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NoSQL Databases · Reviewed 21 Oct 2026

Redis Cloud

Fully managed Redis database for caching, search, and real-time AI applications.

from $5 · free tier · redis.io ↗

Paid against free › NoSQL Databases › Redis Cloud

Redis Cloud at a glance
734.7KEstimated total trafficModeled monthly visits22.1KRanking keywordsPublic search sample97/100Technical health15 pages checked0Referring domainsLive backlink summary

Overview

01

Summary

The useful answer before the supporting detail.

Redis Cloud is a fully managed in-memory database service built on Redis, the fast in-memory database for caching, vector search, and NoSQL S1 S39. It is used by developers and enterprises to build real-time applications such as caching, session management, and AI agent memory, solving the problem of deploying and scaling Redis without operational overhead S39. Its practical value lies in offering enterprise-grade features like active-active geo-distribution, Redis Data Integration, and AI capabilities, with deployment options across major clouds or on-premises S39 S40.

02

Product identity and positioning

Name, category, positioning, audience, and first-party claims.

  • Identity: Redis Cloud is the fully managed version of Redis, the in-memory database S39 S1.
  • Core purpose: Provides a fast, scalable data store for caching, search, and real-time AI workloads S1 S39.
  • Intended users: Developers and enterprises building real-time applications S49 S39.
  • Product scope: A managed cloud service, distinct from Redis Software (on-prem) and Redis open source, with components like Redis Insight and Redis Data Integration S1 S40.
  • Positioning: Positioned as the world's fastest in-memory database, enabling faster app development S1 S49.
  • Claimed distinction: Offers enterprise-grade architecture beyond open source, with features like active-active and Redis 8 on AWS/GCP S39 S40.
03

Use cases and audiences

Who the product serves and the jobs it is designed to handle.

AudiencePractical use case
DevelopersBuild real-time applications with caching, session management, and pub/sub messaging S39.
AI teamsUse Redis for semantic caching, agent memory, and vector search to power AI agents S1 S39.
EnterprisesDeploy globally with active-active geo-distribution for high availability S40.
Data engineersSync data from source databases to Redis for real-time feature serving S40.
Fraud detection teamsUse Redis for real-time decisions and feature stores to stop fraud S1 S42.

Features

09

Features and capabilities

Core functions and evidence-backed product capabilities.

CapabilityWhat it does
CachingProvides sub-millisecond read/write performance for caching use cases S1.
Vector searchSupports real-time vector search for semantic caching and AI agent memory S39.
Redis Data Integration (RDI)Syncs data from source databases and warehouses in near-real-time for fast reads S40.
Active-Active geo-distributionEnables multi-region replication for high availability and low latency S40 S39.
Redis CopilotAI-powered assistant to help build applications S39.
Redis InsightGUI for visualizing, querying, and debugging Redis data S1.
Data tiering (Flex)Intelligently manages data between RAM and NVMe SSD storage for cost efficiency S42.
10

Integrations and ecosystem

Connected products, exports, APIs, and workflow compatibility.

Integration or interfaceTypeWhat it enables
AWS, GCP, AzureCloud marketplaceDeploy Redis Cloud on major cloud providers, with Azure Managed Redis as a first-party service S39 S49.
Datadog, Dynatrace, New RelicMonitoring integrationMonitor Redis Cloud performance and metrics S15.
GrafanaIntegrationVisualize Redis metrics in Grafana dashboards S50.
ODBC/JDBC connectorData connectorConnect Redis to BI tools and other data systems S50.
Spark connectorData connectorIntegrate Redis with Apache Spark for data processing S50.
Redis Data Integration (RDI)Data integrationSync data from source databases and warehouses in near-real-time S40.
Client librariesSDKBuild applications with Python, Node, Java, Go, .NET, and more S1.

Pricing

11

Pricing and plans

Published plans, free access, trials, credits, and pricing limitations.

Plan or offerVerified price and billingIncludedLimits
Free$0 S40One database, shared deployment S4030 MB storage S40
EssentialsFrom $0.007/hour (~$5/month) S40Single database, up to 99.99% uptime S40250 MB to 100 GB storage S40
ProFrom $0.014/hour, minimum $200/month S40Multiple databases, active-active, auto-tiering S40Unlimited RAM S40
AnnualAnnual subscription pricing S50Includes 24/7 support, Grafana integration S50Not established
FlexibleOn-demand pricing S50Includes ODBC/JDBC connector S50Not established

Competition

12

Competitive landscape

Domains competing for the same organic search demand.

