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AI visibility report

AI visibility report for Redis in Databases & Data Infrastructure.

Outside the top three on 18 of the 25 prompts buyers actually ask.

PingCAP is cited on 6 of those losses.

25 prompts
6 platforms
Updated Jul 20, 2026 - refreshed weekly
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1percent
Presence Rate
Low presence

Still absent from 99.3% of tracked prompt responses

Top-3 citations across 150 prompt × platform pairs

+0.00
Sentiment
-1.00.0+1.0
Neutral
No clearrank

Peer Ranking

#1#15
No clear rankin Databases & Data Infrastructure

Key Metrics

Presence Rate0.7%
Share of Voice1.4%
Avg Position#2.0
Docs Presence0.0%
Blog Presence0.0%
Brand Mentions12.7%

Platform Breakdown

Google AI Mode
4%1/25 prompts
Bing Copilot
0%0/25 prompts
Perplexity
0%0/25 prompts
ChatGPT
0%0/25 prompts
Gemini Search
0%0/25 prompts
Grok
0%0/25 prompts

How to read this. Redis appears in 0.7% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.

Where Redis is losing

Prompts where competitors are visible and Redis is not.

These prompt-level losses are the first prompts to track and repair.

Where Redis is winning1

  • Which ORMs and query builders offer the best TypeScript experience for a distributed SQL database?

    Avg # 2.0 · 1 platform

Where Redis is losing5

  • Which document databases handle schema evolution most smoothly — without requiring migration scripts for every change?

    Competitors on 4 platforms

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  • Which time-series databases maintain query performance best at 10 million events per second ingestion over long retention periods?

    Competitors on 4 platforms

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  • Which globally distributed SQL databases are worth evaluating for a latency-sensitive SaaS product compared to a traditional single-region setup?

    Competitors on 3 platforms

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  • Which distributed SQL platforms support migrating from a legacy relational database with minimal downtime for a production application?

    Competitors on 3 platforms

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  • Which columnar databases handle mixed OLAP and OLTP workloads well — when does it make sense to use one over a standard row-store?

    Competitors on 3 platforms

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Research dossierCapabilities, use cases, sources, reviews, pricing, and FAQ

Overview

Redis is an open-source in-memory data platform founded in 2011 and commercially operated by Redis Ltd. (formerly Redis Labs), headquartered in San Francisco, California. Originally a high-performance key-value cache and data structure server created by Salvatore Sanfilippo, Redis has expanded into a multi-model real-time data platform supporting caching, vector search, pub/sub messaging, streams, time series, full-text search, and generative AI use cases. It delivers sub-millisecond read and write latency by keeping data primarily in memory with optional disk persistence. Redis is offered as Redis Open Source (tri-licensed RSALv2/SSPLv1/AGPLv3 from v8.0), self-managed Redis Software with enterprise features, and as fully managed Redis Cloud on AWS, Azure, and Google Cloud. With over 10,000 enterprise customers and 73,000+ GitHub stars, Redis is widely recognized as the de facto standard for application caching and an increasingly prominent real-time context layer for AI applications.

Redis is a real-time in-memory data platform offering a multi-model database—key-value, document (JSON), time series, vector, and probabilistic data structures—with native support for caching, pub/sub messaging, streams, full-text search, vector similarity search, and AI agent memory. Available as open source, self-managed enterprise software, or a fully managed cloud service, Redis delivers sub-millisecond latency at scale and serves use cases from application caching to GenAI-powered RAG pipelines. Its expanding AI product suite includes LangCache (fully managed semantic caching for LLMs), Redis Data Integration (CDC-based real-time sync), and integrations with major AI agent frameworks including LangChain, LangGraph, and AutoGen.

