PostgreSQL AI: The Everything Database (+ Hidden 10x Performance Tricks)

This content originally appeared on DEV Community and was authored by Zahir Fahmi

PostgreSQL just ate everyone’s lunch. Here’s proof:

The Numbers Don’t Lie

• 49% developers switched from MySQL (Stack Overflow 2024)
• 70% infrastructure cost reduction
• 10M vectors with <100ms queries
• 50K queries/sec at Uber production
• 600M vectors at Instacart

The Secret Sauce Nobody Talks About

Found 4 optimization tricks that changed everything:

1. Parallel Partitioned Search (10x faster)

-- Create partitioned table
CREATE TABLE embeddings_partitioned (
  id BIGSERIAL,
  embedding vector(1536)
) PARTITION BY HASH (id);

-- Create 8 partitions = 8 CPU cores
DO $$ 
BEGIN 
  FOR i IN 0..7 LOOP
    EXECUTE format('CREATE TABLE embeddings_part_%s 
                    PARTITION OF embeddings_partitioned 
                    FOR VALUES WITH (modulus 8, remainder %s)', i, i);
  END LOOP;
END $$;

-- Enable parallel execution
SET max_parallel_workers_per_gather = 8;

2. Binary Quantization (30x faster!)

-- Convert float vectors to binary
ALTER TABLE docs ADD COLUMN embedding_binary bit(1536);

UPDATE docs SET embedding_binary = (
  SELECT string_agg(
    CASE WHEN unnest > 0 THEN '1' ELSE '0' END, ''
  )::bit(1536)
  FROM unnest(embedding::float[])
);

-- Two-stage search: binary first, then refine
WITH candidates AS (
  SELECT id, embedding FROM docs
  WHERE bit_count(embedding_binary # query_binary) < 400
  LIMIT 1000
)
SELECT * FROM candidates 
ORDER BY embedding <=> query_vector 
LIMIT 10;

3. Memory-Mapped Indexes

-- Force index to stay in RAM
CREATE EXTENSION pg_prewarm;
SELECT pg_prewarm('idx_embeddings_ivfflat', 'buffer');
SET shared_buffers = '8GB';

Migration Success Stories

• Instagram: Billions of vectors, zero new infrastructure
• Spotify: Recommendation engine serving 500M users
• Discord: 150M users, p99 latency <50ms
• DoorDash: 40% latency reduction after migration
• Uber: Driver matching at 50K queries/sec

The Cost Breakdown That Matters

When PostgreSQL AI Makes Sense

Perfect for:

• You’re already using PostgreSQL
• 1-100M vectors (covers 95% of use cases)
• Need ACID compliance + vectors
• Want to avoid vendor lock-in
• Team knows SQL

Consider alternatives when:

• Billions of vectors across 100+ nodes
• Need GPU acceleration
• Purely vector-only workload

One Database to Rule Them All

PostgreSQL now handles:

• Vector embeddings (pgvector)
• JSON documents (JSONB)
• Time-series data (TimescaleDB)
• Full-text search (tsvector)
• Graph data (Apache AGE)
• Regular ACID SQL (obviously)

No more data pipeline nightmares. No more sync issues. Just SQL.

Full Deep Dive

Production code, benchmarks, and more tricks:
https://ncse.info/postgresql-ai-integration/

Question for the community:
Are you still using separate vector databases or have you consolidated to PostgreSQL? What’s been your experience with pgvector at scale?

Drop your thoughts below!

This content originally appeared on DEV Community and was authored by Zahir Fahmi