Sampled competitors

CompetitorFirst-page appearancesSampled queriesAverage positionQueries observed
percentagecal.com313.37.8 6
appbrain.com1127.8 6
medium.com112aof redis
wccftech.com1137.8 6
stackoverflow.com114aof redis
docs.cloud.google.com115aof redis
php.net1167.8 6
reddit.com1177.8 6
learn.microsoft.com117aof redis
math.answers.com1187.8 6

Website intelligence — Collected evidence

Customers, testimonials, and first-party review claims

No supporting public-page evidence was collected for this category.

Independent review sources discovered

PlatformStatusDiscovered source
Googlediscovered_not_scrapedhttps://drive.google.com/file/d/1ocJNDbEUxXVJqyBVD35k-Vvr1y8hjN5k/view
Googlediscovered_not_scrapedhttps://groups.google.com/g/feast-dev/c/D0IHN_DALFA?pli=1
Googlediscovered_not_scrapedhttps://drive.google.com/file/d/1KfOMI9C-aitJNPdGB56L-6tA8BBp9gsl/view
13

AI visibility

Measured ChatGPT and Google AI mentions with cited sources.

AI-assistant referral traffic

  • Estimated current generative-AI referral share: 2.0%
MonthSource
—All AI assistants
—All AI assistants
—All AI assistants