Key Facts

Founded
2011
HQ
San Francisco, CA, USA
Founders
Ofer Bengal, Yiftach Shoolman
Employees
1000-5000
Funding
~$355M
Customers
10,000+
Valuation
$2B
Status
Private

Target users

Backend and full-stack developers building real-time, high-traffic applicationsAI/ML engineers building GenAI applications, RAG pipelines, and AI agent memory layersPlatform and infrastructure engineers managing low-latency data tiers at scaleEnterprise architects at financial services, gaming, retail, and healthcare companiesDevOps and SRE teams requiring high-availability, geo-distributed data infrastructureStartups and growth-stage companies needing fast, managed cloud database infrastructure

Key Capabilities10

  • Sub-millisecond in-memory read/write latency with optional disk persistence (AOF/RDB)
  • 18+ native data structures: strings, hashes, lists, sets, sorted sets, JSON, streams, time series, vector sets, probabilistic types
  • Redis Query Engine: vector search, full-text search, geospatial queries, secondary indexing, and aggregations on hash/JSON documents
  • LangCache: fully managed semantic caching for LLMs with up to 70% cost reduction and 15x faster cache-hit response times
  • Redis Data Integration (RDI): zero-code CDC-based real-time data sync from source databases into Redis
  • Active-Active geo-distribution with 99.999% uptime SLA and local sub-millisecond latency across regions
  • Pub/Sub messaging and Streams for event-driven architectures and real-time data pipelines
  • Flexible deployment: Redis Cloud (fully managed, AWS/Azure/GCP), Redis Software (self-managed enterprise), Redis Open Source
  • Redis Insight GUI with Redis Copilot AI assistant for data visualization, debugging, and query development
  • Auto-Tiering / Redis Flex: hybrid RAM + SSD storage for up to 75% cost reduction on large datasets

Key Use Cases8

  • Application caching and database query caching to reduce backend load and latency
  • Session management and authentication token storage for web and mobile applications
  • Vector database and AI agent memory layer for RAG pipelines and GenAI applications
  • LLM semantic caching to reduce token costs and improve chatbot/agent response times
  • Real-time leaderboards, rate limiting, and analytics using sorted sets and HyperLogLog
  • Event streaming and pub/sub messaging between microservices
  • Distributed session storage and feature stores for machine learning pipelines
  • Fraud detection and risk scoring using real-time in-memory data processing

Redis customer outcomes

Axis Bank

76% faster performance; wait times from 170ms to 40ms; $82,000 saved

Axis Bank used Redis Data Integration (RDI) to power its mobile banking app for over 10 million daily users, achieving dramatically faster data retrieval and eliminating manual intervention for account updates. The deployment reduced user wait times from 170ms to 40ms and saved $

Mangoes.ai

70% cache hit rate; 70% LLM cost reduction; 4x faster responses

Mangoes.ai deployed Redis LangCache for its healthcare voice assistant application, achieving a 70% cache hit rate that translated directly into 70% savings on LLM API spend while delivering 4x faster responses for real-time patient interactions.

Recent Trend

Visibility-2.4 pts
Avg position-3.94
Sentiment-0.44

How AI describes Redis3

Database Comparison Guide 2026: MySQL vs PostgreSQL vs MongoDB vs Redis vs SQLite vs Supabase — OnlineTools4Free Research | OnlineTools4Free 5. Interpret results in terms of scalability, latency variance, and operational risk, not just raw thr...

What tools and benchmarks help compare database platforms for high-concurrency transactional workloads before committing to one?

bing-copilot-searchDirect Redis mention
...most practical managed database platforms for multi‑cloud portability today are MongoDB Atlas, CockroachDB, YugabyteDB, and Redis Enterprise Cloud — all designed to run seamlessly across AWS, Azure, and GCP without locking you into one provider. These...

Which managed database platforms make multi-cloud portability practical — so moving between cloud providers isn't a nightmare?

bing-copilot-searchDirect Redis mention
Best-fit caching tools for persistent databases are Redis, Memcached, and newer distributed in-memory systems like Apache Ignite or DragonflyDB.

What in-memory caching tools integrate best with persistent databases — and which are worth adding versus just optimizing primary database queries?

bing-copilot-searchDirect Redis mention

Alternatives in Databases & Data Infrastructure6

Redis positions itself as 'the real-time context engine for AI apps,' differentiating through extreme speed (sub-millisecond in-memory latency), a uniquely broad multi-model data structure set (18+ types including vector sets, JSON, streams, and probabilistic structures), and an evolving AI/GenAI stack anchored by LangCache semantic caching and native vector search.

