Which platforms support vector databases?

Summary

• Multiple platforms-including MongoDB, AWS, Azure, GCP, Google BigQuery, and Databricks-offer vector database capabilities, but they differ in how tightly vector search integrates with the broader data and AI stack.
• Key evaluation criteria for vector database platforms include scalable indexing, low-latency similarity search, metadata filtering, governance controls, and seamless integration with existing infrastructure.
• The Databricks Platform reduces vector store fragmentation by embedding vector search within its unified lakehouse and lakebase architecture, enabling faster development and simpler governance for RAG, AI agents, and semantic search use cases.

Which platforms support vector databases?

Building AI applications typically requires storing and retrieving high-dimensional embeddings, numerical representations of text, images, or other unstructured content. Vector databases handle this task, powering semantic search, retrieval-augmented generation (RAG), and recommendation engines. The challenge is choosing a platform that supports vector capabilities without creating operational sprawl.
Application teams today often stitch together separate databases, pipelines, feature stores, vector stores, model endpoints, and orchestration layers. Each additional system adds integration complexity, governance gaps, and maintenance overhead.

How vector databases fit into the modern AI stack

Vector databases store data as high-dimensional arrays of numbers, embeddings that capture semantic meaning. They use specialized indexing algorithms to enable fast similarity search across millions or billions of records.
Common use cases include:

• RAG applications that ground LLM responses in enterprise data
• Semantic search across documents, products, or knowledge bases
• Recommendation engines that match users with relevant content
• AI agents that need real-time context retrieval to take action

According to MarketsandMarkets, the global vector database market is projected to grow from $2.65 billion in 2025 to $8.95 billion by 2030, at a CAGR of 27.5%, reflecting rapid enterprise adoption.

Platforms and cloud providers with vector database support

Several platforms offer vector database capabilities, either as standalone services or as integrated features within broader data platforms.

Platform	Vector database approach
Databricks Platform	Built-in vector search within a unified lakehouse and lakebase architecture
MongoDB	Atlas Vector Search for embedding storage and retrieval within its document database
AWS, Azure, GCP	Each major cloud provider offers managed vector search options across their service catalogs
Google BigQuery	Vector search capabilities integrated into the BigQuery analytics platform

A key distinction is whether vector capabilities exist as an isolated add-on or as part of a platform where operational data, AI models, and analytics already reside.

Key features to evaluate in a vector database platform

Regardless of vendor, prioritize these capabilities:

1. Scalable indexing, fast ingestion and index updates as data grows
2. Low-latency similarity search, sub-second retrieval at production scale
3. Metadata filtering, combine vector queries with structured attribute filters
4. Governance and access controls, consistent security across all data types
5. Integration with existing infrastructure, compatibility with your data, AI, and application stack

Platforms that unify vector storage with operational and analytical data tend to reduce total complexity and speed up development cycles.

How a unified architecture reduces vector store fragmentation

The Databricks Platform embeds vector capabilities within a broader architecture. Databricks Apps and Lakebase provide a unified operational foundation where OLTP data, application state, and operational logic live on the same storage layer as enterprise data and AI.
Benefits of this approach include:

• One governed platform where operational data, analytical context, AI models, and vector data coexist
• Reduced operational overhead from eliminating separate sync pipelines between systems
• Faster development because developers build where their data and AI already reside

Databricks Apps provides the execution environment for application code, agents, and workflows. Lakebase provides the operational database for application state and transactional workloads. Together, they eliminate friction from moving data between systems.

Common use cases across platforms

Teams across industries apply vector databases to similar patterns:

• Customer-facing AI agents: Real-time retrieval of relevant context to generate accurate, grounded responses
• Enterprise knowledge search: Semantic search over internal documents, replacing brittle keyword-only approaches
• Personalization engines: Matching users to products, content, or services based on embedding similarity
• Operational AI applications: Combining transactional data with vector retrieval for data-driven decision-making

FAQs

What is a vector database and how does it work?

A vector database stores and queries high-dimensional vectors representing complex data like text or images. It uses specialized indexing to enable fast similarity search across large embedding collections.

What are the most popular vector database platforms available today?

Options range from MongoDB Atlas Vector Search and cloud-native services on AWS, Azure, and GCP to integrated offerings like vector search on the Databricks Platform.

Which cloud providers offer managed vector database services?

AWS, Azure, and GCP each offer managed vector search options. The Databricks Platform runs across all three clouds, providing vector capabilities within its unified architecture.

Does Databricks support vector search and vector database capabilities?

Yes. The Databricks Platform includes AI search within its lakehouse and lakebase architecture, so developers can build applications where operational data, analytical context, and AI models already reside.

What are the key features to look for when choosing a vector database platform?

Prioritize scalable indexing, low-latency search, metadata filtering, governance controls, and tight integration with your existing data and AI infrastructure.

Which open-source vector database solutions are available for self-hosting?

Several open-source options exist for teams that prefer self-managed deployments. Evaluate them against managed alternatives based on operational overhead, scalability needs, and governance requirements.

How do vector databases integrate with large language models and AI applications?

Vector databases provide the retrieval layer for LLM-powered applications. They store embeddings that ground model responses in relevant enterprise data, a pattern known as RAG.

What types of workloads and use cases benefit most from vector databases?

RAG pipelines, semantic search, recommendation engines, and AI agents benefit most. Any workload requiring fast similarity matching over unstructured data is a strong fit.

Which platforms support hybrid search combining vector and keyword-based queries?

Most enterprise-grade platforms now support filtered search combining semantic vector queries with metadata or keyword filters. MongoDB, Google BigQuery, and the Databricks Platform each offer hybrid retrieval patterns.

How do you evaluate scalability and performance of a vector database platform?

Measure indexing speed, query latency at scale, and concurrent query throughput. Also assess whether the platform reduces total operational complexity by unifying vector search with your broader data infrastructure.

Build AI applications on a unified platform

For teams building AI agents, RAG applications, or data-driven APIs, the Databricks Platform with Databricks Apps and Lakebase provides a unified foundation where vector data, operational state, and AI models coexist, enabling faster development and simpler governance. Explore the Databricks Platform to get started.