9 Most Popular Vector Databases: How to Choose

The tool you use to build a quick proof-of-concept is rarely the right one for a scalable, production-grade system. This is especially true for vector databases. While a lightweight library might work for a demo, it often lacks the clustering, security, and high-availability features needed to support a real application with actual users. Making the wrong choice early on can lead to painful migrations and costly refactoring down the line. This article is designed to help you make a strategic decision from the start. We’ll break down the most popular vector databases, highlighting which are best for experimentation and which are truly ready for production.

Key takeaways

• Vector databases are essential infrastructure for modern AI: They power search, memory, and retrieval across RAG, agentic, and multimodal systems.
  
  

• Open-source options provide better control, scalability, and integration: Milvus, Weaviate, Qdrant, and others support hybrid search, flexible deployment, and production-grade performance.
  
  

• Cake helps teams operationalize vector search at every stage: From prototyping to production, Cake makes it easy to deploy, scale, and secure vector DBs as part of a complete AI stack.

Vector databases are what made Retrieval-Augmented Generation (RAG) pipelines possible. They enable fast, semantic search across unstructured data, giving LLMs access to external knowledge far beyond their training set.

But their role doesn’t stop there.

Today, vector databases are foundational to agentic AI systems. They store long-term memory, track interactions over time, and retrieve relevant context across tools, modalities, and workflows. Whether you’re building a customer support agent, a research copilot, or a multi-step planner with tool access, vector search is one of the core building blocks.

Not all vector databases serve the same needs. Some are open and modular. Others are closed and tightly integrated. The right choice depends on the type of search you need, how your system is architected, and the level of control you want.

This guide breaks down the top vector databases available in 2025, focusing on open vs. closed options, technical trade-offs like hybrid search, and what to consider when deploying these tools in real production pipelines.

What is a vector database?

Think of a traditional database as a perfectly organized filing cabinet. If you know the exact label on a folder, you can retrieve it instantly. A vector database is more like a library where books are grouped by their meaning and context, not just their titles. You can find books about "epic journeys" even if that phrase isn't on the cover. They are specialized databases built to store and search through "vector embeddings"—numerical codes that represent complex data like text, images, or audio. These embeddings capture the data's underlying meaning, making them the engine behind many modern AI features like smart search engines, recommendation systems, and AI assistants that need to understand context.

How vector databases work

So, how does a database understand the "vibe" of a photo or a paragraph? It all starts with a process called embedding, where an AI model converts unstructured data into a list of numbers, known as a vector. This vector acts like a coordinate, placing the data in a vast, multi-dimensional space where similar items cluster together. When you submit a search query, whether it's a sentence or an image, it also gets converted into a vector. The database then uses highly efficient search methods, like Approximate Nearest Neighbor (ANN) search, to find the vectors—and the original data they represent—that are closest to your query. This is how you can search for "cozy living room ideas" and get relevant images, even if those exact words aren't in the file descriptions.

How they compare to traditional databases

It’s important to clarify that vector databases don't replace traditional databases; they work alongside them. A traditional database, like a SQL database, is designed for structured data—think neat rows and columns of customer information or sales figures. It’s fantastic at finding exact matches, like pulling up a specific order number. Vector databases, however, are built for the complex, high-dimensional data used in AI and focus on finding similar or related items. In a real-world AI application, you’ll often find both working in tandem. The traditional database might handle user accounts and billing, while the vector database powers the app's semantic search or personalization features. Integrating these systems seamlessly is a core challenge in operationalizing AI, which is why having a managed stack can make all the difference.

Vector search vs. hybrid search: which one do you need?

Before choosing a database, you need to understand what kind of search your application requires. Most teams reach for a vector database when they want semantic search—finding relevant results based on meaning rather than exact keywords. Vector embeddings make this possible by turning data into dense representations that can be compared by similarity.

But dense search alone often isn’t enough.

