Customer Support
Ideal for businesses looking to enhance AI-driven customer service applications.
Skip if:
If you do not require real-time interactions.
Open source alternative to Amazon SageMaker
Last verified
Optimize LLM inference with a self-hosted KV cache layer that reuses context across vLLM deployments.
Ideal for businesses looking to enhance AI-driven customer service applications.
If you do not require real-time interactions.
Streamline processing of large volumes of documents with fast retrieval capabilities.
If your application does not involve document handling.
LMCache addresses the slow response times and high costs associated with traditional LLM applications by implementing efficient caching mechanisms.
Enable fast, uninterrupted interactions with AI chatbots by caching long conversational histories.
Enhance the speed and accuracy of RAG queries by dynamically combining stored KV caches.
Effortlessly scales without complex GPU request routing.
Reduces the cost of storing and delivering KV caches through novel compression techniques.
Seamlessly integrates with popular LLM serving engines like vLLM and TGI.
LMCache and OpenAI Prompt Caching both reduce repeated LLM prefill work, but they serve different deployment models. LMCache is for teams running their own inference stack; OpenAI Prompt Caching is a managed API feature for teams already using OpenAI models.
| Feature | LMCache | OpenAI Prompt Caching |
|---|---|---|
| License | Apache-2.0 | Proprietary API feature |
| Self-hosting | Yes, runs with open inference stacks such as vLLM | No, handled inside OpenAI API infrastructure |
| Cache scope | Persistent and tiered KV cache reuse across requests, sessions, engines, and storage backends | Automatic prompt-prefix caching for eligible API requests |
| Operations | Exposes KV cache observability, storage backends, and deployment controls | No infrastructure to manage, but no control over cache placement |
Choose LMCache when you operate self-hosted inference and need control over KV cache reuse, storage placement, observability, or multi-engine deployments. It is strongest for long-context agents, RAG, and repeated workloads where cache reuse can cut time to first token and improve throughput across your own serving fleet.
Choose OpenAI Prompt Caching when you want caching without owning inference infrastructure. It can reduce latency and input token cost automatically for repeated prompts, but it keeps the optimization inside OpenAI's managed API rather than your own deployment.
Set up LMCache in the Python environment that runs your self-hosted inference stack.
```bash
pip install lmcache
lmcache --help
```LMCache is an open-source Knowledge Delivery Network that accelerates LLM applications.
By caching key-value pairs, LMCache enables faster retrieval of information.
Yes, LMCache integrates seamlessly with popular LLM serving engines.
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