Future-Proofing Data Storage for Generative AI

Introduction

Storing data in a way that ensures future usability and compatibility with generative AI tools is a question many organizations we’ve talked to have been thinking about.

Poor database organization and infrastructure can significantly handicap the utility, performance, and scalability of Generative AI tools, making this an important decision, especially for companies that might be looking to leverage AI tools in the GXP space where significant architecture overhauls can be prohibitively resource-intensive.

So, for companies looking to build their own Generative AI storage capabilities in-house or for companies evaluating what might make sense in the longer term, how can they make sure that they have a database that will support the varied use cases you might want for Generative AI without yet knowing exactly what those use cases might be?

The short answer is that you can’t - you won’t know exactly what use cases you’ll need or exactly how database providers will expand their offerings in the future.

But you can know what types of information are stored and how they need to be accessed in order to be used by AI. And, even if you don’t know the exact use cases yet, you do know what general functionality is required.

For many IT and engineering teams, this consideration manifests as a common dilemma: should we adopt standalone vector store systems or hybrid vector store systems?

Here are some high-level overviews, pros, and cons of some of the the two database types, according to our own tests and the experiences of our life sciences customers.

Hybrid Vector Stores

These are databases like MongoDB, Azure AI Search, AWS Cognitive Search, and others.

These databases are hybrid because they store vectors (the data format used for many AI-based systems that require specific contextual information) alongside structured application data in an otherwise standard structured database form.

• Pros: This can allow for powerful querying capabilities and more robust support for real-time data processing. This provides the functionality of a traditional structured application database and the flexibility of AI-based databases altogether. This is especially helpful when you are continually updating data and may not always be searching primarily by vectors.

• Cons: Setup can be more tedious than standalone vector stores, and performance can be impacted if not properly optimized. Many of the closed source versions of these databases can also become very expensive at scale (Azure AI Search and AWS Cognitive Search are two examples of this) and lose accuracy when it comes to retrieving the most relevant vectors.

Standalone Vector Stores

These are databases like Pinecone, Chroma, PG Vector, FAISS, and others

These are considered standalone databases because they primarily store and access information as vectors.

• Pros: Standalone databases like this are, naturally, solely optimized for vector similarity search and retrieval, making it incredibly efficient and accurate at identifying the most semantically similar pieces of information (vectors), even across a very large number of vectors. It’s fairly simple to set up scalable architectures, especially with new serverless offerings from providers like Pinecone (although this is also more expensive).

• Cons: Users are limited to searching primarily by vectors. Metadata tags can help filter vectors, but standalone vector stores typically do not support additional database types required to create a robust AI application with changing data.

Wrapping up

In conclusion, both hybrid vector stores and standalone vector stores come with their own advantages and shortcomings. The choice between the two primarily depends on the specific use cases and the nature of the data that companies are considering. Thinking carefully about these questions can help companies determine what data store works best.

The options are also not mutually exclusive. Many organizations may find that a combination of these two may be best - in these cases, what type of data store is used where is, unsurprisingly, dictated by the use case.

Working through these questions can help an organization ensure that the database system they invest in is intentionally chosen with the right considerations in mind.

If you’re curious to talk more about the right vector stores for your organization, just reach out to info@artosai.com.