10 Best Databases for Machine Learning & AI

Finding the right database for machine learning and AI projects has become one of the most important infrastructure decisions developers face. Traditional relational databases weren’t designed for the high-dimensional vector embeddings that power modern AI applications like semantic search, recommendation systems, and retrieval-augmented generation (RAG).

Vector databases have emerged as the solution, optimized for storing and querying the numerical representations that ML models produce. Whether you’re building a production RAG pipeline, a similarity search engine, or a recommendation system, choosing the right database can make or break your application’s performance.

We’ve evaluated the leading databases for ML and AI workloads based on performance, scalability, ease of use, and cost. Here are the 10 best options for 2025.

Comparison Table of Best Databases for Machine Learning & AI

1. Pinecone

Pinecone is a fully managed vector database built specifically for machine learning applications at scale. The platform handles billions of vectors with low latency, offering a serverless architecture that eliminates infrastructure management. Companies like Microsoft, Notion, and Shopify rely on Pinecone for production RAG and recommendation systems.

The database excels at hybrid search, combining sparse and dense embeddings for more accurate results. Single-stage filtering delivers fast, precise queries without post-processing delays. With SOC 2, GDPR, ISO 27001, and HIPAA certifications, Pinecone meets enterprise security requirements out of the box.

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2. Milvus

Milvus is the most popular open-source vector database with over 35,000 GitHub stars, designed for horizontal scaling across billions of vectors. Its cloud-native architecture separates storage, compute, and metadata layers, allowing independent scaling of each component. NVIDIA, IBM, and Salesforce use Milvus in production environments.

The platform supports multiple index types including HNSW, IVF, and DiskANN, plus hybrid search combining vector similarity with scalar filtering. Zilliz Cloud offers a managed version starting at $99/month, while the open-source edition runs free under Apache 2.0. Memory-efficient disk-based storage handles datasets larger than available RAM.

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3. Weaviate

Weaviate combines vector search with knowledge graph capabilities, enabling semantic relationships between data objects alongside similarity queries. The platform supports hybrid search out of the box, merging vector similarity, keyword matching, and metadata filters in single queries. Built-in vectorizers from OpenAI, Hugging Face, and Cohere generate embeddings automatically.

Multi-modal support handles text, images, and video within the same database. Weaviate performs 10-nearest-neighbor searches in single-digit milliseconds over millions of items. Vector quantization and compression reduce memory usage significantly while maintaining search accuracy, making it cost-efficient for large deployments.

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4. Qdrant

Qdrant is a high-performance vector search engine written in Rust, delivering consistently low latency without garbage collection overhead. The platform delivers 4x higher requests per second than many competitors while maintaining sub-millisecond query times. Discord, Johnson & Johnson, and Perplexity run Qdrant in production.

Payload-based filtering integrates directly into search operations rather than post-processing, supporting complex boolean conditions across multiple fields. Hybrid search combines dense vectors with sparse representations like TF-IDF or BM25 for semantic plus keyword matching. Both REST and gRPC APIs ship with official clients for Python, TypeScript, Go, Java, and Rust.

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5. ChromaDB

ChromaDB provides the fastest path from idea to working vector search prototype. The Python API mirrors NumPy’s simplicity, running embedded in applications with zero configuration and no network latency. The 2025 Rust rewrite delivered 4x faster writes and queries compared to the original Python implementation.

Built-in metadata filtering and full-text search eliminate the need for separate tools alongside vector similarity. ChromaDB integrates natively with LangChain and LlamaIndex for rapid AI application development. For datasets under 10 million vectors, performance differences from specialized databases become negligible, making it ideal for MVPs and learning.

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6. pgvector

pgvector transforms PostgreSQL into a vector database through a simple extension, enabling similarity search alongside traditional SQL queries in a single system. Version 0.8.0 delivers up to 9x faster query processing and 100x more relevant results. Instacart migrated from Elasticsearch to pgvector, achieving 80% cost savings and 6% fewer zero-result searches.

For 90% of AI workloads, pgvector eliminates the need for separate vector infrastructure. Vectors live alongside operational data, enabling single-query joins between embeddings and business records with guaranteed ACID consistency. Google Cloud, AWS, and Azure all offer managed PostgreSQL with pgvector support, and the extension runs free under the PostgreSQL license.

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7. MongoDB Atlas

MongoDB Atlas Vector Search adds similarity capabilities directly into the document database, storing embeddings alongside operational data without sync overhead. At 15.3 million vectors with 2048 dimensions, the platform maintains 90-95% accuracy with sub-50ms query latency. Atlas Search Nodes allow vector workloads to scale independently from transactional clusters.

