Cut LLM costs. Save up to 90% with semantic caching.
90% of apps get stuck before they even launch*. Our report with MIT Technology Review Insights exposes the blockers and shows you how to move fast.
*Forbes, January 2024
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Learn why issues like AI output quality, integration complexity, and model inferencing costs are slowing —and what businesses are doing to solve them.
See how compound AI systems—built by combining multiple models or technologies—help businesses cut costs, boost performance, and make scaling AI easier.
Find out what goes into a successful GenAI stack, from integrating third-party models to using semantic caching and cutting-edge tools that can handle production demands.
65% of businesses use GenAI in at least one function, but only 5% have moved it to production.
54% of businesses use AI agents, and 32% plan to implement semantic caching soon.
32% of businesses plan to move to open source models. See why the shift is happening.
Hallucinations (72%) are the biggest concern for GenAI apps, with high costs (58%) and latency (56%) close behind.
Learn how companies are making GenAI work in practice.
Hear from top leaders like Rowan Trollope, CEO of Redis, and Harrison Chase, CEO of LangChain, on what’s next for GenAI.
See what MIT Technology Review Insights learned from surveying 250 business leaders on the state of GenAI adoption and deployment.
Redis gives you the tools and insights to help you build smarter, manage better, and scale faster. Grab the report and start building GenAI apps today.