Vector Database Comparison (Supabase vs. Pinecone vs. Qdrant)

You need a vector database

to power semantic search, RAG

RAG

Click to read the full definition in our AI & Automation Glossary.

systems, or client intake matching. The question isn't whether you need one - it's which one fits your firm's technical reality and budget.

This guide compares three production-ready options: Supabase (Postgres-based, full-stack platform), Pinecone (managed, purpose-built for vectors), and Qdrant (open-source, self-hosted). Each serves different firm profiles. We'll tell you which one to pick based on your team size, technical capacity, and use case.

Data scale. Are you indexing 10,000 client documents or 10 million? Supabase handles up to ~1M vectors comfortably on standard plans. Pinecone scales to billions. Qdrant sits in between but requires you to manage infrastructure.

Query speed. For real-time client-facing tools (chatbots, instant search), you need sub-100ms queries. Pinecone delivers this out of the box. Supabase hits 100-300ms with pgvector. Qdrant matches Pinecone if configured correctly.

Team skill level. Supabase requires basic SQL knowledge. Pinecone is API

-only - no database admin needed. Qdrant demands Docker, Kubernetes, or cloud VM management.

Budget reality. Supabase starts free, scales to $25-$200/month for small firms. Pinecone starts at $70/month minimum. Qdrant costs whatever your cloud infrastructure runs (typically $50-$500/month depending on scale).

Integration complexity. Supabase plugs into existing Postgres workflows. Pinecone requires dedicated API integration. Qdrant offers REST and gRPC APIs

but you handle uptime.

Supabase adds vector search to a full Postgres database. You get authentication, storage, and real-time subscriptions in one platform. The vector extension (pgvector) runs inside Postgres.

Core capabilities:

• Stores up to 1M vectors on Pro plan ($25/month), 10M+ on Team/Enterprise
• Supports 1536-dimension embeddings (OpenAI standard) and 768-dimension (open models)
• Handles 50-200 queries/second with proper indexing
• Includes HNSW and IVFFlat indexing algorithms
• Provides row-level security for multi-tenant data isolation

Actual performance numbers:

On a dataset of 100,000 legal document embeddings (1536 dimensions), expect:

• Query latency: 150-250ms for top-10 similarity search
• Indexing time: 2-5 minutes for full dataset
• Storage: ~600MB for 100K vectors with metadata

Setup steps:

1. Enable pgvector extension in Supabase dashboard (Database > Extensions)
2. Create table: CREATE TABLE documents (id bigserial primary key, content text, embedding vector(1536));
3. Add HNSW index: CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
4. Query with: SELECT * FROM documents ORDER BY embedding <=> '[your_vector]' LIMIT 10;

Pricing breakdown:

• Free tier: 500MB database, 2GB bandwidth (good for testing only)
• Pro ($25/month): 8GB database, 50GB bandwidth, daily backups
• Team ($599/month): 100GB database, 250GB bandwidth, point-in-time recovery
• Compute add-ons: $0.01344/hour for additional CPU/RAM

Integration example:

from supabase import create_client
import openai

supabase = create_client("your-url", "your-key")
embedding = openai.Embedding.create(input="contract review", model="text-embedding-3-small")['data'][0]['embedding']

results = supabase.rpc('match_documents', {
    'query_embedding': embedding,
    'match_threshold': 0.7,
    'match_count': 5
}).execute()

Best for:

• 2-20 person firms with a developer or technical operations manager
• Firms already using Postgres for practice management data
• Projects needing user authentication, file storage, and vector search together
• Budgets under $100/month for database infrastructure

Not suitable for:

• Real-time applications requiring sub-50ms latency
• Datasets exceeding 5M vectors without enterprise budget
• Teams with zero SQL experience

Pinecone is a managed vector database

. You send vectors via API

API

Click to read the full definition in our AI & Automation Glossary.

, it handles indexing, scaling, and uptime. No database administration required.

Core capabilities:

• Scales to billions of vectors with automatic sharding
• Delivers 10-50ms query latency at p95
• Supports metadata filtering (e.g., "find similar contracts from 2023 only")
• Provides namespaces for multi-client data isolation
• Includes sparse-dense hybrid search for keyword + semantic matching

Actual performance numbers:

On a dataset of 500,000 client matter embeddings (1536 dimensions):

• Query latency: 20-40ms for top-10 similarity search
• Indexing throughput: 1,000-2,000 vectors/second
• Storage: Fully managed, no capacity planning needed

Setup steps:

1. Create index via dashboard or API

   API

   Click to read the full definition in our AI & Automation Glossary.

