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# Membria vs. Alternative AI Solutions

Feature Membria (Edge + Gateway) Cloud-only LLM
(GPT-4o API, Claude-Opus)		 On-Device Big-Tech
(Apple, Google Nano)		 Hybrid RAG
(LangChain + Remote)		 Private LLM Packs
(MLC LLM, PrivateGPT)
Core Approach Tiny-LLM on device + Gateway router → Cloud MoE on demand Monolithic frontier model in vendor datacenter Mid-size model compiled for phone/PC NPU Local RAG index → remote LLM for generation Full model quantised & sideloaded locally
Compute Location 90–95 % Edge, 5–10 % Cloud 100 % Cloud 100 % Edge Retrieval on Edge, generation in Cloud 100 % Edge
Model Size 3–7 B active / 50 B capacity via DoD 175–1000 B dense (or 1-2 T MoE) 7–15 B ~1–3 B retriever, 175–400 B generator 4–8 B
Context Handling Graph + CAG → “unlimited” selective retrieval Fixed 128 k window; pay per token 8–32 k Unlimited docs → must fit model window 4–8 k
Personalisation Per-user Knowledge Cache; on-device LoRA DoD Limited; shared weights Device profile only User docs in RAG; no weight change Manual fine-tune required
Privacy Data stays on device; only anonymised queries leave Prompts/CoT stored server-side On-device; closed firmware Docs local, prompts leave device Fully offline
Cost Model One-time HW + pay-per-consult (¢) $20-40 / mo + token fees Bundled in device Token fees + local infra No subscription; HW & storage
Typical Latency < 300 ms local; 1–2 s with cloud hop 1–3 s per call 200–400 ms 400 ms retrieval + 2 s gen 300–600 ms
Strengths Fast & private; no monthly fee; scales without GPU hunger Highest quality & creativity Works offline; tight OS integration Good for doc search Max privacy; no vendor lock-in
Limitations Needs Gateway orchestration; rare heavy queries still cost Expensive, privacy concerns Quality below GPT-4 class Two moving parts; privacy partial Quality below GPT-4 class

# Key Take-aways

  1. Economics flip: Membria shifts costs to the edge and turns cloud usage into a pay-per-consult micro-fee, while pure-cloud vendors fight margin erosion.
  2. Privacy by default: Only Membria and Private LLM Packs keep all user data on-device without manual setup.
  3. Latency wins UX: Sub-300 ms offline answers feel instant; cloud calls stay 1-3 s even on fast links.
  4. Future-proof routing: Gateway design lets developers mix local skills with frontier experts via a single API, no prompt-gymnastics needed.

# Memory Systems for LLM Agents

Feature Membria MCP
(Hybrid RAG/CAG + Ontology Graph)		 Mem0 Zep Asimov KCG Asimov Positron
Design Goal Local “swap-context” for tiny-LLM; keeps full user graph offline; calls cloud experts only on demand Cloud conversational memory that auto-extracts facts & CoT Low-latency memory/cache service for agents Public, verifiable ontology-driven knowledge graph Personal MCP node that builds user graph from local & social data
Storage Tier Edge SQLite / Parquet + optional Arweave; RDF/Property-Graph; LoRA patches side-by-side Redis / Postgres + vector index (cloud) Managed PG + vector (cloud) IPFS / Arweave + on-chain attestations RocksDB / LiteFS (edge)
Retrieval API CAG (exact CoT reuse) + RAG (semantic) — <1 ms /memory REST; automatic embedding search Zep-LangChain client SPARQL / GraphQL; Prompt-to-Triple gRPC / HTTP; GraphQL for triples
Context Swap Router injects distilled triples/snippets when prompt ≈ 75 % full Returns recent facts & CoT Returns summarised chat chunks External to caller LLM Same as Membria but local
Personalisation Per-user graph, on-device LoRA via DoD Tenant-scoped cloud Cloud only None (public graph) 100 % personal, optional encrypted backup
Privacy Offline-by-default Vendor cloud Vendor cloud Public graph (pseudonymous) Full offline
Latency ~0.3 s offline ~0.4 s ~0.4 s 2 s + ~0.3 s
Licensing Apache-2 core; commercial Gateway SaaS if cloud consults MIT Apache-2 GPL-3 code, Polyform data GPL-3
Strengths Fast, private, hybrid; true “RAM swap” Automatic fact extraction Ultra-fast recall Verifiable provenance Full sovereignty; zero subscription
Limitations Needs Gateway orchestration; edge disk space Central privacy risk; per-token cost Same risk; no DoD Heavy Web3 stack; latency seconds User must maintain daemon; HW limits

# Key Findings

  1. Membria MCP is the only option combining an on-device swap-context store and hybrid routing to cloud experts, minimising token overhead while retaining frontier reasoning on demand.
  2. Privacy spectrum: full offline privacy (Membria MCP, Positron) → vendor-cloud (Mem0, Zep) → public verifiable graph (Asimov KCG).
  3. Economics: Membria’s hybrid model removes recurring fees for users and slashes GPU cap-ex for providers, unlike cloud-only Mem0/Zep.
  4. Latency & UX: Membria and Positron deliver sub-300 ms offline answers; Mem0/Zep are fast but depend on connectivity; Asimov KCG suffers multi-second latency due to Web3.
  5. Developer effort: easiest drop-in: Zep/Mem0; best flexibility: Membria Gateway SDK; highest barrier: Asimov KCG & Positron.

Bottom line: For a private, cost-efficient, low-latency memory layer that still lets agents tap frontier reasoning when needed, Membria MCP is the most balanced solution in 2026.