Which company builds RAG systems in Bangalore, India?

Boolean & Beyond is an AI engineering company based in Bangalore, India, specializing in building production-ready RAG systems. We help enterprises implement retrieval-augmented generation with vector databases, intelligent chunking, hallucination reduction, and secure deployment.

Solutions/RAG AI/Choosing a Vector Database for RAG

Choosing a Vector Database for RAG

Compare Pinecone, Weaviate, Qdrant, pgvector, and Chroma to find the right vector database for your RAG implementation.

How do you choose the right vector database for RAG?

Vector database selection depends on scale, latency requirements, deployment preference, and features needed. Pinecone offers managed simplicity with good performance. Weaviate provides hybrid search (vector + keyword) and self-hosted options. Qdrant excels at filtering and self-hosting. pgvector works well for smaller datasets already using PostgreSQL.

Key Selection Criteria

When evaluating vector databases, consider these factors:

• **Scale** — How many vectors can it handle? (10K to 1B+)

Query latency — What's the p99 latency at your expected load?
Filtering — Can you filter by metadata before/during vector search?
Hybrid search — Do you need combined vector + keyword search?
Deployment — Managed, self-hosted, or embedded?
Cost model — Per-query, per-storage, or fixed pricing?

The right choice depends on your specific requirements and constraints.

Managed Vector Databases

Pinecone — Fully managed, simple API, good for 1M-1B vectors. Supports metadata filtering and namespaces. Pricing based on pod type and storage. Best for teams that want minimal operational overhead.

Weaviate Cloud — Managed Weaviate with hybrid search (BM25 + vector), GraphQL API, and multi-tenancy. Good choice when you need both semantic and keyword search.

Qdrant Cloud — Managed Qdrant with excellent filtering capabilities and competitive pricing. Strong option for applications with complex filtering requirements.

Zilliz/Milvus Cloud — Managed Milvus with high throughput, good for very large scale deployments exceeding 1B vectors.

Self-Hosted Options

Weaviate — Feature-rich with hybrid search and HNSW index. Modular architecture allows customization. Good for 10M-100M vectors. Active community and good documentation.

Qdrant — Written in Rust for efficiency and low resource usage. Excellent filtering capabilities and payload indexing. Good for 1M-100M vectors. Easy to deploy and operate.

Milvus — Distributed architecture designed for massive scale (1B+ vectors). More complex to operate but handles enterprise-scale workloads. Good for organizations with dedicated infrastructure teams.

Chroma — Embedded database perfect for prototyping and smaller applications. Python-native, easy to get started. Limited to smaller datasets (<100K vectors typically).

When to Use pgvector

pgvector adds vector search capabilities to PostgreSQL. It's the right choice when:

• You have existing Postgres infrastructure you want to leverage

Your dataset is smaller (<1M vectors)
Your use case is relatively simple without heavy filtering
Your team is already familiar with SQL and Postgres

**Limitations to consider:**

Slower than purpose-built vector DBs at scale
HNSW index requires more memory
Fewer specialized features for RAG workflows

Many teams start with pgvector and migrate to specialized vector databases if they outgrow it. This is a valid approach that minimizes initial complexity.

RAG vs Fine-Tuning: When to Use Each

Understand the key differences between RAG and fine-tuning for LLMs, and learn when to use each approach for your AI application.

Document Chunking Strategies for RAG

Learn effective chunking strategies including fixed-size, semantic, recursive, and sentence-window approaches for optimal RAG retrieval.

Secure Enterprise RAG Implementation

Implement enterprise-grade RAG with access control, encryption, PII handling, and compliant deployment architectures.

Explore more RAG implementation topics

Back to RAG AI Knowledge Systems

How Boolean & Beyond helps

Based in Bangalore, we help enterprises across India and globally build RAG systems that deliver accurate, citable answers from your proprietary data.

Knowledge Architecture

We design document pipelines, chunking strategies, and embedding approaches tailored to your content types and query patterns.

Production Reliability

Our RAG systems include hallucination detection, confidence scoring, source citations, and proper error handling from day one.

