What is the best vector database for your AI Agent?

In today’s context of generative artificial intelligence and applications that combine multimodal data, vector databases have become key components for achieving advanced semantic experiences, from intelligent searches to autonomous assistants.

Since we’ve been working with these technologies, we decided to do a comparative analysis of the main solutions on the market, which we’re sharing below, but first:

Why do vector bases matter?

Modern AI agents need to interpret and operate on text, images, audio, and other types of unstructured data. Vector databases allow this information to be represented in a high-dimensional vector space and queried based on semantic similarity—an essential capability for tasks such as Retrieval-Augmented Generation (RAG), contextual chatbots, and recommendation engines.

Here is an image of how it works:

Vector Database Comparison (now yes)

🟢 Pinecone: Agility to build quickly

Ideal for startups and products that need to go to market without operational overhead.

• Arquitectura: Serverless y gestionada, con latencias <50ms.
• Use cases: Customer service with RAG, searches on SaaS platforms.
• Advantages: Easily integrates with ML frameworks.
• Caution: Increasing costs if the reading load is high.

🟣 Milvus: Open-source scalability

Recommended for scenarios with high data volume and mature technical teams.

• Architecture: Distributed, allows decoupling of computing and storage.
• Use cases: Indexing technical documents, complex recommenders.
• Advantages: Supports multiple indexing algorithms.
• Care: Requires fine tuning of parameters for optimal performance.

🟡 Weaviate: Contextual intelligence with a graph model

Powerful for scenarios where relationships between entities matter as much as vectors.

• Architecture: Supports triplets (graph) and multimodal embeddings.
• Use cases: LegalTech, media, multimedia content classification.
• Advantages: Embeddings during ingestion, rich relational searches.
• Cautions: It can make the architecture more complex if only a simple search is needed.

🔴 Qdrant: Efficiency for the edge

Designed for resource-constrained solutions with low latency requirements.

• Architecture: Based on Rust, optimized for efficiency.
• Use cases: Mobile apps with visual search, fake news detection.
• Advantages: Native geofilters, low RAM consumption.
• Care: Although very efficient, its ecosystem is smaller than other options.

🔵 Supabase + pgvector: Consistency and flexibility

Ideal for those already using PostgreSQL and need vector capabilities without sacrificing ACID transactions.

• Architecture: Extends PostgreSQL with vector support.
• Use cases: Fintech, Legal, mixed solutions with transactional logic and embeddings.
• Advantages: Low operating cost and familiarity with SQL.
• Caution: Not optimized for highly concurrent vector queries.

And what base is appropriate for your case?

Here are some real use cases where vector bases make a difference.

• Customer Service with RAG Agents: Companies like Vanguard have achieved double-digit reductions in response times by combining Pinecone with LLMs to retrieve contextual knowledge.
• Technical indexing for engineering: Shell implemented Milvus to assist engineers by retrieving technical documentation contextually.
• Audiovisual classification at scale: Streaming platforms use Weaviate with video and audio embeddings to automatically label content.
• E-commerce tips: Qdrant allows for image and behavioral searches, which is crucial for mobile apps where UX is vital.

Our recommendation

From our AI and Data Engineering team, we see an emerging pattern: hybrid solutions. For example, storing vectors in Pinecone or Qdrant for performance, and metadata in PostgreSQL with pgvector for governance and complex queries. This hybrid architecture combines the best of both worlds: speed and structure.

Are you designing an AI agent for your business?

At Tekne, we help companies of all sizes implement robust, optimized, and secure vector architectures for their AI workflows. If you’re thinking about building conversational solutions, intelligent search engines, or recommenders with generative AI, don’t hesitate to send us a message.

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Fuentes de la nota:

• https://www.datacamp.com/es/blog/the-top-5-vector-databases
• https://pynomial.com/2021/10/open-source-vector-databases-overview/
• https://redis.io/solutions/vector-database/
• https://www.linkedin.com/pulse/3-ways-vector-databases-take-your-llm-use-cases-next-level-mishra/