What does it all mean? An introduction to semantic caching and Fastly’s AI Accelerator
Head of Product, Developer Experience, Fastly
Caching has been around for decades, and it has played a crucial role in improving performance and scalability across various applications and platforms. But with the advent of AI, data context is significantly more complex. This reintroduces challenges around performance that can slow things down for end users.
Semantic caching to the rescue
With traditional caching, you typically store the entire object or file, or the entire query even. But complex data requires smarter caching - enter semantic caching. Semantic caching stores and reuses data based on meaning, not just keywords. It breaks the query down into smaller, meaningful concepts, which can be used to understand matches against future queries — even though they are not identical, just semantically similar.
The benefits of this approach:
Improved performance: By caching responses instead of constantly hitting the original LLM API, semantic caching amps up performance and reduces query time. This translates to less waiting time for users.
Fewer tokens used: By intelligently analyzing and matching queries with cached responses, semantic caching lightens the load on LLM APIs and saves tokens used for processing.
Why semantic caching is a best practice for generative AI applications
Generative AI applications are applications that leverage large language models (LLMs) to create new content or tackle complex problems based on input data or prompts. The LLMs are essentially the engine behind the scenes powering the application. Generative AI use cases span multiple industries but currently, some of the more common ones include chatbots and virtual assistants, code generators, content creation tools, and knowledge bases.
Semantic caching is a game-changer for these types of applications and use cases because it helps generate responses to queries faster by not having to make API calls to the LLM. Not only does this greatly improve the end-user experience, but it also helps organizations save on LLM costs. Also, with reduced load on the backend LLM, it’s easier to scale the application as the number of concurrent users increases. Semantic caching is so critically important for generative AI applications that major LLM providers like OpenAI now include it in their list of best practices.
Solving generative AI application challenges with Fastly’s AI Accelerator
Fastly’s AI Accelerator is a pass-through API that makes semantic caching easy and works with your existing code. And all the benefits of semantic caching are included - improved performance for end users, and reduced costs with fewer calls to the LLM API. It even helps lower the environmental impact of those AI calls!
In case you missed our announcement, Fastly AI Accelerator is now generally available with support for major LLM providers, greater configurability to control and monitor how AI Accelerator caches your LLM responses, and best of all - it’s available for in-app purchase.
