LMCache vs PromptShark

LMCache and PromptShark are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

LMCache

The library plugs into vLLM or TGI backends and stores KV cache tensors so that overlapping prompt prefixes — system prompts, document chunks, conversation history — are served from cache on subsequent requests. The vendor states 8–10x latency improvements for prompt caching workloads and 4–10x for RAG queries where the same document chunks appear across requests. The compression and streaming techniques described in the backing research (CacheGen, CacheBlend) are what make cache delivery fast enough to beat recomputation. The ceiling appears when your workload has little prompt overlap — unique user queries with no shared prefix — at which point the cache layer adds infrastructure without meaningful savings.

PromptShark

PromptShark is a local MITM proxy written in Go and C++ that sits between your agent and any OpenAI-compatible API endpoint. Every request and response pair is captured, logged, and surfaced in a real-time dashboard — no changes to your agent code, just a single base_url swap. The loop detector flags infinite tool-calling cycles automatically. The time-travel replay feature lets you re-run or edit any historical API step without firing a live request, which means no extra token spend during debugging. The self-hosted, MIT-licensed architecture means your prompts and API keys never leave your own infrastructure.

Attribute	LMCache	PromptShark
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	Cross-platform; integrates with vLLM, TGI, SGLang	Cross-platform (Go binary + Docker)
Pros	Shared KV cache across serving instances, so you avoid GPU session-affinity routing and can load balance freely without cold-cache penalties on every node switch. KV cache compression via the CacheGen approach, so storage costs for large caches stay bounded — without this, storing full-precision KV tensors for long contexts becomes prohibitively expensive at volume. Native integration with vLLM and TGI, so teams already running those backends add the cache layer without replacing their serving stack. Designed for RAG workloads via the CacheBlend technique, which lets the system combine cached KV entries from different document chunks rather than requiring an exact prefix match — so document-heavy pipelines see cache hits even when queries draw from different combinations of stored passages. Fully open-source under Apache-2.0 with published research papers, so you can inspect the compression and streaming logic, audit it for your compliance requirements, and fork or extend it without vendor lock-in.	Zero-instrumentation integration via a single base_url swap, so you get full request visibility without touching your agent's codebase or adding an SDK dependency. Automatic infinite tool-calling loop detection, which means runaway agent cycles are flagged before they drain your token budget rather than after you read the invoice. Per-step token usage and USD cost tracking surfaced in real time, so you can attribute spend to specific agent decisions rather than receiving an opaque monthly total. Time-travel replay lets you re-run and edit any past API step without issuing a live request, so iterating on a prompt variant costs nothing in tokens during a debugging session. MIT-licensed and fully self-hostable via Docker, which means your prompts, responses, and API keys remain on your own infrastructure with no third-party data handling.
Cons	On workloads where prompt overlap is low — unique queries, varied instructions, one-shot tasks — cache hit rates fall close to zero, and you are running a distributed caching system that adds latency on misses without providing the offsetting speedup; teams in this situation remove the layer rather than tune it. LMCache has no API and requires direct integration into a vLLM or TGI deployment, so teams running other inference backends (Triton, custom serving, managed endpoints) face a porting effort the docs do not cover — at that point, teams typically stay with whatever per-request caching their serving engine natively offers. Cache invalidation for dynamic content — documents that change, system prompts that update, user context that shifts — requires manual invalidation logic that the library does not automate; teams building products where source documents update frequently report building their own staleness-tracking layer on top.	The proxy is scoped to OpenAI-compatible API calls; agents using Anthropic, Google, or other non-OpenAI-format endpoints get no coverage, and teams with a mixed provider setup gain only partial visibility. The docs describe no multi-user access controls or role separation, so the tool cannot be safely exposed across a development team without additional network-layer restrictions — teams beyond a single developer typically add a separate access layer or switch to a purpose-built observability platform. There is no alerting or notification system described in the repository; when a loop fires at 2 AM, the dashboard captures it, but nobody is paged — teams with production monitoring requirements move to a dedicated tracing platform such as LangSmith or Langfuse that integrates with existing alerting pipelines. With only 3 commits and 1 star at the time of curation, the project is early-stage; the community reports no track record of sustained maintenance, which is a real risk if you build a debugging workflow around it and a breaking API change in the upstream OpenAI spec goes unpatched.

Bottom line

Only PromptShark exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between LMCache and PromptShark?

LMCache is Free and open source, while PromptShark is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is LMCache better than PromptShark?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.