Website intelligence — Collected evidence

Site-stated AI capabilities

Page typeEvidenceSource
featuresFraud detectionStop fraud, protect customersReal-time decisionsAct on data in real timeCaching & performanceOur bread & butter Real-time messagingStreams at the speed of thoughtSession managementConsistent experiences everywhereLeaderboardsKnow who’s winninghttps://redis.io/feature-form/
featuresMachine learning thrives on real-time data, but creating real-time pipelines can seem impossible. Not with Feature Form. Simply define your features as code, and Feature Form will turn them into prod-grade data pipelines for model training and inference.https://redis.io/feature-form/
featuresOperationalize real-time data for machine learninghttps://redis.io/feature-form/
featuresBuilt for enterprise AI teamshttps://redis.io/feature-form/
featuresFraud detectionStop fraud, protect customersReal-time decisionsAct on data in real timeCaching & performanceOur bread & butter Real-time messagingStreams at the speed of thoughtSession managementConsistent experiences everywhereLeaderboardsKnow who’s winninghttps://redis.io/solutions/
featuresFraud detectionStop fraud, protect customersReal-time decisionsAct on data in real timeCaching & performanceOur bread & butter Real-time messagingStreams at the speed of thoughtSession managementConsistent experiences everywhereLeaderboardsKnow who’s winninghttps://redis.io/blog/building-feature-stores-with-redis-introduction-to-feast-with-redis/
featuresA feature store is a centralized place where data scientists from different teams across your organization can share features) for machine learning. The feature store allows them to search, reuse, and serve features in production at scale. As MLOps matures, feature stores are becoming a cornerstone of machine learning platforms for a few reasons:https://redis.io/blog/building-feature-stores-with-redis-introduction-to-feast-with-redis/
featuresWhen companies need to deliver real-time, ML-based applications to support high volumes of online traffic, Redis is most often selected as the foundation for the online feature store, thanks to its ability to deliver ultra-low latency with high throughput at scale. We’re seeing ML cloud platform providers offer feature stores using Redis, as in the recent examples of H2O AI Feature Store and Microsoft’s Azure Feature Store. And we’re also seeing a host of build-your-own implementations of feature stores using Redis at a variety of companies including Wix, Swiggy, Comcast, Zomato, AT&T, DoorDash, iFood, and Uber.https://redis.io/blog/building-feature-stores-with-redis-introduction-to-feast-with-redis/
featuresOpen source feature store implementations such as Feast are also choosing Redis. Redis was selected as Feast’s online store for real-time use cases at scale, first by the Indonesian ride-hailing company Gojek (who was the initial creator of Feast in collaboration with Google), and later by Feast’s early adopters, such as Udaan and Robinhood. In addition to that, Redis was selected by Microsoft for the online store of its new Azure Feature Store with Feast (stay tuned for more details on that soon!).https://redis.io/blog/building-feature-stores-with-redis-introduction-to-feast-with-redis/
featuresFeast (Feature store) is an open source feature store that’s part ofthe Linux Foundation’s AI & Data Foundation. Feast can serve features from a low-latency online store or from an offline store, while also providing a central registry, storage, and serving. This allows ML engineers and data scientists to discover the relevant features for ML use cases and serve them in production.https://redis.io/blog/building-feature-stores-with-redis-introduction-to-feast-with-redis/
featuresFraud detectionStop fraud, protect customersReal-time decisionsAct on data in real timeCaching & performanceOur bread & butter Real-time messagingStreams at the speed of thoughtSession managementConsistent experiences everywhereLeaderboardsKnow who’s winninghttps://redis.io/blog/feature-stores-for-real-time-artificial-intelligence-and-machine-learning/
featuresFeature Stores for Real-time AI/ML: Benchmarks, Architectures, and Case Studieshttps://redis.io/blog/feature-stores-for-real-time-artificial-intelligence-and-machine-learning/
features[](https://claude.ai/new?q=Search%20the%20web%20and%20answer%3A%20Summarize%20this%20Redis%20blog%20post%3A%20%22Feature%20Stores%20for%20Real-time%20AI%2FML%3A%20Benchmarks%2C%20Architectures%2C%20and%20Case%20Studies%22%20%E2%80%94%20https%3A%2F%2Fredis.io%2Fblog%2Ffeature-stores-for-real-time-artificial-intelligence-and-machine-learning.md)https://redis.io/blog/feature-stores-for-real-time-artificial-intelligence-and-machine-learning/
features[](https://www.perplexity.ai/search/new?q=Summarize%20this%20Redis%20blog%20post%3A%20%22Feature%20Stores%20for%20Real-time%20AI%2FML%3A%20Benchmarks%2C%20Architectures%2C%20and%20Case%20Studies%22%20%E2%80%94%20https%3A%2F%2Fredis.io%2Fblog%2Ffeature-stores-for-real-time-artificial-intelligence-and-machine-learning.md)https://redis.io/blog/feature-stores-for-real-time-artificial-intelligence-and-machine-learning/
featuresReal-time artificial intelligence / machine learning (AI/ML) use cases, such as fraud prevention and recommendation, are on the rise, and feature stores play a key role in deploying them successfully to production. According to popular open source feature store Feast, one of the most common questions users ask in their community Slack is: howscalable/performantis Feast?This is because the most important characteristic of a feature store for real-time AI/ML is the feature serving speed from the online store to the ML model for online predictions or scoring. Successful feature stores can meet stringent latency requirements (measured in milliseconds), consistently (think p99) and at scale (up to millions of queries per second, with gigabytes to terabytes-sized datasets) while at the same time maintaining a low total cost of ownership and high accuracy.https://redis.io/blog/feature-stores-for-real-time-artificial-intelligence-and-machine-learning/
featuresAs we will see in this post, the choice of an online feature store, as well as the architecture of the feature store, play important roles in determining how performant and cost-effective it is. It’s no wonder that oftentimes companies, before choosing their online feature store, perform thorough benchmarking to see which choice of architecture or online feature store is the most performant and cost-effective. In this post, we will review architectures and benchmarks from both DIY feature stores built by companies successfully deploying real-time AI/ML use cases and open source and commercial feature stores. Let’s begin.https://redis.io/blog/feature-stores-for-real-time-artificial-intelligence-and-machine-learning/
featuresIn the diagram above you can see an example of how the online mortgage company Better.com implemented its lead scoring ranking system using the open-source Feast feature store. As presented by Vitaly Sergey, Senior Software Engineer at Better.com, the features are materialized from the offline stores (S3, Snowflake, and Redshift) to the online store (Redis). In addition to that, features are also ingested into the online store from streaming sources (Kafka topics). Feast recently added support for streaming data sources (in addition to batch data sources) which is currently supported only for Redis. Supporting streaming data sources is very important for real-time AI/ML use cases as these use cases rely on fresh live data.https://redis.io/blog/feature-stores-for-real-time-artificial-intelligence-and-machine-learning/
featuresFraud detectionStop fraud, protect customersReal-time decisionsAct on data in real timeCaching & performanceOur bread & butter Real-time messagingStreams at the speed of thoughtSession managementConsistent experiences everywhereLeaderboardsKnow who’s winninghttps://redis.io/blog/introducing-redis-feature-form/
featuresFraud detectionStop fraud, protect customersReal-time decisionsAct on data in real timeCaching & performanceOur bread & butter Real-time messagingStreams at the speed of thoughtSession managementConsistent experiences everywhereLeaderboardsKnow who’s winninghttps://redis.io/solutions/ai-cost-optimization/
featuresAI & ML Appshttps://redis.io/feature-form/

These are first-party product claims, not independent measurements of AI-search visibility.