  • Unlike MongoDB or SingleStore, Redis's primary identity is performance-first in-memory infrastructure rather than an OLTP or analytical primary database.
  • Redis competes directly against managed caching services (Amazon ElastiCache, Google Memorystore) and increasingly against purpose-built vector databases (Pinecone, Weaviate).
  • Its licensing shift in 2024—from BSD-3 to RSALv2/SSPLv1, then adding AGPLv3 in v8.0—constrains cloud provider redistribution of open-source Redis, protecting commercial differentiation for Redis Cloud and Redis Software.
View category comparison hub

Reviews

Praised

  • Extremely fast, sub-millisecond performance
  • Simple API and easy to get started
  • Rich and versatile data structure support
  • Reliable for caching and session management
  • Strong multi-language client library ecosystem
  • Great for pub/sub and real-time event streaming
  • Active open-source community and good documentation
  • Improves application latency and reduces database load

Criticized

  • High enterprise/commercial licensing costs for smaller teams
  • Dataset size limited by available RAM
  • Complex cluster configuration and CROSSSLOT errors at scale
  • Not suitable as a sole primary database for complex queries
  • Persistence (AOF/RDB) adds latency and operational complexity
  • License change from BSD-3 created open-source community uncertainty
  • Backup and recovery complexity for large-scale deployments

Redis receives consistently strong reviews across platforms, praised almost universally for its exceptional speed, simplicity, and reliability for caching and session management workloads. Reviewers on G2 and Gartner Peer Insights highlight sub-millisecond performance and ease of integration with multiple programming languages as standout strengths. Common criticisms center on enterprise licensing cost (perceived as high for smaller teams), the learning curve for Redis Cluster configuration at scale, and the platform's limitations as a sole primary database for complex query workloads. G2 reviewers note that backup complexity for large-scale deployments and limited built-in query language (compared to SQL) are practical pain points.

Pricing

Redis Cloud is offered in three tiers: Free (always free, 30MB, shared deployment, community support); Essentials (from $0.007/hour, approximately $5/month minimum, shared deployment, 250MB–100GB RAM+SSD, SAML SSO, RBAC, encryption, up to 99.99% SLA); and Pro (from $0.014/hour with a minimum ~$200/month, dedicated deployment, unlimited RAM, multi-region active-active, auto-tiering, up to 99.999% SLA). Redis Flex (hybrid RAM+SSD) is available under Essentials and Pro for up to 75% lower cost on large datasets. Redis Software (self-managed enterprise) and Redis for Kubernetes are available on annual plans with custom enterprise pricing. All Redis Cloud tiers are available on AWS, Azure, and Google Cloud. Redis Open Source is free to download and self-host.

Limitations

  • Redis's primary data store is memory-resident, meaning dataset sizes are bounded by available RAM (though Redis Flex/Auto-Tiering extends to SSD).
  • Core data-access remains effectively single-threaded per shard, limiting vertical scaling for CPU-intensive workloads.
  • Horizontal scaling via Redis Cluster can introduce complexity—certain multi-key commands that work on single instances fail with CROSSSLOT errors in cluster mode, potentially requiring application code changes.
  • Persistence options (AOF/RDB) introduce latency overhead and are less durable than disk-native databases.
  • Redis is not a general-purpose relational database and lacks ACID transaction guarantees across multiple keys at scale, as well as native SQL query support.
  • The 2024 license change from BSD-3 to RSALv2/SSPLv1 (with AGPLv3 added in v8.0) raised community concerns and prompted several open-source forks (Valkey, Dragonfly).
  • Enterprise licensing costs are frequently cited as a barrier for smaller teams.

