For developers building applications that need fast data access, Redis is an in-memory data platform that also functions as a vector database, letting teams cache, queue, and run AI retrieval workloads from one system.
Presence & Market Position
#34 of 103
GTM Developer Tools
▲ 94 60-day move
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Redis is positioning itself as the real-time context layer for AI agents, moving from cache database toward what it calls a context engine that keeps agent memory and retrieval current at low latency. Conversation about Redis is dense and practitioner-driven: tech-stack rundowns list it as default infrastructure alongside Postgres and Kafka, tutorials walk through building agent memory and retrieval systems with its vector library, and migration write-ups report latency and cost gains after adopting it for caching and search. Ecosystem tooling built on top extends it into managed search and serverless deployment, and conference presence keeps its AI tooling visible to developer audiences. That mix points to rising momentum in the AI-infrastructure conversation layered onto an established caching footprint.
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Business Profile
Founded in 2011, Redis has scaled to roughly 1,500 employees and raised $347M over its history, reaching a $2B valuation in 2021 and reporting $300M+ in ARR as of January 2026. The company remains independent, led by CEO Rowan Trollope. In 2025 Redis returned to a fully open-source license and acquired real-time data platform Decodable, its second acquisition after Speedb in 2024, extending its context and memory capabilities for AI agents. The record shows a mature, revenue-generating infrastructure company still expanding through acquisition and licensing shifts as it competes for position in the AI workload layer.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Redis has built a dedicated agent stack under its Iris context engine: Redis Data Integration keeps source data fresh, Agent Memory extracts and recalls session and long-term facts via vector search, LangCache caches repeated LLM calls to cut token costs, and Redis Search powers the real-time queries underneath. These ship via REST API, Python SDK, and an MCP server, with an open-source Agent Memory server alongside managed Redis Cloud options.
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