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Vector Databases vs. NewSQL Databases

Vector Databases vs. NewSQL Databases

Jan 06, 202518 min read

Introduction

Vector databases excel at storing and querying high-dimensional vector embeddings, enabling AI applications to find semantic and perceptual similarities through specialized index structures optimized for nearest-neighbor search. NewSQL databases combine the ACID guarantees and relational model of traditional SQL databases with the horizontal scalability and performance characteristics previously associated only with NoSQL systems.

But here's where things get interesting: as enterprise applications increasingly incorporate AI capabilities alongside mission-critical transactional workloads, the boundaries between these specialized database categories are beginning to blur. NewSQL systems are adding vector support, while vector databases are enhancing their transactional capabilities and data consistency guarantees.

For architects and developers designing data systems in 2025, understanding when to leverage each technology—and when they might complement each other—has become essential for building applications that balance advanced AI features with enterprise-grade reliability and consistency. The decision requires careful consideration of your specific workloads, data access patterns, and consistency requirements rather than simply choosing the trendiest option.

Today's Database Landscape: Specialization Reigns

Remember when relational databases were considered the universal solution for virtually all data persistence needs? Those days are firmly behind us. The modern data landscape has evolved into a rich ecosystem of purpose-built solutions, each optimized for specific data types, access patterns, and operational characteristics.

In this increasingly specialized landscape:

Traditional relational databases continue to excel at transactional workloads with well-defined schemas and strong consistency requirements
Document databases handle flexible JSON-like data with nested structures and schema flexibility
Key-value stores provide blazing-fast simple data access with minimal overhead
Graph databases make relationship-heavy data efficiently queryable and traversable
Time series databases efficiently manage chronological data points with time-optimized storage and queries
Wide-column stores distribute massive structured datasets across clusters with column-oriented optimizations

Vector databases and NewSQL systems represent two important innovations in this specialized ecosystem:

Vector databases have emerged as essential infrastructure for AI applications, effectively bridging the gap between models that generate embeddings and applications that need to efficiently query them. The explosion of generative AI, semantic search, and recommendation systems has made them increasingly central to modern applications.
NewSQL databases arose to solve the seemingly contradictory challenge of maintaining SQL's relational model and ACID guarantees while achieving the horizontal scalability previously only possible with NoSQL systems. They've become critical for applications that need both transactional integrity and the ability to scale out across distributed infrastructure.

What makes this comparison particularly relevant is the growing number of enterprise applications that need both the AI-powered capabilities of vector databases and the transactional reliability of NewSQL systems.

Why You Might Be Deciding Between These Database Types

If you're reading this, you're likely facing one of these scenarios:

You're adding AI features to a mission-critical enterprise application: Perhaps you have an existing application using a NewSQL database and now need to incorporate semantic search or recommendations.
You're architecting a new application with both AI and transactional requirements: You're building a platform that requires both vector similarity search and reliable ACID transactions.
You're evaluating specialized vs. unified approaches: You're weighing whether to use specialized databases for different workloads or find a single solution that addresses multiple needs.
You're concerned about data consistency across AI and transactional components: You need to ensure that AI-powered features operate on consistent, up-to-date data.
You're future-proofing your architecture: You want to understand how these technologies might converge or complement each other as your application evolves.

As someone who's implemented both types of systems across diverse industries, I can tell you that making the right choice requires understanding not just what each database type excels at, but how their architectural differences impact your specific requirements for consistency, scalability, and query patterns.

Vector Databases: The Backbone of Modern AI Search

Architectural Foundations

At their core, vector databases like Milvus and Zilliz Cloud revolve around a powerful concept: representing data items as points in high-dimensional space where proximity equals similarity. Their architecture typically includes:

Vector storage engines optimized for dense numerical arrays that can range from dozens to thousands of dimensions
ANN (Approximate Nearest Neighbor) indexes like HNSW, IVF, or PQ that make billion-scale vector search practical
Distance computation optimizations for calculating similarity using metrics like cosine, Euclidean, or dot product
Filtering subsystems that combine vector search with metadata constraints
Sharding mechanisms designed specifically for distributing vector workloads

The key insight: vector databases sacrifice the perfect accuracy of exact nearest neighbor search for the dramatic performance gains of approximate methods, making previously infeasible similarity search applications practical at scale.