14

Marketing opportunities

Actions calculated directly from the collected evidence.

OpportunityRecommended actionEvidence
Competitor gapReview the sampled queries where percentagecal.com appears.3 first-page appearances across 7.8 6

Website intelligence — Collected evidence

Website evidence gaps

  • customers: no usable public page was collected; verify whether the site publishes this evidence.
  • reviews: no usable public page was collected; verify whether the site publishes this evidence.
  • changes: no usable public page was collected; verify whether the site publishes this evidence.
  • identity: no usable public page was collected; verify whether the site publishes this evidence.
  • technical: no usable public page was collected; verify whether the site publishes this evidence.

Trust and proof

15

Customers, proof, and reviews

Published proof, customer signals, and review evidence.

Independent reviews

PlatformPageHow it was foundNote
G2Redis Products / Read 172 Reviews on G2Grounded web searchProductRedis Software130 reviewsRedis Software is designed for organizations that need fast, reliable in-memory database capabilities alongside the flexibility to manage their infrastructure. With sub-millisecond latency and millions of operations per second, Redis Software excels in delivering real-time performance. Built for high availability and disaster recovery, Redis Software ensures your data remains safe and accessible, providing the foundation for fast, scalable applications. ... Produc

Customer proof

No named customers, testimonials, or customer case studies were found.

Independent mentions

External links were grounded in one live web search. Search snippets are leads, not independently scraped page content; rejected links remain available in Review evidence.

16

Company, security, and trust

Ownership, policies, security signals, and public trust evidence.

SignalFindingSource
Company and ownershipRedis is the company behind Redis Cloud, the world's fastest in-memory database S1.S1
Contact and support24/7 toll-free support hotline is available on paid plans S50.S50
Legal and privacyRedis uses cookies in compliance with its Privacy Policy S39.S39
Security and complianceClaims compliance with SOC 2, PCI, HIPAA, GDPR S39.S39
Uptime and status pageOffers up to 99.999% uptime SLA S15 S40.S15
17

Documentation and product updates

Help resources, product guidance, and visible changes over time.

Resource or updateTypeWhat is availableDate or freshness
DocsDocumentationDocumentation is publicly available. (source)Not dated
Contact and supportSupportPublic contact and support channel. (source)Not dated
Contact and supportSupportPublic contact and support channel. (source)Not dated
What Is a Solution Architect, and How to Become One / RedisGuideDevelopers love Redis. Unlock the full potential of the Redis database with Redis Enterprise and start building blazing fast apps. (source)Not dated
About Redis: Fast in-memory databaseRelease notesPublic product change history. (source)Not dated
Redis pricing calculator: find your plan fastRoadmapPublic product roadmap. (source)Not dated

Not found in the collected public sources: status page, separate help center or knowledge base.

Technical

18

Technical and delivery signals

Delivery model, platforms, API, data ownership, reliability claims and release activity.

SignalFindingSource
Delivery model (cloud, self-hosted, desktop, browser extension)Fully managed cloud service, deployable on-prem or BYOC S39.S39
Platforms and appsWorks on most POSIX systems like Linux, *BSD, and Mac OS X S1.S1
API and extensibility (API, SDK, webhooks, MCP, plugins)Provides client libraries for Python, Node, Java, Go, .NET, and more S1.S1
Data ownership and exportNot published on the collected pages.—
Performance and reliability claimsSub-millisecond local read/write latency at any throughput S39.S39
Release activityRedis 8.4 is fully managed S39.S39
19

Measured infrastructure

Domain, certificate, and hosting signals, measured directly.