Many real-world applications benefit from hybrid search, which combines dense (semantic) and sparse (lexical) approaches. This allows your system to capture both meaning and precision, which is especially important when working with structured documents, technical language, or regulatory data.

Here’s a quick comparison:

If your use case requires both accuracy and nuance (e.g., legal search, patient data retrieval, or multi-turn agent workflows) hybrid search isn’t optional. It’s essential.

Some databases like Weaviate, Qdrant, and Milvus offer native support for hybrid search. Others require bolting on external indexes or managing separate pipelines for sparse and dense retrieval. This capability should be a key consideration when evaluating your stack.

If your use case requires both accuracy and nuance (e.g., legal search, patient data retrieval, or multi-turn agent workflows) hybrid search isn’t optional. It’s essential.

Open-source vs. closed vector databases: what's best for your project?

Many teams start with a hosted vector database because it’s the fastest way to get something running. Closed platforms like Pinecone or Vertex AI are simple to launch and manage, but that convenience often comes at the cost of flexibility, performance, and long-term control.

In contrast, open-source vector databases give teams full visibility into how search works, the ability to optimize for specific workloads, and the flexibility to deploy in any environment. They’re easier to tune, cheaper to scale, and more composable—especially important when building agentic systems that evolve over time.

Here’s a breakdown of the most widely used vector databases in both categories, with a focus on real production trade-offs.

The pros and cons of closed/SaaS vector databases

The pros and cons of open-source vector databases

How to choose the right vector database for you

The answer depends not just on features, but on where you are in the lifecycle of your AI system. Some tools are great for fast iteration. Others are built for real-world performance at scale. Knowing which phase you’re in, i.e., prototyping vs. production, can save you a lot of time and refactoring later.

BLOG: Deploy AI Models to Production Nearly 4X Faster with Cake

Performance and scalability

When your AI application moves from a demo to a production environment, performance becomes paramount. It’s not just about raw query speed; it’s about consistently low latency and high throughput, even as user load increases. Modern AI systems, from RAG pipelines that need to retrieve context instantly to agentic workflows that perform multiple searches in a sequence, depend on a vector database that can keep up without faltering. This is why evaluating a database on its ability to scale is just as important as its search accuracy. Look for features like horizontal scaling, sharding, and replication to ensure your AI infrastructure can grow with your user base and data volume.

Developer experience and ease of use

It’s tempting to start with a fully managed, closed-source vector database. The initial setup is often faster, letting you build a proof-of-concept quickly. However, this convenience can lead to long-term constraints. As your project matures, you might find yourself limited by the platform's specific architecture, facing vendor lock-in, or struggling with unpredictable costs at scale. In contrast, open-source databases offer complete transparency and control. They give your team the flexibility to inspect the code, optimize performance for your unique workload, and deploy the system in any cloud or on-premise environment, ensuring it aligns perfectly with your operational needs.

Community, support, and cost

Technical features are only part of the story. The ecosystem around a database is equally critical for long-term success. A vibrant open-source community provides a wealth of documentation, tutorials, and peer support, making it easier to troubleshoot issues and find talent. When it comes to cost, it's important to consider the total cost of ownership (TCO). While a SaaS solution has a predictable subscription fee, self-hosting an open-source database involves infrastructure and operational costs. However, for large-scale deployments, the control and efficiency of an open-source solution, especially when managed through a streamlined platform like Cake, often result in significant long-term savings.

Security and integration

Your vector database must fit seamlessly and securely within your existing infrastructure. From an integration standpoint, consider how well it connects with your data sources, MLOps pipelines, and application frameworks. For example, a database with native hybrid search support simplifies your architecture compared to one that requires you to manage separate systems for dense and sparse retrieval. On the security front, enterprise-grade features like role-based access control (RBAC), data encryption, and the ability to deploy within a private network are essential. Open-source tools give you the ultimate control to implement these security measures according to your organization's specific policies, which is a major advantage for regulated industries.

