Decode vs LMCache: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Decode and LMCache — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Decode

Entropik Technologies

Freemium

A human insights platform that uses emotion AI, webcam eye tracking, and predictive models to test creative, products, and experiences before launch.

Emotion AI Measurement: Face emotion, voice emotion, and text sentiment analysis reveal how respondents actually feel during a study rather than only what they report in an answer.
Webcam Eye Gaze Tracking: Real eye tracking runs through a participant's own webcam with zero hardware, producing attention heatmaps that show where people look first and what they miss.
AI Creative Insights: Neuro AI predicts attention, emotional resonance, brand recall, and conversion impact for ad creative, packaging, OOH, and web layouts before media spend is committed.
Synthetic Audience: Build reusable synthetic personas and compare how each creative performs persona by persona ahead of fielding a study with real respondents.
AI Moderator: Runs moderated and unmoderated interviews at scale, then extracts themes, emotions, and supporting evidence from raw interview and video feedback automatically.
Shopper and Shelf Simulation: Simulates real-world shelf and pack testing with attention heatmaps, shelf visibility analysis, planogram optimization, and purchase-intent prediction.
UX Research Suite: Prototype testing, unmoderated task studies, usability and wireframe testing, card and tree sorting, and live website and app testing, each enriched with gaze and emotion data.
Global Respondent Panel: Access to more than 103 million respondents worldwide, or bring your own panel free of charge on any plan.

Pre-Flight Ad Testing: Comparing creative variations and messaging options to predict which version earns attention and recall before buying media.
Packaging and Shelf Decisions: Testing pack designs and planograms in a simulated retail environment to forecast visibility and purchase intent.
Product Concept Validation: Screening product concepts, storyboards, and innovation ideas for early-stage market fit before committing development resources.
UX Friction Discovery: Running prototype and usability studies where webcam eye tracking and emotion signals expose confusion users cannot articulate.
Qualitative Research at Scale: Using the AI Moderator to conduct and synthesize many interviews into structured themes instead of manual transcript coding.
Brand Tracking and Price Testing: Running recurring consumer studies on brand perception, pricing, and the customer journey across multiple markets.

Free

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.

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.

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.
Long-Context Agents: Accelerate agentic workloads that repeatedly process large shared context.
Enterprise-Scale Inference: Share KV caches across multiple serving instances to raise throughput in production clusters.
Cache Compression Research: Prototype custom KV compression and serialization through the pluggable SERDE interface.