Vector Database Selection: Choosing the Right Solution for RAG

Selecting the right vector database is critical for RAG application performance. Each solution offers different trade-offs between scalability, cost, features, and operational complexity.

Key Selection Criteria

from dataclasses import dataclass
from typing import List, Dict
from enum import Enum

class DeploymentModel(Enum):
    MANAGED_CLOUD = "managed_cloud"
    SELF_HOSTED = "self_hosted"
    SERVERLESS = "serverless"

@dataclass
class VectorDBRequirements:
    expected_vector_count: int
    dimension_size: int
    queries_per_second: int
    latency_p99_ms: int
    metadata_filtering: bool
    hybrid_search: bool
    multi_tenancy: bool
    deployment_preference: DeploymentModel

@dataclass
class VectorDBOption:
    name: str
    max_vectors: int
    supported_dimensions: List[int]
    hybrid_search: bool
    pricing_model: str
    managed_available: bool

def evaluate_options(requirements: VectorDBRequirements) -> List[Dict]:
    """Evaluate vector database options against requirements."""

    options = [
        VectorDBOption(
            name="Azure AI Search",
            max_vectors=1_000_000_000,
            supported_dimensions=[1, 3072],
            hybrid_search=True,
            pricing_model="per_document_and_query",
            managed_available=True
        ),
        VectorDBOption(
            name="Pinecone",
            max_vectors=1_000_000_000,
            supported_dimensions=[1, 20000],
            hybrid_search=True,
            pricing_model="serverless_or_pod",
            managed_available=True
        ),
        VectorDBOption(
            name="Qdrant",
            max_vectors=100_000_000,
            supported_dimensions=[1, 65536],
            hybrid_search=True,
            pricing_model="self_hosted_or_cloud",
            managed_available=True
        ),
        VectorDBOption(
            name="Weaviate",
            max_vectors=1_000_000_000,
            supported_dimensions=[1, 65536],
            hybrid_search=True,
            pricing_model="self_hosted_or_cloud",
            managed_available=True
        )
    ]

    scored_options = []
    for option in options:
        score = 0

        # Check vector capacity
        if option.max_vectors >= requirements.expected_vector_count:
            score += 30

        # Check hybrid search requirement
        if requirements.hybrid_search and option.hybrid_search:
            score += 25
        elif not requirements.hybrid_search:
            score += 25

        # Check managed preference
        if requirements.deployment_preference == DeploymentModel.MANAGED_CLOUD:
            if option.managed_available:
                score += 20

        scored_options.append({
            "name": option.name,
            "score": score,
            "meets_requirements": score >= 75
        })

    return sorted(scored_options, key=lambda x: x["score"], reverse=True)

Performance Benchmarking

import time
import numpy as np
from typing import Callable

def benchmark_vector_db(
    insert_fn: Callable,
    search_fn: Callable,
    vectors: np.ndarray,
    queries: np.ndarray,
    batch_size: int = 100
) -> Dict:
    """Benchmark vector database operations."""

    results = {
        "insert_times": [],
        "search_times": [],
        "search_recall": []
    }

    # Benchmark inserts
    for i in range(0, len(vectors), batch_size):
        batch = vectors[i:i+batch_size]
        start = time.perf_counter()
        insert_fn(batch)
        elapsed = time.perf_counter() - start
        results["insert_times"].append(elapsed / len(batch))

    # Benchmark searches
    for query in queries:
        start = time.perf_counter()
        search_results = search_fn(query, top_k=10)
        elapsed = time.perf_counter() - start
        results["search_times"].append(elapsed * 1000)  # Convert to ms

    return {
        "avg_insert_ms": np.mean(results["insert_times"]) * 1000,
        "p50_search_ms": np.percentile(results["search_times"], 50),
        "p99_search_ms": np.percentile(results["search_times"], 99),
        "queries_per_second": 1000 / np.mean(results["search_times"])
    }

The best vector database depends on your specific requirements. Azure AI Search excels for hybrid search with integrated semantic ranking. Pinecone offers the simplest serverless experience. Qdrant and Weaviate provide flexibility for self-hosted deployments.