Are you prototyping or in production? Your database choice matters

Not every vector database is built for long-term use in production systems. Some are optimized for fast iteration. Others are designed for durability, observability, and performance at scale.

Choosing a database for your prototype

Tools like Chroma and FAISS are ideal when you’re experimenting with retrieval patterns or building early-stage demos. They’re fast to spin up, easy to use locally, and great for validating search quality. But they lack core production features like clustering, auth, observability, and hybrid scoring.

Choosing a database for production

Databases like Milvus, Weaviate, and Pinecone offer the features needed to support real workloads—multi-node clustering, high availability, granular access control, and hybrid search out of the box. These capabilities are critical when vector search becomes a core part of a deployed AI application.

Starting with the right tool for your use case and maturity level helps avoid rework, migration pain, and unexpected scaling issues down the line. And with Cake, you can prototype with one stack and transition to production without rewriting your pipelines.

Cake doesn’t replace vector databases; it gives you a production-ready foundation to run them as part of a modern AI system.

Exploring the most popular vector databases

With so many options on the market, it can be tough to figure out which vector database is the right fit. The best choice often comes down to your specific needs for performance, scalability, and the kind of search you're implementing. Are you building a simple prototype or a production-grade system that needs to handle millions of users? Let's walk through some of the most popular open-source and commercial options to see how they stack up. Many of these are powerful but complex to manage, which is why platforms like Cake exist—to streamline the deployment and management of the entire AI stack, including your chosen vector database.

Milvus

If you're dealing with massive datasets, Milvus is a name you'll hear a lot. It's an open-source, high-performance database specifically built for scale. As one source notes, "Milvus is an open-source, high-performance database built for searching billions of vectors. It works well with major deep learning tools like TensorFlow and PyTorch." This makes it a go-to for enterprise applications where you can't afford performance bottlenecks. Its distributed architecture is designed for high availability and resilience, ensuring your AI applications remain responsive even under heavy load. Milvus is a fantastic choice for production systems that require robust, scalable vector search capabilities from the get-go.

Qdrant

For applications that need speed and real-time responsiveness, Qdrant is a strong contender. It’s known for its efficiency, which is a major advantage in interactive AI systems. According to ZenML, "Qdrant is a high-performance database written in Rust, efficient for real-time applications. It allows filtering search results based on other data." This ability to apply filters during a search is incredibly useful, as it lets you narrow down semantic search results with specific metadata—like finding products in a certain category that are also conceptually similar to a user's query. Its performance and developer-friendly API make it a popular choice for teams building sophisticated RAG pipelines.

Weaviate

Weaviate stands out for its speed and powerful hybrid search capabilities, combining keyword-based and vector search to deliver more accurate results. It's an open-source database that can handle enormous amounts of data. As one review puts it, "Weaviate is an open-source, graph-based vector database known for speed and hybrid search. It can store billions of data objects and vectors." This makes it particularly effective for complex use cases where both the meaning (semantic) and specific terms (lexical) are important. Weaviate also offers features like data classification and question answering, making it more than just a storage and retrieval system.

MongoDB Atlas Vector Search

For teams already using MongoDB, Atlas Vector Search is a compelling option because it integrates vector capabilities directly into a familiar environment. This eliminates the need to manage a separate database for your vector embeddings. DataCamp highlights this advantage, stating, "MongoDB Atlas Vector Search allows developers to store and query vector embeddings directly inside any collection, combining operational and vector data in a single document database." This unified approach simplifies the data architecture, making it easier to build and maintain applications that leverage both traditional and AI-driven search without adding another piece of infrastructure to your stack.

Vespa

Vespa is an enterprise-grade platform that has been battle-tested at a massive scale. Originally developed by Yahoo, it's designed for applications that require a combination of search types. TrueFoundry describes it as "an open-source, enterprise-level platform from Yahoo for large-scale search and personalization, combining many types of search (keywords, structured data, vectors)." This makes Vespa a powerhouse for complex recommendation engines, personalization systems, and large-scale search applications where you need to blend different retrieval methods. It’s a great fit for mature organizations that need a single platform to handle diverse and demanding search workloads.