   : dimension=1536, metric='cosine', pod_type='p1.x1'
2. Upsert vectors: index.upsert(vectors=[("id1", [0.1, 0.2, ...], {"client": "ACME Corp"})])
3. Query: index.query(vector=[0.1, 0.2, ...], top_k=10, filter={"client": "ACME Corp"})

Pricing breakdown:

• Starter ($70/month): 100K vectors, 1 pod, 5M queries/month
• Standard ($0.096/hour per pod): ~$70/month for 1 pod, scales linearly
• Enterprise: Custom pricing for multi-region, dedicated support

Pod types:

• p1.x1: 1M vectors, $70/month
• p1.x2: 5M vectors, $140/month
• s1.x1: 5M vectors, $130/month (storage-optimized)

Integration example:

import pinecone
from openai import OpenAI

pinecone.init(api_key="your-key", environment="us-west1-gcp")
index = pinecone.Index("legal-docs")

client = OpenAI()
embedding = client.embeddings.create(input="employment agreement", model="text-embedding-3-small").data[0].embedding

results = index.query(
    vector=embedding,
    top_k=5,
    filter={"practice_area": "employment"},
    include_metadata=True
)

Best for:

• 10-100 person firms with dedicated product/engineering teams
• Client-facing applications requiring fast response times
• Datasets exceeding 1M vectors
• Firms prioritizing uptime over cost optimization

Not suitable for:

• Firms with under $500/month infrastructure budget
• Projects requiring on-premise deployment
• Teams wanting to avoid vendor lock-in

Qdrant is open-source vector search. You deploy it on your own infrastructure (AWS, GCP, Azure, or on-premise). Full control, full responsibility.

Core capabilities:

• Handles 10M+ vectors on a single node (16GB RAM)
• Supports 100+ queries/second with proper hardware
• Provides payload-based filtering with full JSON query syntax
• Includes quantization for 4x storage reduction
• Offers both REST and gRPC APIs

  APIs

  Click to read the full definition in our AI & Automation Glossary.

Actual performance numbers:

On a dataset of 250,000 matter descriptions (768 dimensions):

• Query latency: 30-80ms for top-10 similarity search
• Indexing throughput: 500-1,500 vectors/second
• Storage: ~200MB with scalar quantization enabled

Setup steps (Docker):

1. Run: docker run -p 6333:6333 qdrant/qdrant
2. Create collection via API

   API

   Click to read the full definition in our AI & Automation Glossary.

   :

curl -X PUT 'http://localhost:6333/collections/documents' \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {"size": 768, "distance": "Cosine"},
    "optimizers_config": {"indexing_threshold": 10000}
  }'

3. Insert vectors via REST or Python client
4. Query with payload filters for multi-tenant isolation

Pricing breakdown (self-hosted):

AWS EC2 example:

• t3.medium (2 vCPU, 4GB RAM): $30/month, handles 100K vectors
• t3.large (2 vCPU, 8GB RAM): $60/month, handles 500K vectors
• m5.xlarge (4 vCPU, 16GB RAM): $140/month, handles 2M vectors

Add $20-50/month for backups and monitoring.

Qdrant Cloud (managed option):

• 1GB cluster: $25/month
• 4GB cluster: $95/month
• 8GB cluster: $185/month

Integration example:

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

client = QdrantClient(host="localhost", port=6333)

client.create_collection(
    collection_name="contracts",
    vectors_config=VectorParams(size=768, distance=Distance.COSINE)
)

client.upsert(
    collection_name="contracts",
    points=[
        PointStruct(id=1, vector=[0.1, 0.2, ...], payload={"type": "NDA", "year": 2024})
    ]
)

results = client.search(
    collection_name="contracts",
    query_vector=[0.1, 0.2, ...],
    query_filter={"must": [{"key": "year", "match": {"value": 2024}}]},
    limit=5
)

Best for:

• Firms with existing DevOps or IT infrastructure teams
• Projects requiring on-premise deployment for compliance
• Budgets prioritizing infrastructure control over managed services
• Teams comfortable with Docker, Kubernetes, or VM management

Not suitable for:

• Firms without technical staff to manage deployments
• Projects needing 99.9%+ uptime SLAs without dedicated ops
• Teams wanting vendor support for performance tuning

Pick Supabase if:

• Your firm has 2-20 people and one technical person
• You need authentication, storage, and vectors in one platform
• Your dataset is under 1M vectors
• Your budget is $25-$200/month

Pick Pinecone if:

• You need sub-50ms query performance for client-facing tools
• Your dataset exceeds 1M vectors or will soon
• You have $500+/month for infrastructure
• You want zero database administration

Pick Qdrant if:

• You have DevOps capacity or an IT team
• You need on-premise deployment for compliance
• You want infrastructure cost control
• You're comfortable managing uptime and backups

Hybrid approach: Start with Supabase for prototyping. Migrate to Pinecone when you hit performance limits or scale past 500K vectors. Use Qdrant if compliance mandates on-premise or you have existing Kubernetes infrastructure.

Most 5-50 person professional services firms start with Supabase and stay there. Firms with dedicated product teams or high-volume client applications graduate to Pinecone. Qdrant fits regulated industries (legal, healthcare, finance) requiring data residency control.