How does Redis LangCache reduce my AI API costs?

LangCache provides intelligent semantic caching. Instead of calling your LLM provider for every similar prompt, LangCache caches and retrieves responses based on semantic meaning. This can reduce API calls by up to 90% for repetitive or similar queries.

Can I run Redis on-premise or in a hybrid cloud setup?

Yes. You can use Redis Software for a fully managed, on-premise installation or a hybrid deployment. It offers the same core features as Redis Cloud, including Active-Active global distribution and auto-tiering, but with your own infrastructure management.

What is the difference between Redis Essentials and Redis Pro?

Essentials is a shared, low-cost plan for smaller workloads (250MB-100GB RAM). Pro is for production workloads, offering dedicated resources, unlimited RAM, multiple databases, and advanced features like Active-Active and Redis Flex for tiered storage.

How does Redis handle vector search for RAG applications?

Redis provides a native vector database module. You can store, index, and perform high-speed similarity searches on vector embeddings. This is used to retrieve the most relevant context (documents, data) to ground LLM responses and build Retrieval-Augmented Generation (RAG) pipelines.

What is Redis Flex and when should I use it?

Redis Flex is an auto-tiering architecture that uses both RAM and flash storage. It allows you to store terabytes of data at up to 80% lower cost than all-RAM, while maintaining high performance for the 'hot' data in RAM. It's ideal for large datasets that need Redis speed without the prohibitive cost of all-RAM storage.

How can I migrate data into Redis from other systems?

Redis provides tools like RIOT (Redis Input/Output Tools) for data migration and Redis Data Integration (RDI) for continuously ingesting live data from databases like PostgreSQL, MySQL, and Kafka into Redis using Change Data Capture (CDC).

Directory/Productivity/Databases/Redis

Redis

Redis is an open-source, in-memory data store used as a database, cache, and message broker. Its core strength is delivering sub-millisecond latency for high-performance applications. For AI and ML, it functions as a real-time context engine, offering vector search, semantic caching (LangCache), and agentic memory to reduce hallucinations and token costs. Key workloads include caching, session management, leaderboards, real-time analytics, and fraud detection. It is deployed via Redis Cloud (managed), Redis Software (on-prem), or Redis Open Source.

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Added on

Sep 22, 2026

In-memory DBVector searchReal-time AI contextSemantic cachingActive-Active geo-distributionMulti-cloud support450+ commands

Product information

Everything worth knowing about Redis

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Real-time Context Engine for AI

Provides real-time context (memory, retrieval, state, streaming) for AI agents and applications, solving hallucination and inconsistency issues.

Semantic Caching (LangCache)

Intelligent caching that saves on API token costs and shortens response times by caching based on meaning, not exact matches.

Vector Search & Retrieval

Retrieve the most relevant data from structured and unstructured sources instantly with vector search for AI models.

Unified Platform for AI Apps

Work from any stack with one platform for real-time agents, combining data structures, search, and caching.

Technical capabilities

Multi-Model Data Structures

Supports 450+ commands and data types including JSON, Streams, and Pub/Sub for complex application needs.

Active-Active Global Distribution

Multi-region replication for local-speed reads and zero-downtime failover, crucial for global applications.

Must watch videos

Inside the state of context engineering 2026

18:14

Tutorial18:14

Inside the state of context engineering 2026

AI agents are only as powerful as the context they can access. Join Redis CTO Benjamin Renaud and Head of AI Products Simba Khadder for a live conversation on the state of context engineering. Drawing on new research from more than 1,000 IT and AI infrastructure leaders, they’ll unpack why 97% say context is critical—but only 4% have built infrastructure where it compounds. You’ll hear who should own context inside an engineering organization, why MCP went from overhyped to unavoidable, and what compounding context looks like in practice. Live August 27. Read The state of context engineering 2026 report. https://redis.io/resources/state-of-context-engineering-2026/ #Redis #AI #AIAgents #ContextEngineering #MCP #AIInfrastructure #AgentMemory

