Una IA más rápida comienza con el almacenamiento semántico en caché

AI Accelerator is a semantic caching solution for large language model (LLM) APIs used in generative AI applications. AI request handling is positioned at the edge of the network, with the platform utilizing intelligent semantic caching and optimized delivery to ensure that organizations can provide faster AI responses to users. Fewer trips to the LLM API also results in savings on token costs.

Fastly enables AI acceleration by moving AI request handling, optimization, and response delivery closer to end users. Instead of routing every individual query back to a centralized, high-latency data center or LLM provider, Fastly’s global edge network optimizes traffic flow to significantly improve throughput and reduce round-trip times. This approach is especially effective for high-volume inference workloads where even millisecond delays can degrade the user experience.

Semantic caching is a technique that identifies and reuses similar or equivalent AI responses, rather than caching only exact matches. 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. Fastly applies semantic caching at the edge to reduce redundant LLM inference calls, lower token costs, and deliver consistently faster AI responses. This is particularly valuable for chatbots and virtual assistants, code generators, content creation tools, and knowledge bases.

The most critical performance metric for AI applications is how quickly a user sees a response. Traditional LLMs are computationally expensive and slow. Using semantic caching, Fastly can identify if a new query is essentially the same as a previous one. In these cases, Fastly serves the answer directly from the edge. This reduces the latency from seconds (waiting for the LLM to generate the response) to milliseconds (serving a pre-cached response), representing a massive performance improvement for the end user.

Yes. By utilizing semantic caching, Fastly reduces the number of calls that need to reach backend LLM providers. This lowers inference costs, reduces origin load, and helps teams control spend as AI usage grows—without sacrificing response speed or user experience.

Fastly provides a high-performance delivery and optimization layer that sits seamlessly in front of an organization's existing AI infrastructure and LLM providers. Because it functions as a performance-enhancing proxy rather than a replacement for specific models, engineering teams can accelerate AI workloads without modifying their underlying frameworks, deployment pipelines, or specific model choices.

Yes. Fastly AI is built for enterprise-scale AI applications that demand reliability, security, and predictable performance. It provides the controls, observability, and scalability required by CTOs and platform leaders running AI workloads in production, while enabling faster AI experiences for end users globally.

Fastly AI is well-suited for conversational AI and customer support, AI-powered search and knowledge bases, real-time personalization and content generation, and agentic workflows. Any application where LLM performance optimization and low-latency responses are critical can benefit from Fastly’s edge-based semantic caching capabilities.