How do their growth rates compare?

supermemory has a month-over-month growth rate of -7.7%; Treg has +223.4%.

How does pricing compare?

supermemory: Contact for Pricing. Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates. Treg: Freemium. Pay-per-call with no subscription; prices are quoted before each call and start with $1.00 of free credit. Bringing your own provider keys makes those calls unmetered.

What are alternatives to supermemory and Treg?

Other tools in related categories include OpenRouter, Composio, TinyFish, Exa, OrcaRouter.

supermemory vs Treg — Traffic (156.58K vs 452.94K), Pricing & Verdict

Open AI Tools

supermemory vs Treg — Traffic (156.58K vs 452.94K), Pricing & Verdict | Open AI Tools

Side-by-Side Traffic & Specification Matrix

Dimension / Metric	supermemory	Treg
Official Domain	supermemory.ai	treg.to
Pricing Model	Contact for Pricing	Freemium
Monthly Visits	156.58K (156,579)	452.94K (452,935)
MoM Traffic Delta	-12.98K (-7.7%)	+312.9K (+223.4%)
Primary Market & Share	🇺🇸 United States (46.2%)	🇺🇸 United States (36.8%)
Direct Visits / mo	71.16K	235.3K
User Rating	4.5	4.5
Supported Platforms	Website	Website

supermemory

A memory and continual-learning engine that gives any AI agent persistent context

learner-1 memory model

A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating.

Vector-graph memory store

Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate.

Model, harness and MCP agnostic

Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools.

Real-time context injection

Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts.

Strengths & Pros

+Memory is decoupled from model providers, so accumulated context survives model swaps
+Rich engineering evidence, including published benchmarks, latency figures and cost comparisons
+Multiple integration surfaces — REST API, SDKs, plugins and MCP
+Strong enterprise posture with SOC 2 Type II, HIPAA BAA, GDPR and air-gapped deployment options