Serverless Postgres for modern SaaS

Launch In Days. Grow For Years.

Built-In Tenant
Virtualization

Drop-In User
Management

Onboard Tenants Once,
Place Globally

Instant Customer
Dashboards

Seamless Tenant-Aware
Vector Embeddings

Effortlessly
Elastic

Backed by

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

Performance isolation across tenants

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

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

Could you share the key design decisions we made in the company last month based on our technical docs?

Sure, I can help with that. Here are the key design decisions we made last month
1. Decided to create a new region in Europe.
2. Store the logs in S3 for long term storage

Integrated with

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;