Redis
In-memory database platform for caching, real-time data and AI workloads.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- Redis Ltd.
- Entity type
- COMPANY
- Founded
- 2011
- Headquarters
- Alon 2 Tower, 32nd Floor, 94 Yigal Alon St., Tel-Aviv 6789140, Israel
- Company size
- 1,001–5,000
- Market role
- B2B SaaS Provider
- Official website
- redis.io
What Redis does
Redis creates value by providing a high-performance data layer for modern applications and monetising that utility across multiple deployment models. The open-source product drives widespread developer adoption, while paid offerings convert that demand into recurring revenue via managed DBaaS, self-managed enterprise software, premium support, and implementation services. The company also expands account value through adjacent tools such as observability, data integration, and AI-oriented semantic caching and vector capabilities.
Category differentiation
This is the infrastructure software company behind Redis database products, not a retail, adtech, or consumer internet business. It should also be distinguished from hyperscaler database services that may offer competing managed caching products.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
Redis is a private infrastructure software company best known for its in-memory data platform used in caching, streaming, session storage, real-time application workloads, and increasingly AI application memory and vector search. Its commercial portfolio includes a managed cloud service, self-managed enterprise software, developer and observability tools, data integration products, and professional services. The company primarily serves developers, platform teams, enterprise IT, SRE, data engineering, and AI engineering buyers. Redis makes money through paid cloud subscriptions and usage, enterprise software licensing and support, and consulting services, while its free open-source product broadens adoption and feeds its commercial funnel. The business model combines developer-led distribution with enterprise upsell, positioning Redis as application infrastructure rather than a consumer platform or adtech business.
Company news briefing
Briefing updated:
Following the general availability of its Data Integration service on AWS, Redis has further embedded itself as a critical layer for AI and SaaS infrastructure. Recent technical developments formalise its role in managing multi-tenant LLM rate limiting and idempotent webhook handling for reliable SaaS billing systems. These updates, alongside its continued integration into observability frameworks like Langfuse and high-performance Next.js caching architectures, reinforce Redis’s position as the primary standard for operational resilience and scalable data management in distributed, data-intensive environments.
Business model & monetisation
Redis uses a hybrid monetisation model. Open-source software is free and functions as top-of-funnel distribution. Paid revenue comes primarily from recurring subscriptions and consumption for Redis Cloud, enterprise licensing and support for self-managed Redis Software and Redis Enterprise, and additional service fees for architecture, deployment, migration, and optimisation work. Pricing evidence points to low-entry self-serve cloud plans alongside larger enterprise contracts with support and SLA components.
- Managed cloud database service
- SaaS subscription and usage-based pricing
- Self-managed enterprise software
- Enterprise licensing and support subscription
- Professional services
- Service fee / retainer
- Premium support and SLAs
- Support subscription
- Open-source distribution
- Free adoption funnel
Products & capabilities
No products with linked sources are available in this view.
Products & market categories
Competitors & alternatives
- Oracle
Enterprise cloud infrastructure and customer experience software provider.
- Hevo
No-code ELT platform for cloud data pipelines and transformations.
- MongoDB
Cloud database platform for developers, enterprises, and regulated workloads.
Side-by-side comparisons
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more
Recorded impact score: 4/5
Redis 8.10 introduces Compact Hash, JSONPath extensions, and performance improvements. The blog post is dated September 14, 2026.
FastAPI per-tenant Claude API Keys & Rate Limits
Conversational AI & Chatbots · Recorded impact score: 2/5
A technical how-to demonstrating how to use FastAPI dependency injection to create tenant-specific Anthropic Claude clients and per-tenant rate-limit buckets. The article presents a Tenant model with encrypted API key storage, an in-memory RateLimitBucket class (with a recommendation to use Redis for distributed deployments), and dependency providers (get_tenant_id, get_tenant, get_claude_client, get_rate_limit_bucket, check_rate_limit). It warns against using lru_cache for tenant lookups (stale credentials) and shows handler examples that enforce tenant isolation and rate limiting for multi-tenant LLM usage.