What Sets Vector DBs Apart

In my experience implementing these systems, these capabilities really make vector databases shine:

Tunable accuracy-performance tradeoffs: The ability to adjust index parameters to balance search speed against result precision
Multi-vector record support: Storing multiple embedding vectors per item to represent different aspects or modalities
Hybrid search capabilities: Combining vector similarity with traditional filtering for precise results
Distance metric flexibility: Supporting different similarity measures for different embedding types
Metadata filtering: Narrowing results based on traditional attributes alongside vector similarity

Recent innovations have further expanded their capabilities:

Sparse-dense hybrid search: Combining traditional keyword matching strengths with semantic understanding
Cross-encoder reranking: Refining initial vector search results with more computationally intensive models
Serverless scaling: Automatically adjusting resources based on query and indexing loads
Multi-stage retrieval pipelines: Orchestrating complex retrieval flows with filtering and reranking stages

Zilliz Cloud and Milvus: Leading the Vector Database Ecosystem

Among the growing ecosystem of vector database solutions, Zilliz Cloud and the open-source Milvus project have emerged as significant players:

Milvus is a widely-adopted open-source vector database that has gained popularity among developers building AI applications. Created to handle vector similarity search at scale, it provides the foundation for many production systems in areas ranging from recommendation engines to image search. The project has a strong community behind it and is designed with performance and scalability in mind.

Zilliz Cloud is the managed service version of Milvus, offering the same core functionality without the operational complexity. For development teams looking to implement vector search capabilities without dedicating resources to database management, Zilliz Cloud provides a streamlined path to production. This cloud-native approach aligns with modern development practices where teams increasingly prefer to consume databases as services rather than managing the underlying infrastructure themselves.

Popular Use Cases: Vector Databases

Vector databases are transforming various industries with their ability to power similarity-based applications:

Retrieval-Augmented Generation (RAG): Vector databases connect language models with relevant information sources. Users can ask complex questions like "What were our Q2 sales results in Europe?" and receive accurate answers drawn directly from internal documents—ensuring responses are factual and up-to-date.
Semantic Search: Vector databases enable natural language search that understands user intent rather than just matching keywords. Users can search with conversational queries like "affordable vacation spots for families" and receive semantically relevant results, even when these exact words don't appear in the content.
Recommendation Systems: E-commerce platforms, streaming services, and content platforms use vector databases to deliver personalized recommendations based on semantic similarity rather than just collaborative filtering. This approach reduces the "cold start" problem for new items and can better explain why recommendations are being made.
Image and Visual Search: Retailers and visual platforms use vector databases to enable search-by-image functionality. Users can upload a photo to find visually similar products, artwork, or designs—particularly valuable in fashion, interior design, and creative fields.
Anomaly Detection: Security and monitoring systems leverage vector databases to identify unusual patterns that don't match expected behaviors. This is particularly valuable for fraud detection, network security, and manufacturing quality control.

NewSQL Databases: Scaling Transactions Without Compromise

Architectural Foundations

NewSQL databases like Google Spanner, CockroachDB, and SingleStore emerged from a fundamental challenge: how to maintain the ACID guarantees and relational model that enterprise applications depend on while achieving the horizontal scalability necessary for modern workloads. Their architecture typically includes:

Distributed SQL engines that preserve standard SQL semantics while operating across clusters
Sophisticated consensus protocols (like Paxos or Raft) that ensure data consistency in distributed environments
Automatic sharding systems that distribute data across nodes while maintaining transactional integrity
Optimistic or multi-version concurrency control for high throughput without sacrificing consistency
Distributed execution engines that parallelize query operations across the cluster

The core insight: by rethinking how relational databases handle distributed consensus, transaction coordination, and query execution, NewSQL systems achieve horizontal scalability without abandoning the SQL model or ACID guarantees that applications rely on.

