Build Multi-Tenant Agent Platforms on Memory Sharding That Actually Isolates
Multi-tenant agent platforms — agents-as-a-service, vertical AI SaaS, white-label agent products — need strict memory isolation per tenant plus the ability to scale beyond a single shard. MemoryLake provides per-tenant memory namespaces with sharded storage and zero cross-tenant leakage.
Build Multi-Tenant Agent Platforms on Memory Sharding That Actually Isolates
Get Started FreeFree forever · No credit card required
The problem: multi-tenant agent memory is hard to isolate and scale
Tenant A's memory must never appear in Tenant B's context. The compliance team requires demonstrable isolation. Scale grows beyond what a single memory store can handle. DIY multi-tenant memory typically gets isolation or scale — not both.
How MemoryLake supports multi-tenant memory sharding
Per-tenant namespaces with strict isolation
Default-deny across tenants.
Sharded storage at scale
Tenants distributed across shards transparently.
Per-tenant compliance posture
Audit trail, encryption, deletion per tenant.
Zero cross-tenant leakage
Enforced at retrieval boundary.
Free forever · No credit card required
How it works for multi-tenant agent memory
- Connect — Each tenant gets a namespace; auth scoped per tenant.
- Structure — Memory writes route to tenant shard automatically.
- Reuse — Tenant queries return only their memory.
Before vs. after: multi-tenant agent memory
| DIY multi-tenant | MemoryLake | |
|---|---|---|
| Cross-tenant isolation | Custom controls | Default-deny |
| Sharded scale | Build it yourself | Built in |
| Per-tenant compliance posture | Custom | Native |
| Audit per tenant | Limited | Full per-tenant trail |
Who this is for
Engineering teams building multi-tenant AI products — agents-as-a-service, vertical AI SaaS, white-label agent platforms — where tenant isolation is non-negotiable and scale is a real concern.
Try this use case on your own account
Relaybench hosts one support agent for three companies, and each tenant gets its own MemoryLake workspace with its own project, documents, conversations and memories. The demo asks the same question in all three and counts every tenant's marker words in every hit, shows one customer of two tenants with two separate memories, has one tenant name another tenant's data, and offboards a tenant with `workspace delete`.
What you will see
- A 3 × 3 leak matrix: the same question inside each workspace, facts and document paragraphs scanned for every tenant's marker words. Each tenant finds its own answer; cross-tenant hits: 0
- One person, two tenants: Sam has one actor id bound to two workspaces.
fact list --actorsinside each returns a different memory (SMS only vs. weekday phone calls), with 0 shared fact ids - Default-deny: naming another tenant's project or customer is refused, 4 of 4 (
does not belong to workspace,is not bound to workspace); a request mixing allowed and foreign projects is refused whole - Offboarding:
workspace deleterefuses a wrong--confirmname, then removes the tenant's memories in about a second while the other two keep the same fact ids. The tenant's project (its documents still downloadable) and boundary stay reachable through the deleted workspace's id, and its Library folder and user actor are team-level: the demo lists all four and deletes them. Plus 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/tenant-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Related use cases
Frequently asked questions
Tenant count scale?
Tenant count scale?
Tested with thousands of tenants per workspace.
Per-tenant compliance reports?
Per-tenant compliance reports?
Available for enterprise tier.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.