LLM API costs can quickly become prohibitive at scale, making innovative AI applications financially unsustainable.
Response times of several seconds create poor user experiences and limit real-time AI applications.
Users ask the same questions with different phrasing, but traditional caches miss these semantic similarities.
A transparent proxy that sits between your app and LLM providers, understanding the semantic meaning of queries to deliver intelligent caching.
Your app sends requests to Vectorcache instead of directly to the LLM provider.
We convert your query into vector embeddings to understand its semantic meaning.
Our similarity search finds semantically similar cached responses in milliseconds.
Cache hits return in <50ms. Cache misses forward to LLM and store the response.
Change your endpoint URL and you're done. No complex setup or vector database management required.
Fine-tune similarity thresholds to balance cache hit rates with response accuracy for your use case.
Monitor cache performance, cost savings, and latency improvements with detailed dashboards.
Industry-standard encryption, data isolation, and compliance with strict security policies.
Works with OpenAI, Anthropic, Google, and other major LLM providers out of the box.
Handles conversational context to ensure cached responses remain accurate and relevant.
Handle repetitive questions instantly with semantic understanding of user intent.
Speed up content creation tools by caching similar creative requests and prompts.
Accelerate development workflows with cached responses to common coding questions.
Provide instant answers to frequently asked educational questions and concepts.
Be among the first to experience the future of LLM caching. Get early access and exclusive updates.