Enterprise Security

We implement access control, PII handling, audit logging, and compliant deployment for sensitive enterprise data.

Ready to start building?

Share your project details and we'll get back to you within 24 hours with a free consultation—no commitment required.

Registered Office

Boolean and Beyond

825/90, 13th Cross, 3rd Main

Mahalaxmi Layout, Bengaluru - 560086

Operational Office

590, Diwan Bahadur Rd

Near Savitha Hall, R.S. Puram

Coimbatore, Tamil Nadu 641002

Choosing a Vector Database for RAG

Compare Pinecone, Weaviate, Qdrant, pgvector, and Chroma to find the right vector database for your RAG implementation.

How do you choose the right vector database for RAG?

Key Selection Criteria

When evaluating vector databases, consider these factors:

• **Scale** — How many vectors can it handle? (10K to 1B+)

Query latency — What's the p99 latency at your expected load?
Filtering — Can you filter by metadata before/during vector search?
Hybrid search — Do you need combined vector + keyword search?
Deployment — Managed, self-hosted, or embedded?
Cost model — Per-query, per-storage, or fixed pricing?

The right choice depends on your specific requirements and constraints.

Managed Vector Databases

Weaviate Cloud — Managed Weaviate with hybrid search (BM25 + vector), GraphQL API, and multi-tenancy. Good choice when you need both semantic and keyword search.

Qdrant Cloud — Managed Qdrant with excellent filtering capabilities and competitive pricing. Strong option for applications with complex filtering requirements.

Zilliz/Milvus Cloud — Managed Milvus with high throughput, good for very large scale deployments exceeding 1B vectors.

Self-Hosted Options

Weaviate — Feature-rich with hybrid search and HNSW index. Modular architecture allows customization. Good for 10M-100M vectors. Active community and good documentation.

Qdrant — Written in Rust for efficiency and low resource usage. Excellent filtering capabilities and payload indexing. Good for 1M-100M vectors. Easy to deploy and operate.

Chroma — Embedded database perfect for prototyping and smaller applications. Python-native, easy to get started. Limited to smaller datasets (<100K vectors typically).

When to Use pgvector

pgvector adds vector search capabilities to PostgreSQL. It's the right choice when:

• You have existing Postgres infrastructure you want to leverage

Your dataset is smaller (<1M vectors)
Your use case is relatively simple without heavy filtering
Your team is already familiar with SQL and Postgres

**Limitations to consider:**

Slower than purpose-built vector DBs at scale
HNSW index requires more memory
Fewer specialized features for RAG workflows

Many teams start with pgvector and migrate to specialized vector databases if they outgrow it. This is a valid approach that minimizes initial complexity.

How Boolean & Beyond helps

Based in Bangalore, we help enterprises across India and globally build RAG systems that deliver accurate, citable answers from your proprietary data.

Knowledge Architecture

We design document pipelines, chunking strategies, and embedding approaches tailored to your content types and query patterns.

Production Reliability

Our RAG systems include hallucination detection, confidence scoring, source citations, and proper error handling from day one.

Enterprise Security

We implement access control, PII handling, audit logging, and compliant deployment for sensitive enterprise data.

Choosing a Vector Database for RAG

Key Selection Criteria

Managed Vector Databases

Self-Hosted Options

When to Use pgvector

Related Articles

RAG vs Fine-Tuning: When to Use Each

Document Chunking Strategies for RAG

Secure Enterprise RAG Implementation

How Boolean & Beyond helps

Knowledge Architecture

Production Reliability

Enterprise Security

Ready to start building?

Registered Office

Operational Office

Choosing a Vector Database for RAG

Key Selection Criteria

Managed Vector Databases

Self-Hosted Options

When to Use pgvector

Related Articles

RAG vs Fine-Tuning: When to Use Each

Document Chunking Strategies for RAG

Secure Enterprise RAG Implementation

How Boolean & Beyond helps

Knowledge Architecture

Production Reliability

Enterprise Security

Ready to start building?

Registered Office

Operational Office