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

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

LMCache

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.

FNA Technology

Paid

AI visibility platform that tracks how ChatGPT, Perplexity, Claude, Gemini and AI Overviews cite your brand, and ships fixes.

AI Citation Tracking: Continuously samples roughly 50,000 prompts per week across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews to record where and how a brand is cited.
Per-Engine Visibility Scoring: Reports a composite AI Visibility score plus share of voice and prompts won or lost, broken out engine by engine so declines can be traced to a specific model.
Prompts & Citations Explorer: Lets teams open any tracked prompt and read the actual model answer to see which competitor was named and why.
Brand Entity Analysis: Maps the adjectives each engine associates with your brand versus competitors and suggests reframing plays to change that portrait.
Competitor Intelligence: Tracks rival citation share on comparison and 'alternatives to' prompts, highlighting categories where a competitor dominates.
Prioritized Action Plan: Converts findings into P0/P1 recommendations such as schema additions or comparison pages, each with a time estimate and projected score gain.
Site Audit for AI Parseability: Audits pages for missing entity definitions, structured Q&A data and other signals that prevent models from citing the site correctly.
White-Label Reporting and API: Higher tiers add white-label client reports, SSO/SAML and API access for agencies managing multiple brands.

AI Search Monitoring: Marketing teams track whether ChatGPT and Perplexity recommend their product or a competitor on high-intent category prompts.
Competitive Benchmarking: Brands quantify how much citation share a named rival is capturing on 'alternatives to' and 'best of' queries.
Content Prioritization: Content teams decide which pages to write or refresh based on which prompts are currently missed rather than on keyword volume alone.
Technical AEO Audits: SEO specialists find pages lacking FAQ schema or entity markers that keep answer engines from parsing them.
Agency Client Reporting: Agencies run pooled prompt tracking across multiple client workspaces and deliver white-label AI visibility reports.
Executive Reporting: Operators present a weekly digest showing search clicks alongside AI citation share to explain traffic shifts leadership sees.