Exploring the AI Database Landscape
As AI continues to advance, the databases that support these technologies must evolve just as rapidly. After decades of evolution, the days of the "one size fits all" databases are over, as the impressively long list of 400+ databases here would suggest.
Instead, we see a diverse array of databases emerging, each optimized for specific AI workflows—whether it's rapid retrieval, managing complex multidimensional data, or scaling to massive datasets. Below, we’ll explore the current landscape of AI databases, categorizing them to match the distinct demands of AI applications. For full disclosure, I'm the CEO of Activeloop, and the creator of one such database for AI, called Deep Lake, also mentioned in this report. However, we've done our best to be as unbiased as possible while providing the market overview.
Over the course of this market guide, we will cover a range of databases optimized (and not so much) for AI workloads, including search-optimized systems, traditional databases with AI extensions, vector databases, graph databases, cloud data warehouses, AI-native data lakes (like Deep Lake), all designed to meet the diverse data management needs of modern AI applications.
Search Databases: Lexical Retrieval
Search databases play a crucial role in AI, especially when the task is to retrieve specific information quickly from many datasets. They're especially useful if searching for specific alphanumerical keywords, that naive vector search will inevitably confuse.
A few notable mentions in lexical retrieval domain include:
- Elasticsearch: Elasticsearch is a formidable search engine, widely used for indexing and querying text. In AI, it becomes indispensable for complex queries, including those involving vectors.
- Solr: Similar to Elasticsearch, Solr is built on Lucene and excels in full-text search and real-time indexing, making it well-suited for AI applications that require rapid search capabilities.
Traditional Databases
Traditional databases, encompassing both SQL and NoSQL, have long been the backbone of data storage. Initially, relational databases like Oracle dominated the landscape, offering structured data storage in rows and columns.
As data became more diverse with Web 2.0 and big data, NoSQL databases emerged to handle unstructured data and scale horizontally. While traditional databases use in-memory storage for fast queries, this approach is costly for large AI datasets due to their size.
As such, this category can be seen adapting to meet the new challenges posed by AI.
- PostgreSQL with PGVector: PostgreSQL, known for its robustness, now extends its capabilities with PGVector, allowing it to manage vector embeddings natively. This makes it a strong contender for AI workloads that require a blend of traditional relational data handling and vector operations. Also includes Postgres-based services such as Neon, Supabase, TimeScaleDB, or Lantern.
- MongoDB: MongoDB’s flexible document model has made it a staple in applications needing scalable, schema-less data structures. It’s increasingly favored in AI environments, particularly for handling large-scale, semi-structured data.
- Redis: Once a simple key-value store, Redis has evolved to support complex data structures, including vectors. Its speed makes it ideal for real-time AI applications where performance is non-negotiable.
- Rockset: Rockset, acquired by OpenAI, specializes in real-time analytics, optimized for serving low-latency queries on large datasets—a critical requirement in AI scenarios where swift data processing and retrieval are paramount.
Vector Databases: A Step Towards a Database for AI
Vector databases are purpose-built to handle high-dimensional vector data, essential for AI applications like recommendation engines, natural language processing, and Retrieval-Augmented Generation. There's a myriad of players in this domain that are further summarized here, but here's the few top players:
- Pinecone: Pinecone delivers a fully managed vector database service, optimized for high performance and scalability, crucial for applications needing fast, accurate similarity searches.
- Weaviate: Weaviate is another open-source vector database, seamlessly integrating with AI models. It enables semantic search and supports a variety of machine learning frameworks using GraphQL API.
- Milvus: Milvus is an open-source vector database designed for large-scale, unstructured data. Its support for multiple indexing algorithms makes it adaptable to various AI workloads.
- Qdrant: Qdrant is a vector similarity search engine that offers high scalability and performance, particularly useful in AI contexts that involve large sets of vector embeddings.
- Chroma: Chroma simplifies integration with machine learning workflows, providing a straightforward approach to vector storage and retrieval.
An important note here is that vector databases are different from vector libraries such as Faiss from Facebook. These can be good for obtaining MVPs (minimum viable products), especially when knowledge is static or slow-to-update, but not suited for production. Vector databases benefitted greatly from the AI hype cycle - attracting considerable usage. However, they currently lack true multi-modality (i.e., ability to store more than light metadata and vectors, or, in some cases, multiple vectors within the same collection/database).
Graph Databases: Mapping Relationships
Graph databases bring a powerful dimension to AI, allowing the modeling of real-world complexities where entities are deeply interconnected. Graph search helps to reduce the scope of the vector search positively impacting the overall relevancy, as seen in Linkedin’s Customer Service use case.
- Neo4j: Neo4j is the most widely known graph database, optimized for storing and querying highly interconnected data. It excels in scenarios where relationships are first-class citizens and can be critical in AI for understanding the connections and patterns within data.
- Amazon Neptune: Neptune, Amazon’s managed graph database service, supports both property graph and RDF graph models, offering flexibility for AI applications that require different types of relationship modeling and querying.
Data Warehouses: Evolving from Big Data
Data warehouses have been the cornerstone for large-scale data storage and analytics. With AI's rise, these platforms are integrating vector support to remain relevant, while battling it out in the structured data space, whether it's with public performance benchmarks face-offs.
- Snowflake: cloud data warehouse is expanding its capabilities to include vector data, allowing for seamless management and querying of large-scale AI datasets.
- Databricks: renowned for its Lakehouse, is incorporating vector capabilities, making it a comprehensive platform for end-to-end AI workflows—from data processing to model deployment.
- BigQuery: Google’s BigQuery now supports vector search and operations, enabling complex AI queries directly within the data warehouse. Its tight integration with other Google Cloud services and its capacity to handle massive datasets make it a robust choice for scaling AI efforts.
Data Lakes: Balancing Scale and Flexibility
Data lakes have been the cold storage layer for processing vast amounts of structured and unstructured data. Traditional analytical data lakes include Hudi (by OneHouse), Delta Lake (by Databricks), Iceberg (By Tabular reportedly acquired by Databricks for $2B). They are useful for cost-efficient storage that can scale to petabytes, for mid-second latency use cases - such as analysts querying all patents or analyzing youtube videos.
- Deep Lake: Deep Lake by Activeloop addresses these challenges by enabling enterprises to structure their multi-modal data in a unified AI-friendly format, and search across their multi-modal data with AI. It automatically connects and optimizes diverse data sources for efficient search without costly in-memory storage. Deep Lake offers accurate natural language querying through semantic and lexical indexing, optimized embeddings, and end-to-end learnable index for domain-specific understanding. Deep Lake helps enterprises deploy customized RAG systems that retrieve knowledge accurately, integrate with top LLM frameworks, and continuously improve with AI (Disclosure: This is the company I founded).
Conclusion: Navigating the AI Database Ecosystem
The database landscape is undergoing a profound transformation to meet the demands of the AI-enabled world. We're seeing a shift towards more holistic, AI-native solutions capable of handling the complexity and scale of modern AI workloads. While the array of options can be overwhelming, their modular nature enables selecting the right tools as your needs evolve. The success of your AI initiatives will increasingly depend on your chosen data store, defining not only storage costs but also the potential applications of your data that will compound over time. If you want your internal knowledge to be a true competitive advantage, actively contributing to development processes from training to deployment, it may be time to re-evaluate your database strategy.
