LMCache vs Proxon

LMCache and Proxon 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.

Proxon

Proxon surfaces AI activity across teams into a single operating record: which tools and agents exist, who owns them, what they cost, and whether they're producing results. The attribution layer connects token spend to specific teams and workflows rather than burying it in an undifferentiated API bill. Adoption tracking lets leadership identify high-performing workflows and push them to teams still running ad hoc. The ceiling appears when you need deep integrations with proprietary internal tooling — the vendor page describes discovery and governance but does not detail connector coverage, so teams with niche or self-built stacks will need to validate fit before committing.

Attribute	LMCache	Proxon
Pricing	Free	Paid
Free trial	No	No
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	No
Platforms	Cross-platform; integrates with vLLM, TGI, SGLang	Web
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.	Single inventory of all AI tools, agents, and workflows across the organization, so leadership stops discovering production AI systems by accident during an incident or audit. Spend attribution down to the team, workflow, and model level, which means the monthly API bill becomes an actionable breakdown instead of a number nobody can explain. Built-in optimization signals that flag redundant model work and suggest caching or routing changes, so engineering doesn't need a separate cost analysis pass to find waste. Adoption trend tracking by team, which means high-performing workflows can be identified and pushed to underperforming teams rather than staying siloed with whoever built them first. API access for exporting governance and cost data, so Proxon can feed into existing dashboards or alerting pipelines rather than requiring a separate login for every stakeholder.
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 vendor page does not enumerate which AI platforms, model providers, or agent frameworks Proxon connects to natively. Teams running self-built or niche tooling will find gaps in the discovery layer — activity that runs outside supported connectors stays invisible, which defeats the core premise of a complete inventory. No self-hosted deployment option is described. Organizations in regulated sectors where activity metadata cannot leave their own infrastructure hit a hard architectural wall here, and at that point the evaluation moves to purpose-built on-premises governance tools regardless of what Proxon offers. Policy enforcement and approval flows are listed as capabilities but the vendor page does not show the depth of the rules engine. Teams that need granular, conditional access controls — for example, blocking specific models for specific data classifications — will need to pressure-test whether Proxon's governance layer goes deep enough or whether they need a dedicated AI access control product alongside it.

Bottom line

LMCache is free while Proxon is paid; LMCache is open source; only Proxon exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between LMCache and Proxon?

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

Is LMCache better than Proxon?

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.