Landscape

Cache ≠ memory.
And nobody verifies.

Researched October 2026 from docs, pricing pages, and benchmarks — not marketing.

MemflexJev (TypeSafe)Mem0PortkeyGPTCache
Answers repeats, zero LLM✅ core✅ (trained)❌ facts only✅ cached✅ cached
Calibrated confidence✅ per-reflex✅ RLCDpartial❌❌
Self-correction loop✅ verify + 👎❌❌❌❌
Cost on repeats₹0$42/B tokens$19+/mo + tokensper-token + Enterprisefree, self-run
Multilingual-first✅ 19 langs testedEN-first❌❌❌
Self-host, 1 command✅ SQLite / Postgres❌ hostedpartialpartial / Enterprise✅ + Milvus
Unseen generalization❌ → LLM✅ trained✅ LLM✅ LLM✅ LLM

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Don't take our word for it — measured.

30 questions customers actually ask — refunds, cancellations, delivery tracking — in English, German, Spanish and French, each asked 3 different ways. Result: 22 answered from memory at ₹0.

73.3%asks served from memory (22/30)
8distinct reflexes learned
~0.1msmemory path (vs seconds on LLM)
₹0.24metered savings on 30 asks

At 1 lakh asks a month with 70% repeats, that's roughly 70,000 free answers — about ₹21,000–35,000/month saved, plus instant replies instead of multi-second waits.

Honest limits: mock-backend latency flatters absolutes — the real delta is memory-path ms vs your LLM's seconds. Hit-rate follows your repeat rate; unique-every-time workloads gain nothing.

The moat is a combination, not a trick: zero-call repeats + verified memory + multilingual + free self-host. Where giants overlap, Memflex plugs in beside them.