Built-in tenant virtualization
Native tenant data isolation
100% secure with no cross-tenant access
No more struggle with row level security
Securely share data across tenants using shared tables
Tenant level backups
Instant restoration from backups for your customers
No hacky or buggy scripts to restore specific customers
Manage different backup strategies per tenant
Performance isolation
across tenants
across tenants
Hot tenants have no impact on other tenants
Performance insights per tenant
Predictable performance for each tenant
Drop-in tenant and user management
Tenant-level user authentication and authorization controls
Manage organizations, user invites, and multiple membership support
Built-in basic auth, social logins, and enterprise login support
Postgres as the source of truth for user data
Onboard tenants once, place globally
Place isolated tenant databases globally with the user experience, operational simplicity, and cost efficiency of a single database
Store tenant’s data closer to their application for low latency and satisfy compliance requirements
We deploy, we route, and we manage. Seamless schema migrations, incremental rollouts, and client-side routing.
Instant customer dashboards
Track the growth of customers, users, and queries at all times
Dive into specific customers using per-tenant metrics
Manage user profiles for each tenant right from the dashboard
Configure auth settings per customer with single click
Seamless tenant-aware vector embeddings
Build your high-performance AI-native SaaS application with vector embeddings
Store your vector embeddings with your customer data, improving efficiency and performance
Use open-source pgvector for Postgres to store, index, and query embeddings for each tenant
Work with any large language models of your choice from OpenAI, Hugging Face and more
Build domain specific conversational UIs, chatbots and semantic search products
Effortlessly scale your embeddings as your AI use case grows
CREATE TABLE wiki_documents(
tenant_id uuid,
id integer,
embedding vector(3));
INSERT INTO wiki_documents (tenant_id,id, embedding)
VALUES ('018ade1a-7843-7e60-9686-714bab650998',1, '[1,2,3]');
SELECT embedding <-> '[3,1,2]' AS distance FROM wiki_documents;
Store vector embeddings per tenant or share embeddings across tenants
Deploy embeddings closer to your customers for latency or compliance needs
Index embeddings and query them efficiently
Effortlessly elastic
Truly serverless - "Think queries, not machines"
Pay for what you use
Scales to zero with instant availability
Scale to millions of tenants
Limitless connections as you grow
Postgres built for modern SaaS
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