What Sets NewSQL DBs Apart

Having deployed NewSQL databases in enterprise environments, I've found these capabilities particularly valuable:

Distributed transactions: Maintaining ACID guarantees across geographically distributed nodes
Horizontal scalability: Adding capacity by simply adding more nodes to the cluster
SQL compatibility: Supporting standard SQL interfaces and tools despite the distributed architecture
Automatic rebalancing: Redistributing data as the cluster grows or shrinks without manual intervention
Strong consistency models: Providing linearizable consistency for critical operations when needed

Recent innovations have further enhanced NewSQL capabilities:

Multi-region deployments: Spanning multiple geographic regions while maintaining consistency guarantees
Hybrid transactional/analytical processing (HTAP): Supporting both OLTP and OLAP workloads from the same database
Serverless offerings: Consumption-based pricing with automatic scaling
Built-in streaming capabilities: Processing data streams alongside traditional database operations
Specialized storage engines: Optimizing for different workload characteristics within the same system

Popular Use Cases: NewSQL Databases

NewSQL databases excel in scenarios where traditional relational databases hit scaling limitations but applications still require strong consistency:

Global SaaS Platforms: Multi-tenant software platforms leverage NewSQL databases to scale horizontally across datacenters while maintaining transactional integrity for each customer's operations. The ability to add capacity by adding nodes rather than vertically scaling enables these businesses to grow efficiently while preserving the SQL model that their applications were built on.
Financial Systems: Banking and fintech applications use NewSQL databases to combine the strict consistency requirements of financial transactions with the ability to scale to millions of users and transactions. Their strong consistency guarantees ensure accurate account balances and transaction histories, while the distributed architecture provides both scalability and resilience against regional outages.
E-commerce Platforms: Online retailers implement NewSQL databases to handle massive transaction volumes during peak shopping periods while maintaining consistent inventory, order processing, and customer data. The horizontal scaling model allows them to temporarily increase capacity for seasonal peaks without rebuilding their data architecture.
Gaming Backends: Multiplayer game platforms use NewSQL databases to manage player data, inventories, and in-game economies with strict consistency requirements. The distributed architecture supports millions of concurrent players across global regions while ensuring that critical game state remains consistent and transactions like purchases or trades maintain ACID properties.
Healthcare Record Systems: Medical institutions deploy NewSQL databases to manage patient records that require both strict consistency for critical care data and the ability to scale across hospital networks. The SQL interface maintains compatibility with existing healthcare applications while the distributed architecture provides resilience and scaling capability.
IoT Data Management: Industrial IoT platforms use NewSQL databases as the system of record for device state and configuration while maintaining the ability to scale to millions of connected devices. The ACID transactions ensure reliable device management, while the scalable architecture handles the continuous growth in connected systems.

Head-to-Head Comparison: Vector DB vs NewSQL DB


Feature	Vector Databases (Milvus, Zilliz Cloud)	NewSQL Databases (CockroachDB, Spanner)	Why It Matters
Primary Data Model	High-dimensional vectors with metadata	Relational tables with traditional SQL schema	Determines how you model your domain concepts and what operations are efficient
Core Query Capability	Similarity search and nearest neighbor queries	SQL queries with distributed transactions	Defines the fundamental operations your application can perform efficiently
Consistency Model	Usually eventual consistency with tunable options	Strong consistency with ACID guarantees	Impacts application correctness and behavior during concurrent operations
Scaling Approach	Optimized for read-heavy similarity search	Balanced scaling for both reads and writes	Affects how your database grows with increasing data and traffic
Transactional Support	Limited or non-existent	Full ACID transactions across distributed clusters	Determines the reliability of critical business operations
Primary Strength	Finding similar items based on embeddings	Scaling relational workloads horizontally	Aligns database strengths with your core application needs
Query Language	Vector-specific APIs, similarity functions	Standard SQL with distributed extensions	Influences developer learning curve and query expressiveness
AI Integration	Native support for embeddings and similarity	Often requires extensions or separate systems	Determines out-of-box readiness for AI-powered features
Geo-Distribution	Typically single-region with replication	Native multi-region support with consistency controls	Affects global application deployment and latency
Development Familiarity	New paradigm for most teams	Familiar SQL model with distributed considerations	Impacts team onboarding and development velocity

Vector Databases In Action: Real-World Success Stories

Vector databases shine in these use cases:

Retrieval-Augmented Generation (RAG) for Enterprise Knowledge

A global consulting firm implemented a RAG system using Zilliz Cloud to power their internal knowledge platform. They converted millions of documents, presentations, and project reports into embeddings stored in a vector database. When consultants ask questions, the system retrieves the most relevant context from their knowledge base and passes it to a large language model to generate accurate, contextually relevant answers.

This approach dramatically improved knowledge discovery, reduced research time by 65%, and ensured responses were grounded in the firm's actual experience and methodologies rather than generic LLM outputs. The vector database was critical in enabling real-time retrieval across massive document collections while maintaining sub-second query response times.