Marqo

Marqo is an interesting open-source option designed with multi-modal search in mind. This means it's built to handle not just text but also images and other data types, making it highly versatile for modern AI applications. As noted by ZenML, "Marqo is designed for multi-modal search and is a good option for those looking to integrate vector search capabilities into their applications." If your project involves searching across different types of content—for example, finding images that match a text description—Marqo provides the tools to build that functionality right out of the box, simplifying what would otherwise be a complex engineering challenge.

Databases with built-in vector support

Not every team wants or needs a dedicated vector database. Sometimes, it makes more sense to use a familiar tool that has added vector support. This approach is perfect for teams looking to experiment with vector search without overhauling their existing data infrastructure. It allows you to leverage your current operational database and the expertise your team already has. While they might not offer the same specialized performance as a purpose-built solution like Milvus or Qdrant, they provide a practical and efficient on-ramp to building AI-powered features.

Redis

Many developers know Redis as a fast, in-memory key-value store, but it has also evolved to support vector search. This makes it a versatile choice for applications that already rely on Redis for caching or other real-time tasks. One developer on Reddit explained their strategy, noting that "Redis can handle vectors and other types of data or features, making it a versatile choice for many projects." By using Redis, you can add semantic search capabilities to your application while keeping your tech stack lean and leveraging Redis's renowned speed for low-latency queries.

SingleStore

SingleStore is another database that offers a unified approach, combining transactional, analytical, and vector workloads in one platform. This can dramatically simplify your data architecture by eliminating the need to move data between different systems. As one user pointed out, "SingleStore is another option that can handle both operational and vector data, providing a unified approach to data management." For businesses that need to perform real-time analytics alongside vector search—for example, in fraud detection or real-time personalization—SingleStore offers a powerful, integrated solution.

Market leader insights: Microsoft Azure

When looking at the broader market, it's clear that major cloud providers are making vector search a core part of their AI platforms. Microsoft, in particular, has deeply integrated vector capabilities across its ecosystem. According to MarketsandMarkets, "Microsoft (Star) leads the vector database market with deep integration across Azure AI, Fabric, and Cognitive Services, offering enterprise-grade vector search, indexing, and retrieval at a global scale." This all-in-one approach is attractive for enterprises already invested in the Azure ecosystem, as it offers a seamless, managed experience. However, it also represents the "closed" model, which can lead to vendor lock-in and less flexibility compared to open-source alternatives.

How Cake unifies and manages your vector DBs

Cake doesn’t replace vector databases; it gives you a production-ready foundation to run them as part of a modern AI system.

Whether you’re embedding memory into an agentic application or powering semantic search in a RAG pipeline, Cake supports seamless orchestration of vector DBs like Milvus, Weaviate, Qdrant, and PGVector. You can prototype locally with tools like Chroma, then move to a production-grade deployment without rewriting your stack.

Cake manages the infrastructure around your vector DBs so you don’t have to:

• Deploy vector DBs alongside LLMs, toolchains, and orchestration layers
  
  
• Optimize retrieval with prebuilt patterns for chunking, embeddings, and hybrid search
  
  
• Secure your system with built-in auth, compliance, and VPC isolation
  
  
• Monitor performance with real-time observability and feedback loops

This unified layer helps teams go from prototype to production faster, without sacrificing flexibility or control.

Where you'll find vector databases in the wild

Vector databases are a core enabler of intelligent AI systems. They allow teams to store and retrieve high-dimensional embeddings that capture meaning, behavior, and structure. This makes it possible to search, match, and analyze complex data that traditional keyword search can’t handle.

Here’s how organizations are using vector databases in production:

E-commerce and recommendations

Retailers use vector search to deliver personalized product recommendations that reflect user intent, not just past purchases. By embedding behavior and product metadata, systems can identify semantically similar items and increase relevance.

