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Vector Databases Head-to-Head: pgvector vs AlloyDB AI vs Pinecone

You built a RAG pipeline. Now you need to decide where to store embeddings. Three options dominate: pgvector (self-hosted on PostgreSQL), AlloyDB AI (Google Cloud managed), and Pinecone (fully managed SaaS). Each makes a different trade-off between cost, latency, and operational burden.

See also: BigQuery analytics

The decision is nuanced, and it depends on whether you want to own the infrastructure or rent it.

The Trade-Offs

Aspect	pgvector	AlloyDB AI	Pinecone
Setup time	1 week	1 day	1 hour
Query latency	50-100ms	20-50ms	10-20ms
Cost @ 1M vectors	$800/mo	$2K/mo	$3K/mo
Operational overhead	High (you manage)	Medium (Google manages)	None (Pinecone manages)
Data residency control	Full	Full	Limited
Metadata filtering	Native SQL	Native SQL	Limited

pgvector: DIY but Cost-Effective

You run PostgreSQL yourself. pgvector is a Postgres extension that adds vector search.

-- Enable the extension
CREATE EXTENSION vector;

-- Create embeddings table
CREATE TABLE documents (
  id BIGSERIAL PRIMARY KEY,
  content TEXT,
  embedding vector(1536),  -- OpenAI embeddings
  metadata JSONB,
  created_at TIMESTAMP DEFAULT NOW()
);

-- Create index for fast search
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

Query a vector:

SELECT id, content, 
       1 - (embedding <=> '[0.1, 0.2, ...]') AS similarity
FROM documents
WHERE metadata->>'category' = 'finance'
ORDER BY embedding <=> '[0.1, 0.2, ...]'
LIMIT 10;

Strengths:

✅ You own the data (no vendor lock-in)
✅ Cheap at scale ($800/mo for 1M vectors)
✅ Native SQL queries (combine vector + metadata)
✅ No per-query costs

Weaknesses:

❌ You manage backups, upgrades, scaling
❌ 50-100ms query latency (slower than managed)
❌ Index optimization requires DBA skills

AlloyDB AI: Managed PostgreSQL with Vectors

Google’s managed PostgreSQL offering with built-in vector search. Same pgvector under the hood, but Google handles ops.

from google.cloud.sql.connector import Connector

connector = Connector()

async def query_documents(embedding: list[float]) -> list[dict]:
    async with connector.connect(
        "projects/my-project/instances/alloydb-instance",
        driver="asyncpg",
        user="postgres",
        db="vectors_db",
    ) as conn:
        results = await conn.fetch("""
            SELECT id, content, 
                   1 - (embedding <=> $1) AS similarity
            FROM documents
            WHERE metadata->>'category' = 'finance'
            ORDER BY embedding <=> $1
            LIMIT 10
        """, embedding)
        return results

Strengths:

✅ Zero ops (Google manages everything)
✅ 20-50ms latency (faster than DIY pgvector)
✅ Automatic backups and failover
✅ VPC integration (no internet required)

Weaknesses:

❌ $2K/mo (2-3x pgvector cost)
❌ Still managed by Google (less control than DIY)
❌ Overkill if you don’t need HA failover

Pinecone: Fully Managed, Fastest

Pinecone is purpose-built for vectors. You don’t touch infrastructure at all.

from pinecone import Pinecone

pc = Pinecone(api_key="...")
index = pc.Index("documents")

# Upsert vectors
index.upsert(vectors=[
    ("doc_1", [0.1, 0.2, ...], {"category": "finance"}),
    ("doc_2", [0.3, 0.4, ...], {"category": "health"}),
])

# Query
results = index.query(
    vector=[0.1, 0.2, ...],
    top_k=10,
    filter={"category": "finance"},
)
# Returns: [("doc_1", 0.95), ("doc_3", 0.87), ...]

Strengths:

✅ 10-20ms latency (fastest)
✅ Zero ops (completely managed)
✅ Auto-scaling (no capacity planning)
✅ Metadata filtering works

Weaknesses:

❌ $3K/mo (most expensive)
❌ Vendor lock-in (proprietary API)
❌ Less control over filtering (not true SQL)
❌ Per-query costs at massive scale

Real-World Recommendations

Use pgvector if:

You’re cost-sensitive
You have a DBA who can manage Postgres
You don’t need < 50ms latency
You want full data control

Use AlloyDB AI if:

You want managed PostgreSQL
You’re already on Google Cloud
You need 20-50ms latency
You can justify $2K/mo

Use Pinecone if:

You need < 20ms latency
You want zero ops
You don’t mind vendor lock-in
Latency is your bottleneck

Case Study: Bancnet’s Choice

Bancnet (Open Banking) needed:

37% latency reduction over baseline
Data residency in EU (no US cloud)
Cost control (scaling to millions of vectors)

Decision: pgvector on self-managed PostgreSQL in Azure.

Results:

Initial latency: 85ms (full-text search)
After pgvector: 52ms (38% reduction ← 37% target)
Cost: $600/mo (vs $2K Managed, $3K Pinecone)
Operational cost: 1 DBA, 5 hours/week maintenance

The trade-off: We own the HA failover, backups, and scaling. But the 37% latency gain and cost savings justified it.

Tags: #VectorDatabases #RAG #Embeddings #Postgres #Performance

Published: June 2026
Author: Pratik Dhanave
Related Projects: Bancnet RAG, Bodh embeddings