See more RAG case studies:

Agentic RAG for Complex Workflows

Agentic RAG is an advanced RAG framework that enhances the traditional RAG framework by incorporating intelligent agent capabilities. A healthcare technology provider built an agentic RAG system that uses vector search to power a clinical decision support tool. The system stores medical knowledge, treatment guidelines, and patient case histories as embeddings in a vector database. When physicians input complex patient scenarios, the agentic system:

Decomposes the complex query into sub-questions
Performs targeted vector searches for each sub-question
Evaluates and synthesizes the retrieved information
Determines if additional searches are needed
Delivers a comprehensive, evidence-based response

This advanced implementation reduced clinical decision time by 43% and improved treatment recommendation accuracy by 28% in validation studies. The vector database's ability to perform multiple rapid similarity searches with different contexts was essential for the agent's multi-step reasoning process.

The DeepSearcher, built by Zilliz Engineers, is a prime example of agentic RAG and is also a local, open-source alternative to OpenAI’s Deep Research. What sets DeepSearcher apart is its unique combination of advanced reasoning models, sophisticated search features, and an integrated research assistant. By leveraging Milvus (a high-performance vector database built by Zilliz) for local data integration, it delivers faster and more relevant search results while allowing easy model swapping for customized experiences.

Semantic Search Beyond Keywords

A legal technology company replaced their traditional keyword-based search with a vector database-powered approach, allowing attorneys to search across case law, statutes, and legal documents with natural language queries instead of Boolean search syntax. Their vector database indexed embeddings of millions of legal documents, capturing the semantic meaning of complex legal concepts.

After implementation, search relevance improved by 48%, search abandonment decreased by 35%, and attorneys reported saving an average of 3-5 hours per week on legal research tasks. The vector database handled their entire legal corpus of over 12 million documents while maintaining consistent sub-100ms query response times.

See more semantic search case studies:

AI-Powered Image Search

A digital asset management platform implemented visual search using a vector database to store embeddings of their clients' image libraries. Marketing teams could now upload reference images to find visually similar assets across their entire media library—a capability impossible with their previous metadata-based search.

This feature increased user engagement by 56% and reduced time spent searching for suitable assets by 62%. The vector database effectively handled libraries ranging from thousands to millions of images per client while maintaining search latency under 200ms, even for the largest collections.

See more image search case studies:

NewSQL Databases in Action: Real-World Success Stories

NewSQL databases excel in these scenarios:

Global Financial Platform Scale-Out

A fintech company migrated their payment processing system from a traditional relational database to a distributed NewSQL database to support their international expansion. Their previous system struggled with cross-region transactions and couldn't scale horizontally to meet growing demand.

The NewSQL implementation used a multi-region deployment with distributed transactions to ensure payment consistency across global operations. This architecture reduced payment processing latency by 73% for international customers while maintaining strict ACID guarantees for financial transactions. The system now handles over 12,000 transactions per second during peak periods with 99.995% availability, all while maintaining the familiar SQL interface that their development team was already proficient with.

E-commerce Platform Transformation

A rapidly growing e-commerce company replaced their sharded MySQL implementation with a NewSQL database to eliminate the scaling limitations they faced during seasonal shopping peaks. Their previous approach required complex application logic to handle cross-shard transactions and struggled with consistent inventory management across shards.

The NewSQL solution provided automatic sharding while maintaining transactional integrity for orders, inventory, and customer data. This implementation handled a 300% increase in transaction volume during Black Friday without performance degradation, reduced database-related outages from several per month to zero in the past year, and eliminated the need for application-level sharding logic—allowing developers to focus on features rather than data distribution.

SaaS Application Scaling

A B2B software company moved their multi-tenant application from a traditional relational database to a NewSQL platform to support their growing enterprise customer base. Their previous single-instance database couldn't scale to meet the needs of larger customers and created performance isolation challenges between tenants.

The NewSQL database allowed them to horizontally scale as customer numbers grew while maintaining strict isolation between tenant data. Performance for large enterprise customers improved by 220%, database operational costs decreased by 40% despite handling 5x more data, and the team maintained their existing SQL-based application code with minimal changes.

Benchmarking Your Vector Search Solutions on Your Own

VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.

VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.

