Headroom vs LMCache: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Headroom and LMCache — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
H
Headroom
Headroom
Headroom compresses tool outputs, logs, files, and RAG chunks before they reach the LLM, cutting 60-95% of tokens while preserving answers.
Key features
- SmartCrusher Compression: Statistical JSON and array compression that removes 70-90% of tokens from tool outputs.
- AST-Aware Code Compression: Uses tree-sitter analysis to compress source code while preserving structure.
- Text & Log Compression: Shrinks search results, build logs, and diffs before they hit the model.
- Compress-Cache-Retrieve: Reversible compression where originals are never deleted and the LLM can retrieve full content on demand.
- Multiple Integrations: Ships as a Python package, a TypeScript package, an OpenAI/Anthropic-compatible HTTP proxy, and an MCP server.
Best for
- Cost-Efficient Agents: Cut token spend on agents that read large tool outputs and logs.
- RAG Pipelines: Compress retrieved chunks before they enter the prompt to fit more context.
- Drop-In Proxy: Route OpenAI/Anthropic traffic through the proxy to compress payloads with no code changes.
- MCP Workflows: Add compression and retrieval tools to MCP-based agent stacks.
L
LMCache
LMCache
LMCache is an open-source KV cache layer that speeds up LLM inference by storing and reusing KV caches across GPU, CPU, disk, and S3.
Key features
- KV Cache Reuse: Stores KV caches of reusable text across the datacenter so prefixes are not recomputed across requests or serving engines.
- Multi-Tier Storage: Persists caches across GPU, CPU, local disk, and S3 with acceleration techniques like zero CPU copy, NIXL, and GDS.
- vLLM Integration: Combines with vLLM to deliver 3-10x reductions in delay and GPU cycles for multi-round QA and RAG workloads.
- Pluggable KV Transformation: A flexible SERDE interface lets researchers add compression, token dropping, and custom serialization.
- Vendor-Neutral Layer: Works as a KV cache layer across mainstream serving engines, inference frameworks, hardware vendors, and storage systems.
- Faster Time-to-First-Token: Cuts TTFT and improves throughput for long-context, agentic, and knowledge-augmented workloads.
Best for
- Retrieval-Augmented Generation: Reuse cached document prefixes to cut latency and GPU cost in RAG pipelines.
- Multi-Turn Conversations: Avoid recomputing conversation-history KV caches across turns in chat applications.