AI visibility report for Redis
Vertical: Databases & Data Infrastructure
AI search visibility benchmark across 5 platforms in Databases & Data Infrastructure.
Presence Rate
Top-3 citations across 125 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Google AI Mode
Gemini Search
ChatGPT
Perplexity
GrokOverview
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
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
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 $
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
How AI describes Redis3
...loyments | | Vespa | Combines vector search, ranking, and traditional search | Large search and recommendation platforms | | Redis | Fast in-memory operations, caching plus vectors | Low-latency applications | | Elastic | Combines keyword, semantic, and...
What are the best dedicated vector databases, and how do they compare to adding vector search extensions to an existing relational database?
Redis ----------------------------------------------------- For caching and data structures: * Local Redis is usually identical to production Redis.
Which developer-focused databases offer the best local development experience that actually mirrors the production setup?
4. Redis * Can create snapshots via RDB/AOF persistence, but not really “branching”.
Which database platforms support branching so I can get a fresh isolated database copy per pull request for feature development?
Most cited sources4
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.
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 Coverage
Prompt-Level Results
| Prompt | ChatGPT | Perplexity | Gemini Search | Grok | Google AI Mode |
|---|---|---|---|---|---|
Capability1/5 cited (20%) | |||||
What are the best dedicated vector databases, and how do they compare to adding vector search extensions to an existing relational database? | |||||
Which managed database platforms offer the best multi-region replication with automatic conflict resolution for write-write scenarios? | |||||
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 columnar databases handle mixed OLAP and OLTP workloads well — when does it make sense to use one over a standard row-store? | |||||
Developer Experience2/5 cited (40%) | |||||
Which developer-focused databases offer the best local development experience that actually mirrors the production setup? | |||||
Which document databases handle schema evolution most smoothly — without requiring migration scripts for every change? | |||||
Which time-series databases have the best query authoring and debugging experience for teams coming from relational databases? | |||||
Which ORMs and query builders offer the best TypeScript experience for a distributed SQL database? | |||||
Which cloud-native database platforms handle connection pooling best for serverless workloads with unpredictable connection spikes? | |||||
Integrations & Ecosystem0/5 cited (0%) | |||||
What tools sync data from a primary operational database to an analytics warehouse for real-time reporting without heavy ETL infrastructure? | |||||
Which developer-focused database platforms integrate best with IaC tools so database provisioning and config can be version-controlled? | |||||
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%) | |||||
What tools and benchmarks help compare database platforms for high-concurrency transactional workloads before committing to one? | |||||
Which managed database services offer the best backup and point-in-time recovery for production applications handling financial transactions? | |||||
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? | |||||
Which serverless database platforms maintain the best read/write throughput under sustained load with reliable autoscaling? | |||||
Setup & First Run0/5 cited (0%) | |||||
Which distributed SQL platforms support migrating from a legacy relational database with minimal downtime for a production application? | |||||
What's the fastest serverless relational database to spin up and connect to a Node.js backend for a new SaaS app? | |||||
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? | |||||
Strengths3
What in-memory caching tools integrate best with persistent databases — and which are worth adding versus just optimizing primary database queries?
Avg # 1.0 · 1 platform
Which developer-focused databases offer the best local development experience that actually mirrors the production setup?
Avg # 5.0 · 1 platform
Which time-series databases have the best query authoring and debugging experience for teams coming from relational databases?
Avg # 14.0 · 1 platform
Gaps5
Which distributed SQL databases handle automatic failover most reliably when a node goes down — with the fastest recovery times?
Competitors on 3 platforms
Which managed database platforms make multi-cloud portability practical — so moving between cloud providers isn't a nightmare?
Competitors on 3 platforms
Which database platforms support branching so I can get a fresh isolated database copy per pull request for feature development?
Competitors on 3 platforms
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 2 platforms
What are the best dedicated vector databases, and how do they compare to adding vector search extensions to an existing relational database?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | PingCAP | 12.0% | 27.0% | 0.8% | 4.8% | 8.8% | #8.0 | +0.22 |
| 2 | Cockroach Labs | 8.0% | 22.0% | 2.4% | 4.0% | 4.8% | #10.6 | +0.16 |
| 3 | Supabase | 6.4% | 10.0% | 1.6% | 0.8% | 6.4% | #16.2 | +0.38 |
| 4 | ClickHouse | 5.6% | 8.0% | 0.8% | 0.0% | 5.6% | #11.5 | +0.00 |
| 5 | PlanetScale | 4.0% | 5.0% | 3.2% | 0.0% | 4.0% | #4.8 | +0.34 |
| 6 | Xata | 2.4% | 5.0% | 0.0% | 2.4% | 2.4% | #4.2 | +0.30 |
| 7 | MongoDB | 2.4% | 8.0% | 0.8% | 0.0% | 2.4% | #6.5 | +0.27 |
| 8 | SingleStore | 2.4% | 3.0% | 1.6% | 0.8% | 2.4% | #8.7 | +0.03 |
| 9 | Redis | 2.4% | 5.0% | 0.0% | 2.4% | 2.4% | #9.0 | +0.17 |
| 10 | Neon | 2.4% | 3.0% | 1.6% | 0.8% | 2.4% | #9.3 | +0.00 |
| 11 | QuestDB | 2.4% | 3.0% | 0.0% | 1.6% | 2.4% | #19.3 | +0.00 |
| 12 | Timescale | 0.8% | 1.0% | 0.0% | 0.8% | 0.8% | #21.0 | +0.00 |
| 13 | EdgeDB | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 14 | Fauna | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 15 | Turso | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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