SignalFinding
First archived2010-06-02 — web archive
TLS certificateGoogle Trust Services, valid, expires 2026-12-03
Security headersHSTS, CSP
Headers not servedX-Content-Type-Options, X-Frame-Options, Referrer-Policy
Server headercloudflare
Mail handled byus-smtp-inbound-1.mimecast.com, us-smtp-inbound-2.mimecast.com
Nameserversns-791.awsdns-34.net, ns-1248.awsdns-28.org

Not measurable: rdap: no RDAP server published for .io. A gap here means the lookup was unavailable, not that the finding is negative.

20

Technical site audit

A bounded multi-page crawl with page-level issues and URLs.

Scout — Bounded technical crawl

  • Pages crawled: 15 of 15 maximum
  • Technical health: 97/100
  • Critical issues: 0
  • Warnings: 6
  • Notices: 4

Issues

SeverityIssueValueAffected URL
noticeMeta description length should be reviewed173https://redis.io/langcache/
noticePage is marked noindex—https://redis.io/context-retriever/
noticeMeta description length should be reviewed47https://redis.io/feature-form/
noticeMeta description length should be reviewed336https://redis.io/resources/videos/agent-memory-workshop/
warningDuplicate meta description in audit sample6https://redis.io/
warningDuplicate meta description in audit sample6https://redis.io/resources/agent-memory-benchmark-report/
warningDuplicate meta description in audit sample6https://redis.io/iris/
warningDuplicate meta description in audit sample6https://redis.io/context-retriever/
warningDuplicate meta description in audit sample6https://redis.io/agent-memory/
warningDuplicate meta description in audit sample6https://redis.io/solutions/flex/

Crawled pages

StatusTitleH1sWordsResponseURL
200Redis - Real-time data for agents & apps11358226 mshttps://redis.io/
200Agent Memory benchmark report \Redis13077497 ms
200Redis Iris \Redis11896349 ms
200Redis LangCache11411194 mshttps://redis.io/langcache/
200Context Retriever \Redis11299211 ms
200Redis Agent Memory \Redis11293395 ms
200Redis Data Integration (RDI) — Real-time data, Redis fast11864317 mshttps://redis.io/data-integration/
200Flex \Redis11321368 ms
200Fast caching solutions with Redis11812569 mshttps://redis.io/solutions/caching/
200Fast messaging solutions with Redis11556527 mshttps://redis.io/solutions/messaging/
200Fast session management solutions with Redis12128480 mshttps://redis.io/solutions/session-management/
200Redis Search: real-time search & query at scale11360178 mshttps://redis.io/search/
200Redis Feature Store11640305 mshttps://redis.io/feature-form/
200Agent memory workshop11273337 mshttps://redis.io/resources/videos/agent-memory-workshop/
200Download Redis now and learn what fast really means12376470 mshttps://redis.io/downloads/

This is a bounded Scout crawl. Core Web Vitals and Lighthouse scores are not claimed unless a dedicated performance audit is connected.

21

Search Console and Analytics

First-party search and behavioral data connection state.

Modeled acquisition channels

ChannelEstimated share
direct32.9%
search49.1%
search organic48.4%
search paid0.7%
social4.6%
social organic4.3%
social paid0.3%
referrals9.9%
paid referrals0.4%
mail1.1%
gen ai2.0%
affiliate0.0%
other0.0%

First-party connections

IntegrationStatusData available after connection
Google Search ConsoleNot connectedClicks, impressions, CTR, average position, queries, pages, countries, and devices
Google AnalyticsNot connectedOrganic sessions, engagement, landing pages, conversions, geography, and devices

No first-party Search Console or Analytics numbers are shown until the site owner authorizes the corresponding Google account. Scout traffic model values above are modeled estimates, not private analytics totals.

Limits and method

22

Limitations and open questions

Known constraints and missing evidence from the collected pages.

The collected sources do not mention any competitors by name. Evidence gaps include lack of named customer references, independent review ratings, and detailed security certifications beyond claims. Open questions remain about data export mechanisms and specific performance benchmarks under production workloads.

23

Sources, method, and limitations

Evidence inventory, collection method, provider use, and constraints.

This report is based on 15 collected pages from redis.io, including product pages, pricing, documentation, and blog posts. The evidence catalog includes static and rendered page captures, with external proof search limited to one query. Pricing data was verified from the authoritative pricing page. Limitations include potential marketing bias and lack of third-party verification.

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