--- title: "MotherDuck Postgres Endpoint: Live Demo and Walkthrough | MotherDuck" description: "MotherDuck's Postgres endpoint lets any Postgres client, driver, or BI tool query your data warehouse without installing a DuckDB library." canonical: "https://motherduck.com/videos/motherduck-postgres-endpoint-demo/" --- [BACK TO VIDEOS](/videos/) [Webinar](/videos/?category=Webinar#videos-and-webinars-library)[MotherDuck Features](/videos/?category=MotherDuck%20Features#videos-and-webinars-library)[Ecosystem](/videos/?category=Ecosystem#videos-and-webinars-library)[SQL](/videos/?category=SQL#videos-and-webinars-library) # MotherDuck Now Speaks Postgres: Fast Analytics Without Changing Your Stack 2026/04/21Featuring: [Garrett O'Brien](/authors/garrett-obrien/) , [Louisa Huang](/authors/louisa-huang/) > TL;DR: MotherDuck's Postgres endpoint lets any Postgres-compatible client, driver, or BI tool query your MotherDuck database directly without a DuckDB library. Louisa Huang and Garrett O'Brien walk through the architecture, run live demos, and share connection pooling tips. ## What the Postgres endpoint actually is The Postgres endpoint is a translation layer that makes MotherDuck speak the Postgres wire protocol. Your application connects with a standard Postgres driver like `node-postgres` or `JDBC`, but execution still happens server-side on MotherDuck using DuckDB SQL. You keep your existing connection pooler and query patterns; MotherDuck handles the analytical work. ## Why this matters if you're running analytics on Postgres If your analytical queries are competing with transactional traffic on the same Postgres cluster, this gives you a way to split them apart. Analytical workloads route to MotherDuck through the same Postgres drivers your app already uses, so your Postgres instance stays lean. It also works in runtimes that couldn't load a DuckDB client before: Cloudflare Workers, Vercel Serverless Functions, AWS Lambda. ## Live demo: NYC taxi dashboard on Vercel Louisa builds a working NYC taxi dashboard with Next.js, Vercel, and `node-postgres` pointed at a free MotherDuck account on a Pulse instance. Every chart fires a query through the Postgres endpoint, and the dashboard stays responsive while aggregations run across millions of rows. ## Connection pooling and read scaling Connection pooling works the same way it does with a real Postgres database. Open connections at server start, reuse them. A pool size of 10 to 100 handles most applications. If you need higher concurrency, swap your regular MotherDuck token for a Read Scaling token. Each pooled connection gets routed to a separate ducklink, so you get horizontal scaling without changing application code. ## When you still want the DuckDB client The Postgres endpoint makes sense for embedded analytics, BI integrations, and serverless workloads. For exploratory analysis, bulk ingestion with the Appender API, or anything that benefits from [DuckDB-Wasm in the browser](/blog/duckdb-wasm-in-browser/), the DuckDB client is still the better fit. The [MotherDuck getting started guide](/docs/getting-started/) walks you through setting up a free workspace to try both. TABLE OF CONTENTS - What the Postgres endpoint actually is - Why this matters if you're running analytics on Postgres - Live demo: NYC taxi dashboard on Vercel - Connection pooling and read scaling - When you still want the DuckDB client Start using MotherDuck now! [Try 7 Days Free](https://auth.motherduck.com/authorize?app_source=web&response_type=code&client_id=bza3KWQpxRAFlTlRFXUo29AOg9xD7zcp&redirect_uri=https%3A%2F%2Fapp.motherduck.com%2F&state=STATE&auth_flow=signup&screen_hint=signup&ext-ph_distinct_id=c498b4a0-d718-4e41-8081-68a8208ca03b) ## FAQS ### What is the MotherDuck Postgres endpoint? The Postgres endpoint is a translation layer that lets MotherDuck speak the PostgreSQL wire protocol. You connect with any Postgres-compatible driver like `node-postgres`, `JDBC`, or `rust-postgres`, and MotherDuck handles the type and metadata conversion. Queries still run on MotherDuck's server-side DuckDB instances, so you get DuckDB performance without replacing your existing Postgres tooling. ### Can I use my existing Postgres client with MotherDuck? Yes. Popular drivers like `node-postgres`, `JDBC`, `rust-postgres`, and `psycopg` all work with the endpoint. You point your client at `pg..motherduck.com` on port 5432, use your MotherDuck token as the password, and connect over SSL. The wire protocol is Postgres, but the SQL dialect is DuckDB's, which is mostly compatible with Postgres SQL and has some of its own extensions. ### Does the MotherDuck Postgres endpoint support dbt? Not yet. dbt support through the Postgres endpoint is actively in development. In the meantime, you can use dbt with MotherDuck through the [dbt-duckdb adapter](/ecosystem/dbt/), which connects via the DuckDB client. ### How should I handle connection pooling with the Postgres endpoint? Use a connection pool the same way you would with a real Postgres database. Initial connections take a few hundred milliseconds for the TCP and TLS handshake and token authentication, so reusing pooled connections removes that overhead on subsequent queries. A pool size of 10 is reasonable for most applications; 100 covers higher-scale workloads. There is no hard connection limit, but standard Postgres pooling best practices apply. ### Should I use the Postgres endpoint or the DuckDB client? Use the Postgres endpoint when you're building customer-facing applications, connecting BI tools, or running in serverless environments like Cloudflare Workers or Vercel that can't load native DuckDB libraries. Use the DuckDB client for exploratory analysis, bulk data ingestion using the Appender API, or when you want [DuckDB-Wasm in the browser](/blog/duckdb-wasm-in-browser/) for ultra-low-latency interactions. ## Related Videos [58:27](/videos/ducklake-time-travel-schema-evolution/)[2026-09-03](/videos/ducklake-time-travel-schema-evolution/) ### [Time Travel & Schema Evolution: DuckLake Handles Change](/videos/ducklake-time-travel-schema-evolution/) Learn how DuckLake time travel, schema evolution, and the change feed work: query any snapshot, undo bad writes, and evolve tables without rewrites. Stream SQL Ecosystem [60:29](/videos/ai-agents-semantic-layer/)[2026-08-26](/videos/ai-agents-semantic-layer/) ### [Do AI Agents Need a Semantic Layer?](/videos/ai-agents-semantic-layer/) Do AI agents need a semantic layer? Evals comparing plain-text context with a Malloy semantic layer show where each wins, and what it costs. Stream AI, ML and LLMs BI & Visualization SQL [63:17](/videos/agentic-data-engineering-stack/)[2026-08-18](/videos/agentic-data-engineering-stack/) ### [Beyond Copilots: We're Building a Data Stack Live with AI Agents](/videos/agentic-data-engineering-stack/) Agentic data engineering in practice: AI agents build dlt ingestion into MotherDuck and a governed Lightdash semantic layer, live in an hour. Stream AI, ML and LLMs Data Pipelines Ecosystem [View all](/videos/)