Download VectorDBBench.

Check out the VectorDBBench Leaderboard for a quick look at the performance of mainstream vector databases.

Decision Framework: Choosing the Right Database Architecture

After helping numerous organizations make this decision, I've developed this practical framework:

Choose a Vector Database When:

AI-powered similarity search is your core value proposition - Your application primarily revolves around finding related items based on semantic or perceptual similarity
You're working with embeddings from machine learning models - Your data naturally exists as vectors from language models, image encoders, or other AI systems
Approximate results are acceptable for improved performance - Your use case can tolerate the imperfect precision of ANN algorithms in exchange for speed
Query patterns focus on "what's similar to this?" - Your primary operations involve finding nearest neighbors in high-dimensional space
Strong transactional guarantees are less critical than search performance - Your application prioritizes fast similarity search over strict consistency guarantees

Choose a NewSQL Database When:

Transactional integrity is non-negotiable - Your application handles financial, healthcare, or other critical data requiring ACID guarantees
You need to scale relational workloads horizontally - You've hit the scaling limits of traditional RDBMSs but must maintain the relational model
SQL compatibility is a requirement - Your team and tooling are built around SQL and relational concepts
Multi-region consistency matters - Your application needs to maintain consistency across geographic boundaries
You're handling both OLTP and analytical workloads - Your application needs to support both transactional and analytical operations efficiently

Consider a Hybrid Approach When:

Your application has clearly distinct workloads - Some features require similarity search while others need transactional guarantees
Data flows naturally between transactional and AI components - Your workflow involves processing transaction data for AI analysis
Different teams maintain different application components - Your organization has separate teams for transaction processing and AI features
Latency requirements differ between components - Some operations need sub-millisecond responses while others can tolerate longer latencies

Consider NewSQL with Vector Extensions When:

Your primary need is transactional with occasional vector search - Strong consistency is your main requirement with some AI capabilities
Operational simplicity trumps specialized performance - Managing a single database system is a higher priority than maximizing vector search performance
Your vector search needs are moderate - Both in terms of collection size and dimensionality
Data consistency between transactions and vectors is critical - You need vector operations to see immediately consistent data after transactions

Implementation Realities: What I Wish I Knew Earlier

After implementing both database types across multiple organizations, here are practical considerations that often get overlooked:

Resource Planning

Vector databases typically require significant memory for indexes, often 2-3x what you might initially estimate based on raw data size
NewSQL databases can have higher CPU requirements than traditional RDBMSs due to the overhead of distributed consensus protocols
Scaling patterns differ fundamentally: vector databases often scale with embedding dimensions and collection size, while NewSQL databases typically scale with transaction volume and query complexity

Development Experience

Query paradigms differ significantly between these database types, requiring different mental models from your development team
NewSQL databases introduce distributed systems concepts like consistency levels and partition tolerance that traditional SQL developers may not be familiar with
Vector search requires understanding of embedding models, dimensionality reduction, and similarity metrics that traditional database developers may not have experience with

Operational Realities

Monitoring needs vary dramatically, with vector databases requiring attention to index performance and NewSQL databases focusing on consensus metrics and distributed transaction latency
Backup and recovery strategies differ substantially, with NewSQL databases often having more sophisticated point-in-time recovery capabilities
Maintenance operations like version upgrades can be more complex in distributed systems, often requiring careful orchestration to maintain availability

Conclusion: Choose the Right Tool, But Stay Flexible

The choice between vector databases and NewSQL databases isn't about picking a winner—it's about matching your database architecture to your specific requirements for consistency, query patterns, and scalability.

If your core use case involves finding similar items or semantic relationships, a vector database likely makes sense as your foundation. If your fundamental need is scalable transactions with strong consistency guarantees, a NewSQL database is probably your starting point.

The most sophisticated data architectures I've helped build don't shy away from specialized databases—they embrace them while creating clean interfaces that hide complexity from application developers. This approach gives you the performance benefits of specialized systems while maintaining development velocity.

Whatever path you choose, the key is building with enough flexibility to evolve as both your requirements and the database landscape continue to change. The convergence between vector capabilities and NewSQL's distributed transaction processing is just beginning, and the most successful architectures will be those that can adapt to incorporate the best of both worlds.

Updated on Apr 07, 2025

Chloe Williams
Chloe Williams is a technical writer at Zilliz.

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