Frequently asked questions

Topic coverageCoverage by buyer topic

Topic Coverage

Capability0/5DevEx1/5Integrations &Ecosystem0/5Performance &Reliability0/5Setup & First Run0/5

Prompt-Level Results

Brand citedCompetitor citedNot cited
PromptBing CopilotPerplexityChatGPTGoogle AI ModeGemini SearchGrok
Capability0/5 cited (0%)

Which globally distributed SQL databases are worth evaluating for a latency-sensitive SaaS product compared to a traditional single-region setup?

What in-memory caching tools integrate best with persistent databases — and which are worth adding versus just optimizing primary database queries?

Which managed database platforms offer the best multi-region replication with automatic conflict resolution for write-write scenarios?

What are the best dedicated vector databases, and how do they compare to adding vector search extensions to an existing relational database?

Which columnar databases handle mixed OLAP and OLTP workloads well — when does it make sense to use one over a standard row-store?

Developer Experience1/5 cited (20%)

Which cloud-native database platforms handle connection pooling best for serverless workloads with unpredictable connection spikes?

Which ORMs and query builders offer the best TypeScript experience for a distributed SQL database?

Which time-series databases have the best query authoring and debugging experience for teams coming from relational databases?

Which document databases handle schema evolution most smoothly — without requiring migration scripts for every change?

Which developer-focused databases offer the best local development experience that actually mirrors the production setup?

Integrations & Ecosystem0/5 cited (0%)

Which developer-focused database platforms integrate best with IaC tools so database provisioning and config can be version-controlled?

What tools sync data from a primary operational database to an analytics warehouse for real-time reporting without heavy ETL infrastructure?

Which cloud database platforms support change data capture for streaming row-level changes to a message queue or event bus with low latency?

Which managed database platforms have the best ORM and query builder compatibility for JavaScript and Python ecosystems?

Which managed database platforms make multi-cloud portability practical — so moving between cloud providers isn't a nightmare?

Performance & Reliability0/5 cited (0%)

Which managed database services offer the best backup and point-in-time recovery for production applications handling financial transactions?

Which serverless database platforms maintain the best read/write throughput under sustained load with reliable autoscaling?

What tools and benchmarks help compare database platforms for high-concurrency transactional workloads before committing to one?

Which time-series databases maintain query performance best at 10 million events per second ingestion over long retention periods?

Which distributed SQL databases handle automatic failover most reliably when a node goes down — with the fastest recovery times?

Setup & First Run0/5 cited (0%)

What's the fastest serverless relational database to spin up and connect to a Node.js backend for a new SaaS app?

Which distributed SQL platforms support migrating from a legacy relational database with minimal downtime for a production application?

I'm evaluating managed cloud databases versus self-hosted options for a seed-stage product — what should I look at?

Which developer-focused database platforms handle schema migrations with CI/CD pipeline tooling out of the box?

Which database platforms support branching so I can get a fresh isolated database copy per pull request for feature development?

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Vertical Ranking

#BrandPres.SoVDocsBlogMent.PosSentiment
1PingCAP6.7%23.0%0.7%2.0%6.0%#4.7+0.28
2CockroachDB5.3%14.9%0.7%2.7%1.3%#3.0+0.33
3Supabase4.7%16.2%4.0%0.7%18.7%#2.8+0.71
4ClickHouse4.0%12.2%0.0%0.0%10.0%#3.9+0.38
5MongoDB2.7%6.8%2.0%0.0%27.3%#2.6+0.59
6QuestDB2.7%8.1%0.0%2.7%5.3%#3.0+0.67
7PlanetScale2.0%6.8%1.3%0.0%18.0%#4.6+0.82
8Timescale1.3%2.7%0.0%0.7%0.7%#7.0+0.68
9Xata1.3%2.7%0.0%1.3%2.7%#8.5+0.60
10Fauna0.7%1.4%0.0%0.0%1.3%#1.0+0.20
11Neon0.7%1.4%0.7%0.0%22.7%#1.0+0.90
12Redis0.7%1.4%0.0%0.0%12.7%#2.0+0.00
13SingleStore0.7%1.4%0.0%0.7%5.3%#3.0+0.20
14Turso0.7%1.4%0.7%0.0%6.7%#5.0+0.85
15Gel0.0%0.0%0.0%0.0%0.0%

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