.io/enterprise-launch)*.*