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 Rate
0.7%
Share of Voice
0.8%
Avg Position
#3.0
Docs Presence
0.0%
Blog Presence
0.7%
Brand Mentions
7.3%
Platform Breakdown
Google AI Mode
4%1/25 prompts
ChatGPT
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.
0%0/25 prompts
Gemini Search
0%0/25 prompts
Perplexity
0%0/25 prompts
Bing Copilot
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 winning
No clear strengths identified yet.
Where Redis is losing5
Which distributed SQL platforms support migrating from a legacy relational database with minimal downtime for a production application?
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.
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
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
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 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+0.8 pts
Avg positionNo trend yet
SentimentNo trend yet
How AI describes Redis3
Aiven * What it does: Manages popular open-source data technologies (PostgreSQL, MySQL, Apache Kafka, Redis, OpenSearch) across AWS, Google Cloud, Microsoft Azure, and others.
Which managed database platforms make multi-cloud portability practical — so moving between cloud providers isn't a nightmare?
google-aiDirect Redis mention
PkgPulse | Tool | Architecture | Best Use Case | Trade-offs | | --- | --- | --- | --- | | Valkey | Single/Multi-threaded I/O (Open-source Redis fork) | Drop-in replacement for standard Redis; broad ecosystem support.
What in-memory caching tools integrate best with persistent databases — and which are worth adding versus just optimizing primary database queries?
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.
Active open-source community and good documentation
Improves application latency and reduces database load
Criticized
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.
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
Brand citedCompetitor citedNot cited
Prompt
ChatGPT
Google AI Mode
Gemini Search
Perplexity
Bing Copilot
Grok
Capability0/5 cited (0%)
Which managed database platforms offer the best multi-region replication with automatic conflict resolution for write-write scenarios?
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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
...## My default ranking | Tool | Best fit with a persistent DB | Worth adding when… | My take | | --- | --- | --- | --- | | Redis / Valkey | General-purpose application cache | Lots of repeated reads, sessions, rate limits, hot objects, distributed ap...
What in-memory caching tools integrate best with persistent databases — and which are worth adding versus just optimizing primary database queries?
Google AI Mode
Gemini Search
Perplexity
Bing Copilot
Grok
