Comparing Vector Databases: Azure AI Search vs Pinecone vs Weaviate

Choosing the right vector database is crucial for RAG applications. After implementing production systems with all three major options in 2025, here’s my detailed comparison.

Quick Comparison Matrix

Feature	Azure AI Search	Pinecone	Weaviate
Managed	Yes	Yes	Yes/Self-hosted
Hybrid Search	Excellent	Basic	Good
Scale	Enterprise	Serverless	Flexible
Cost Model	Per-unit	Per-vector	Per-node
Azure Integration	Native	SDK	SDK

Azure AI Search

Best for organizations already on Azure with hybrid search needs.

from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    SearchIndex, SearchField, VectorSearch,
    HnswAlgorithmConfiguration, VectorSearchProfile
)

# Create index with vector search
index = SearchIndex(
    name="products",
    fields=[
        SearchField(name="id", type="Edm.String", key=True),
        SearchField(name="content", type="Edm.String", searchable=True),
        SearchField(
            name="content_vector",
            type="Collection(Edm.Single)",
            searchable=True,
            vector_search_dimensions=1536,
            vector_search_profile_name="default"
        )
    ],
    vector_search=VectorSearch(
        algorithms=[HnswAlgorithmConfiguration(name="hnsw")],
        profiles=[VectorSearchProfile(name="default", algorithm_configuration_name="hnsw")]
    )
)

# Hybrid search query
results = search_client.search(
    search_text="wireless headphones",
    vector_queries=[VectorizedQuery(vector=embedding, fields="content_vector", k_nearest_neighbors=10)],
    select=["id", "content", "price"],
    top=10
)

Pros: Native Azure integration, excellent hybrid search, semantic ranking Cons: Complex pricing, steeper learning curve

Pinecone

Best for serverless deployments with simple vector-only search.

from pinecone import Pinecone

pc = Pinecone(api_key="your-key")
index = pc.Index("products")

# Upsert vectors
index.upsert(
    vectors=[
        {"id": "prod-1", "values": embedding, "metadata": {"category": "electronics"}},
        {"id": "prod-2", "values": embedding2, "metadata": {"category": "audio"}}
    ],
    namespace="products"
)

# Query with metadata filter
results = index.query(
    vector=query_embedding,
    top_k=10,
    filter={"category": {"$eq": "electronics"}},
    include_metadata=True
)

Pros: Simple API, serverless scaling, predictable pricing Cons: Limited hybrid search, no native text search

Weaviate

Best for flexibility and advanced ML features.

import weaviate
from weaviate.classes.config import Configure, Property, DataType

client = weaviate.connect_to_weaviate_cloud(
    cluster_url="your-cluster.weaviate.network",
    auth_credentials=weaviate.auth.AuthApiKey("your-key")
)

# Create collection with vectorizer
products = client.collections.create(
    name="Product",
    vectorizer_config=Configure.Vectorizer.text2vec_openai(),
    properties=[
        Property(name="content", data_type=DataType.TEXT),
        Property(name="category", data_type=DataType.TEXT)
    ]
)

# Hybrid search
results = products.query.hybrid(
    query="wireless headphones",
    alpha=0.5,  # Balance between keyword and vector
    limit=10
)

Pros: Built-in vectorization, GraphQL API, flexible deployment Cons: More complex operations, variable performance at scale

My Recommendation

• Azure shops: Use Azure AI Search for unified experience
• Startups: Pinecone for simplicity and serverless
• ML teams: Weaviate for advanced features and flexibility

The best choice depends on your existing infrastructure, team expertise, and specific requirements. All three are production-ready in 2025.