The document model stores embeddings within the same records as metadata, eliminating data synchronization complexity. Scalar quantization reduces memory requirements by 75%, while binary quantization cuts them by 97%. Native aggregation pipelines combine vector search with complex transformations in unified queries, and enterprise security features come standard.

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8. Redis

Redis delivers sub-millisecond vector search latency that few databases can match, running up to 18x faster than alternatives in single-client benchmarks and 52x faster in multi-client scenarios. Redis 8.0 introduced native vector types, and the April 2025 vector sets feature optimizes real-time similarity queries with reduced memory usage.

The in-memory architecture combines caching, session management, and vector search in one system. Quantization provides 75% memory reduction while maintaining 99.99% accuracy. For datasets under 10 million vectors where latency matters most, Redis excels. The platform returned to open source under AGPL in 2024, with cloud pricing starting at just $5/month.

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9. Elasticsearch

Elasticsearch bridges semantic understanding with precise keyword matching, performing up to 12x faster than OpenSearch for vector search operations. The platform integrates with AI frameworks like LangChain and AutoGen for conversational AI patterns, and its built-in ELSER embedding model generates vectors without external services.

The query DSL composes vector search with structured filters and full-text search in ways most vector-first databases cannot easily replicate. Strict data consistency guarantees atomic updates across vector and keyword fields. Organizations running Elasticsearch for search can add AI capabilities without new infrastructure, leveraging existing operational expertise and achieving 10x data growth without architectural changes.

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10. Deep Lake

Deep Lake stores vectors alongside images, videos, audio, PDFs, and structured metadata in a unified multi-modal database built on data lake architecture. Intel, Bayer Radiology, and Yale University use Deep Lake for AI workloads requiring diverse data types. The platform offers sub-second latency while costing significantly less than alternatives through native object storage access.

Every dataset is versioned like Git, enabling rollbacks, branching, and change tracking across training iterations. Deep Lake 4.0 delivers 5x faster installation and 10x faster reads/writes through C++ optimization. Native integrations with LangChain, LlamaIndex, PyTorch, and TensorFlow simplify ML pipeline development. Data stays in your own cloud (S3, GCP, or Azure) with SOC 2 Type II compliance.

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Which Database Should You Choose?

For rapid prototyping and learning, ChromaDB or pgvector get you started fastest with minimal setup. If you’re already running PostgreSQL, pgvector adds vector capabilities without new infrastructure. Teams needing enterprise scale with managed operations should evaluate Pinecone for its serverless simplicity or Milvus for self-hosted control.

When sub-millisecond latency matters more than dataset size, Redis delivers unmatched speed for moderate-scale deployments. Organizations working with multi-modal data spanning images, video, and text should consider Deep Lake or Weaviate. For hybrid search combining vectors with full-text and structured queries, Elasticsearch and MongoDB Atlas leverage existing expertise while adding AI capabilities.

Frequently Asked Questions

What is a vector database and why do I need one for AI?

A vector database stores high-dimensional numerical representations (embeddings) generated by ML models and enables fast similarity search across them. Traditional databases can’t efficiently query these embeddings, making vector databases essential for RAG, semantic search, recommendation systems, and other AI applications that rely on finding similar items.

Can I use PostgreSQL instead of a dedicated vector database?

Yes, pgvector transforms PostgreSQL into a capable vector database suitable for 90% of AI workloads. It’s ideal when you need vectors alongside operational data in unified queries. For datasets exceeding 500 million vectors or requiring specialized features, dedicated vector databases may perform better.

Which vector database is best for production RAG applications?

Pinecone offers the smoothest path to production with managed infrastructure, while Milvus provides more control for self-hosted deployments. Both handle billion-scale vector collections with low latency. Weaviate excels when your RAG pipeline needs hybrid search combining semantic and keyword matching.

How much do vector databases cost?

Most vector databases offer free tiers sufficient for prototyping. Production costs vary by scale: Pinecone starts at $50/month, Weaviate at $45/month, and Redis at just $5/month. Open-source options like Milvus, Qdrant, ChromaDB, and pgvector run free if you self-host, though infrastructure costs apply.

What’s the difference between in-memory and disk-based vector databases?

In-memory databases like Redis deliver sub-millisecond latency but require expensive RAM for large datasets. Disk-based systems like Milvus and pgvector cost less per vector but trade some speed. Many databases now offer hybrid approaches with intelligent caching, balancing cost and performance based on access patterns.