Engineering & Developermemory infrastructure for AI SaaS

Run AI SaaS on Memory Infrastructure That Scales With You

Every AI SaaS product reaches the same crossroads: build memory infrastructure in-house or stop pretending users are remembered. MemoryLake gives AI SaaS teams a memory layer that scales to millions of users, swaps models without re-platforming, and ships with the compliance certs enterprise buyers demand.

Get Started Free

Free forever · No credit card required

The problem: in-house memory infrastructure becomes the bottleneck

By month six, your AI SaaS has a memory subsystem nobody owns, a vector store that costs more than your model bill, and three engineers debugging dedupe edge cases instead of shipping features. Memory infrastructure for AI SaaS is plumbing — it shouldn't be your moat.

How MemoryLake solves AI SaaS memory infrastructure

One layer, six memory types

Stop maintaining parallel systems for user state, chat history, and document context. One API serves them all.

Compliance-ready from day one

ISO 27001, SOC 2 Type II, GDPR, and CCPA certified. Pass enterprise security reviews without building your own audit logs.

Per-tenant isolation

Memory is namespaced per user, per workspace, per tier. Fine-grained access scopes match your SaaS pricing model.

Cross-model so your moat doesn't depend on the LLM provider

Your product survives any model swap. Users keep their state when you switch vendors.

Get Started Free

Free forever · No credit card required

How it works for AI SaaS products

Connect — Drop the SDK into your backend. Use API keys per tenant.
Structure — Every user interaction, document upload, and agent run flows into the right memory type.
Reuse — Retrieve at inference. Export memory on user request (one click — GDPR-ready).

Before vs. after: AI SaaS memory stack

	Without MemoryLake	With MemoryLake
Memory infra owned by	Two engineers full-time	Vendor-managed
Enterprise security review	Block on building audit logs	Ship existing certifications
Switching the underlying LLM	Re-platform user data	Memory is model-agnostic
Per-tenant memory isolation	Hand-rolled	Native to the API

Who this is for

Founders and engineering leaders at AI SaaS companies past the prototype stage — when memory plumbing is starting to consume more roadmap than features, and enterprise customers are asking for compliance attestations.

Runnable demoabout 5 minutes

Try this use case on your own account

An AI meeting-notes SaaS gives every customer team its own MemoryLake project, created by its provisioning code and named after the account id. What the customer said at signup, "keep every metric reading we say", goes into the project description. Then the team's eval syncs flow in, and extraction often keeps nothing from a whole meeting of bare readings. The description changes nothing because extraction never reads it. The same sentence set as the project's fact instruction keeps most of them, though not every meeting.

What you will see

One tenant project provisioned three ways, all named acct_7f3a91c2: A the project only · B plus the signup note as --description · C plus the same note as fact instruction set --project
The same three eval syncs go into each: twelve bare metric readings ("Recall at 10 for the new reranker came in at 0.81.") and three action items, as GROUP conversations
Over 6 runs, readings kept: A 5/72 · B 9/72 · C 60/72. The meeting assistant (router chat --boundary, gpt-4o-mini) answered the readings: A 10/144 · B 18/144 · C 120/144
A per-meeting census flags meetings that left nothing behind. memory and ask subcommands and a local web UI

You need: a MemoryLake API key · the memorylake CLI · Python 3.9+

Run it

git clone https://github.com/memorylake-ai/memorylake-usecases.git
cd memorylake-usecases/provisioned-tenant-memory
export MEMORYLAKE_API_KEY=sk-…
python3 demo.py          # or: python3 web/server.py

Open on GitHub Read the walkthrough