- Author uses FastAPI dependency injection to create tenant-specific Anthropic Claude clients and rate-limit buckets.
- Tenant model includes fields: anthropic_api_key, max_requests_per_minute, and preferred_model.
Resilient Real-Time Systems with WebSockets & Redis Pub/Sub
Infrastructure · Recorded impact score: 1/5
This technical guide explains how to build resilient, low-latency real-time systems by combining persistent WebSocket client-server connections with Redis as a central pub/sub broadcast layer, distributed state store, and cache. It describes architectural patterns for scaling (single server, multiple WebSocket servers + single Redis, and Redis Cluster), and explains when to integrate durable queues (Kafka/RabbitMQ/AWS SQS) for persistence and guaranteed delivery. The article includes a concrete Node.js example (ws and ioredis: server.js, publisher.js, client.html) and Docker, plus client reconnection best practices (exponential backoff) and session persistence in Redis to allow resuming on another instance. Operational topics covered are Redis high availability (Sentinel/Cluster), sharding and serialization, backpressure handling, load balancing, security, idempotency, and monitoring. It also discusses operational deployment patterns, monitoring metrics, and security practices for production environments.
- Combines persistent WebSocket client connections with Redis Pub/Sub as a broadcast layer and Redis as a distributed state store and cache.
- Covers scaling patterns: single server, multiple WebSocket servers + single Redis, and multiple servers with Redis Cluster for sharding and horizontal scale.
Serverless Zero-Database Web App with Client-Side Images
Web/App Development & UX Design · Recorded impact score: 1/5
A developer describes how they built Rankly, a Tier List Maker, as a stateless, zero-database web app that scales to large user counts by performing image processing and export entirely in the browser. The architecture uses the HTML5 File API and URL.createObjectURL to avoid uploads, keeps application state in local memory (React), and synthesizes high-resolution PNG exports with the HTML5 Canvas API. By serving only static HTML/CSS/JS from free CDN platforms (e.g., Cloudflare, Vercel), the approach aims to eliminate hosting costs, reduce maintenance and legal compliance burdens, and improve privacy and responsiveness compared with traditional server-based pipelines that rely on S3, databases, or headless browsers for exports.
- The author built Rankly (an online Tier List Maker) using a zero-backend, stateless frontend architecture.
- Images are handled locally via the HTML5 File API and URL.createObjectURL(file) to avoid uploading user assets to a server.
ContextOS: AST-aware Retrieval for AI in Large Codebases
Large Language Models (LLM) & AI · Recorded impact score: 2/5
The article argues that failures of AI coding assistants in large repositories are retrieval problems, not model reasoning issues. The author introduces ContextOS, a local-first context engine that preserves code structure by using Tree-sitter to extract AST-aware chunks (functions, classes, interfaces), prioritizes BM25 lexical search via SQLite FTS5 with a MiniLM ONNX fallback for semantic matching, and applies query-aware context compression. In benchmarks, ContextOS reached 98% file-level recall on 100 exact-function queries against the Redis 7.x C codebase with an average 589 tokens per query, and ~100% accuracy on React/Next.js with ~280 tokens per query. ContextOS exposes a Model Context Protocol (MCP) server and is available on GitHub.
- ContextOS is a local-first context engine for code, introduced by Siddhartha Katiyar.
- ContextOS uses Tree-sitter to perform AST-aware extraction of functions, classes, interfaces, and methods as discrete chunks.
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Questions about Redis
What is Redis?
Redis is an infrastructure software company that provides an in-memory data platform for caching, streaming, real-time workloads, and AI applications through open-source, cloud, and enterprise products.
Who uses Redis?
Redis is used by developers, startups, enterprises, platform teams, DevOps and SRE teams, database administrators, data engineers, and AI engineers building low-latency applications.
How does Redis make money?
Redis makes money through managed cloud subscriptions and usage fees, enterprise software licensing and support, and professional services, while the open-source product drives adoption.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
19 publicly documented primary sources and citations linked across the market graph.
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