In his own words, YugabyteDB expert and vector enthusiast Amol Bhoite shares his experience of deep-diving into semantic AI search with vector databases and what he discovered on his journey.

How Working on YugabyteDB Inspired Exploration of the Vector Database Frontier

YugabyteDB is a PostgreSQL-compatible distributed database equipped with built-in pgvector support and has proven its ability to handle billions of vectors in a single cluster.

Through my work on YugabyteDB, I became deeply fascinated by the possibilities of vector databases, semantic search, RAG, and AI at scale. To understand the space, I immersed myself in training videos, blogs, YouTube “how-to” sessions, and consumed every piece of online material I could find. My goal was to gain a deep understanding of how vector databases power embeddings, RAG pipelines, and semantic AI.

The deeper I went, the more I realised how fragmented this landscape is:

• On one side, you have pure vector engines, like Pinecone, Weaviate, Qdrant, Milvus, and Chroma, each with its own architecture, deployment model, scalability promises, and trade-offs.
• On the other side are vector stores embedded in existing systems, such as PostgreSQL and YugabyteDB with pgvector. These added vector capabilities to familiar stacks but came with their own operational, scaling, and integration limitations.
• On a third frontier, new AI/ML platforms are emerging and evolving rapidly.

The more I explored deployment patterns, index types, storage engines, and ANN algorithms, the more I noticed a gap. People had their opinions, but few had concrete comparisons, frameworks, or unbiased guidance. Every vendor claimed to be “the best,” but almost no one explained the areas they excelled and where they were not a good fit.

This knowledge gap, along with my research, inspired me to write a book: Semantic AI Search with Vector Databases: A Complete Guide to Semantic Search, Embeddings & RAG.

The book acts as a structured, technical reference to clarify trade-offs, deployment patterns, and real-world constraints. It helps the tech community, engineers, architects, CTOs, and decision-makers navigate migration strategies, distributed systems, high availability, and resilient AI design with confidence.

Unifying Relational and Vector Workloads at Scale

In my research, I evaluated three categories of vector service providers:

1. Pure vector databases (such as Pinecone, Weavate, Milvus, Qdrant, Chroma) excel at high-dimensional similarity search, but aren’t suited for relational workloads, joins, or OLTP-style operations.
2. Relational databases with vector extensions (such as pgvector, Aurora, RDS, AlloyDB, Cloud SQL) are familiar and easy to use, but at a global scale with billions of embeddings, they encounter distribution, sharding, and performance limitations.
3. Distributed SQL engines with native vector capabilities offer a unified architecture, combining the strengths of both worlds: relational, transactional, and vector workloads on a globally distributed, fault-tolerant engine.

Why YugabyteDB Excels at Vector Search and AI Workloads

PostgreSQL SQL + pgvector Compatibility: YugabyteDB offers native support for vector columns, vector indexes, and nearest-neighbour queries using standard SQL syntax through the pgvector extension. This allows you to leverage familiar PostgreSQL tools and queries for vector search, without needing to learn a new API or query language.

Scalable, Distributed Architecture: YugabyteDB employs a distributed SQL architecture, leveraging a Vector LSM (Log-Structured Merge) abstraction and integrating the high-performance USearch engine. This enables horizontal scaling of vector data and indexes across multiple nodes, supporting workloads from millions to billions of vectors while maintaining low-latency queries.

Enterprise-Grade Resilience: YugabyteDB ensures fault tolerance and strong consistency for vector indexes through features such as Multi-Version Concurrency Control (MVCC), the Raft consensus protocol, and a Write-Ahead Log (WAL)-based recovery mechanism. This provides enterprise-grade resilience, high availability, and robust data protection for both vector and relational workloads.

Proven at Scale: In benchmarking with the Deep1B dataset, YugabyteDB successfully indexed 1 billion 96-dimensional vectors, achieving a recall of 96.56% and sub-second query latency. This demonstrates YugabyteDB’s ability to handle massive vector workloads with high accuracy and performance.

Hybrid Workloads in a Unified Platform: YugabyteDB allows you to unify transactional (OLTP), relational, and semantic (vector) workloads within a single system. This eliminates the need to maintain separate silos for your operational database and your vector store, simplifying architecture and operations.

If you want to unify transactional, relational, and vector workloads in a single system, rather than managing separate silos, YugabyteDB is a compelling choice.

A Realistic View for Real-World Systems

Vector databases are powerful, but they’re not a magic wand. True intelligence happens when:

• LLMs reason through problems step by step
• Relational databases handle structured metadata
• Vector search finds semantically similar content
• Cloud systems give you scalable, resilient infrastructure

The future isn’t a choice between a cloud database or a vector database; it’s how they work together.

You need to examine your individual business case and make a decision on when to choose a specialized vector engine, when to repurpose your existing database, and whether a hybrid architecture could mean cutting costs, reducing complexity, and avoiding headaches down the line.

Conclusion

Building AI-powered, semantic systems is more than just adopting the latest tools and hoping they work for your business; it’s about understanding trade-offs, designing for scale, and connecting embeddings, relational data, and LLMs to deliver real business value.

Tools will evolve, but principles such as thoughtful design, rigorous testing, and continuous evaluation will endure.

Want to know more?

Check out Semantic AI Search with Vector Databases: A Complete Guide to Semantic Search, Embeddings & RAG for a deep, practical understanding of vector databases, their significance in the AI era, and when they are not applicable.

Learn how vector stores, complete vector databases, and hybrid systems compare, as well as how to model, index, query, scale, and operate vector and relational workloads together.

You’ll walk away with the clarity you need to design a unified stack that supports semantic AI search, embeddings, cloud-native scale, resilience, and the right architectural choices before committing to a solution.