Cake provides hybrid retrieval patterns and production-ready orchestration so e-commerce teams can move from simple filters to full AI-driven personalization.

NLP and conversational agents

In RAG pipelines and agent-based systems, vector databases help retrieve relevant documents, long-term memory, or tool contexts in real time. This improves answer quality, reduces hallucinations, and enables multi-turn reasoning.

Cake simplifies the process of wiring up vector search, memory stores, and orchestration tools into a unified conversational system.

Computer vision and image search

Images can be embedded into vectors that capture visual similarity. This allows teams to build systems for reverse image search, visual deduplication, and multimodal tagging.

Cake supports image embeddings and search alongside text and tabular data, making it easier to build multimodal retrieval systems with shared infrastructure.

Financial services and fraud detection

Banks and fintech companies embed transactional and behavioral data to detect anomalies and match against known fraud patterns. Vector databases support fast, similarity-based detection that adapts as behavior shifts.

Cake helps teams deploy these pipelines with compliance controls, real-time scoring, and observability built in.

Healthcare and medical imaging

Clinicians and researchers use vector search to compare patient cases, scan results, and medical histories. This helps surface similar diagnoses or treatments and supports clinical decision-making.

Cake enables healthcare teams to run secure, compliant retrieval pipelines that integrate with diagnostic models and downstream applications.

Answering your top questions about vector databases

Choosing the right vector database is about more than just benchmarks. It’s about finding the balance between flexibility, scalability, and integration across your AI system.

Open-source vector databases offer the most long-term control and composability, especially for teams building agentic systems that evolve over time. But even the best vector DB needs the right infrastructure around it.

That’s where Cake comes in. We don’t just help you choose the right tool—we give you the orchestration layer to manage it, secure it, and scale it across your AI workflows. Whether you’re starting with a prototype or deploying a production-grade system, Cake helps you move faster with fewer trade-offs.

Ready to build something real? Let’s talk.

Related articles

• Why 90% of Agentic RAG Projects Fail (and How Cake Changes That)

• What Is Agentic AI? (And Why It Changes Everything)

• How to Cut AI Model Deployment Time by 4X

• LLMOps Explained

Frequently asked questions

Which vector databases are open source vs. closed/commercial?

Open-source vector databases include Milvus, Weaviate, Qdrant, PGVector, Chroma, and FAISS. These give you full control over deployment, tuning, and customization.

Closed or commercial options include Pinecone, ElasticSearch’s vector module, AWS S3 Vector Store, and Google Vertex AI Vector Search, which are typically easier to launch but less flexible in how and where they can be deployed.

What are the trade-offs between these systems in terms of scale, features, availability, and security?

Open-source systems generally offer more flexibility and transparency, but may require more hands-on management. Some, like Milvus and Weaviate, support high availability and hybrid search, making them suitable for large-scale production use. Others, like Chroma or PGVector, are easier to use but more limited in scaling and clustering.

Closed systems like Pinecone and Vertex AI often include built-in features like access control and monitoring, but come with vendor lock-in, added cost, and limited portability.

Which databases are credible for production vs. prototyping?

For production, databases like Milvus, Weaviate, Qdrant, and Pinecone are commonly used due to their support for clustering, hybrid search, and performance at scale.

For prototyping, tools like Chroma and FAISS are lightweight and easy to set up, but lack key production features like observability, availability, and security filtering.

Where do hybrid search and filtering fit into the picture?

Hybrid search—combining dense (semantic) and sparse (lexical) search—is critical in many real-world applications where precision and context both matter. Databases like Weaviate, Qdrant, and Milvus support hybrid search natively.

Security filtering is also essential for multi-tenant systems or regulated data. Pinecone and Weaviate are among the few with strong native support for document-level filtering.

Cake makes it easier to take advantage of both by orchestrating vector DBs with hybrid pipelines, access control, and search filtering as part of the full deployment stack.