Top 10 Vector Databases: Choosing the Right One for Your Project

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.

Vector search vs. hybrid search: What’s the difference?

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 right for your AI stack?

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.

Closed/SaaS vector databases

Open-source vector databases

So how do you choose the right one?

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

Prototyping vs. production: choose based on where you are

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.

For prototyping

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.

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.

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.

Common uses for vector databases

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.

How to move forward

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.