What’s new in two: Redis Data Integration, Flex updates, and more

2:18

Tutorial2:18

What’s new in two: Redis Data Integration, Flex updates, and more

What’s new in two May 2026: Redis Iris, Redis 8.8, PrivateLink & more

2:56

Tutorial2:56

What’s new in two May 2026: Redis Iris, Redis 8.8, PrivateLink & more

What you’ll learn in this video Catch up on the latest Redis updates, including Redis Iris for AI agents, Redis Open Source 8.8, Redis Data Integration 1.18, PrivateLink GA, and dynamic endpoints in Redis Cloud. Overview Welcome to What’s New in Two — your fastest way to catch up on everything happening at Redis. In this May update, we cover Redis Iris, our new real-time context engine for AI agents, built to help agents retrieve fresh, live, agent-ready context. We also walk through what’s new in Redis Open Source 8.8, including performance gains, improved vector storage efficiency, the new Array data structure, built-in window-counter rate limiting, hash subkey notifications, and more. You’ll also get updates on Redis Data Integration 1.18, including a new Flink-based processor and preview support for Snowflake as a source, plus new Redis Software authentication support, PrivateLink GA in Redis Cloud Pro, and dynamic endpoints for easier database migrations across Redis Cloud. Key topics covered - Redis Iris, the real-time context engine for AI agents - Redis Context Retriever, Agent Memory, RDI, LangCache, and Redis Search - Redis Open Source 8.8 performance and feature updates - Redis Data Integration 1.18 with Flink-based processing - Snowflake source support and reverse ETL workflows - Certificate-based authentication with LDAP and AD - PrivateLink GA in Redis Cloud Pro - Dynamic endpoints for Redis Cloud migrations Resources & links - Redis Iris: https://redis.io/iris/ - Redis Iris launch blog: https://redis.io/blog/context-is-all-you-need/ - Redis Open Source 8.8 release notes: https://redis.io/docs/latest/operate/oss_and_stack/stack-with-enterprise/release-notes/redisce/ - Redis Data Integration 1.18 release notes: https://redis.io/docs/latest/integrate/redis-data-integration/release-notes/rdi-1-18-0/ - Redis Software 8.0.20-19 release notes: https://redis.io/docs/latest/operate/rs/release-notes/rs-8-0-releases/rs-8-0-20-19/ - AWS PrivateLink for Redis Cloud: https://redis.io/docs/latest/operate/rc/security/aws-privatelink/ - Dynamic endpoints: https://redis.io/blog/dynamic-endpoints-migrate-databases-without-changing-your-endpoint/ - Subscribe to Redis: https://www.youtube.com/@Redisinc?sub_confirmation=1 Timestamps 00:00 - Intro 00:05 - Redis Iris for AI agents 00:56 - Redis Open Source 8.8 01:30 - Redis Data Integration 1.18 01:59 - Certificate-based authentication 02:12 - PrivateLink GA in Redis Cloud Pro 02:23 - Dynamic endpoints in Redis Cloud 02:45 - Wrap-up Have questions? Drop them in the comments — we’re here to help. Subscribe for the next Redis update: https://www.youtube.com/@Redisinc?sub... #Redis #RedisCloud #RedisIris #AIagents #Redis8 #VectorSearch #RDI #PrivateLink #DataIntegration #DeveloperTools About Redis We’re the world’s fastest in-memory database. From our open source origins in 2011 to becoming the #1 cited brand for caching solutions, we’ve helped more than 10,000 customers build, scale, and deploy the apps our world runs on. With cloud and on-prem databases for caching, vector search, and more, we’re helping digital businesses set a new standard for speed.

Who uses it

Real use cases, no hype

Real-Time AI Agent Context

Provide instant, low-latency memory and state for AI agents and conversational systems.

Semantic Caching for LLMs

Cache and retrieve AI model responses based on meaning, drastically cutting API costs and latency.

High-Performance Caching

Sub-ms read/write for databases, APIs, and web applications to boost overall performance.

Real-Time Messaging & Streaming

Power event-driven architectures with Redis Streams for low-latency pub/sub and data pipelines.

Scalable Session Management

Fast, persistent storage for user sessions and authentication tokens across distributed systems.

Vector Search & RAG

Implement retrieval-augmented generation (RAG) and semantic search with integrated vector database capabilities.

Real-Time Leaderboards & Analytics

Track scores, rankings, and metrics with instant updates for gaming, e-commerce, and live apps.

What's great

Sub-millisecond latency for real-time caching and data retrieval.
Proven at massive scale (10B+ Docker pulls, used by 100+ Fortune 500 companies).
Versatile: handles caching, messaging, session management, and vector search.
Excellent for AI/ML workloads with native vector search and LangCache for token optimization.
Fully managed cloud option (Redis Cloud) or flexible self-hosting (Redis Software/Open Source).

Technical strengths

Active-Active global distribution for zero-downtime failover and multi-region replication.
Supports auto-tiering with Redis Flex, using RAM + flash storage for cost-effective scaling.

Where it falls short

Pricing can be complex, especially with usage-based data transfer and minimum spend on Pro plans.
Self-hosting requires significant operational expertise for high availability and scaling.
The free tier is very limited (30 MB, best-effort SLA) and not suitable for production.
Advanced features like Active-Active are only available on the enterprise Pro plan.
Learning curve for advanced data structures like Streams and JSON beyond basic key-value operations.

Technical limitations

Memory footprint can be high for large datasets; requires careful tuning of eviction policies.
While multi-cloud is supported, the deepest integrations are with specific partner marketplaces (AWS, GCP, Azure).

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

Redis Iris is a new platform layer built on top of Redis, specifically designed as a real-time context engine for AI applications. It integrates features like LangCache, Context Retriever, and Agent Memory to provide a unified solution for agentic systems, moving beyond traditional caching and data structures.