Vector databases are foundational to generative AI, which has enjoyed the shortest adoption curve of any technology in recent memory. As generative AI becomes increasingly mission-critical to business, businesses are seeking well-established database solutions that deliver on enterprise-level expectations in vector database and management of data.
The vector search that powers generative AI is like having a super-intelligent financial advisor with perfect memory of every market event, company report, and economic indicator ever recorded.
Imagine you're considering an investment strategy. A traditional search might pull up companies in a specific sector or with certain financial metrics. Vector search, however, goes much deeper:
It could analyze a company's quarterly reports, news articles, social media sentiment, and global economic indicators all at once. Then, it might reveal unexpected connections - perhaps linking a small tech startup's innovation to a potential disruption in the energy sector, which could affect your entire portfolio.
The potential benefits of generative AI are almost limitless. But today, we’re at the start of a transition to enterprises taking greater control of one of the most powerful modern technologies ever developed.
From the cloud to on prem
Reliability is a must for any enterprise system. Generative AI needs to have those same non-negotiable characteristics: security, availability, scalability, compliance, and consistent performance. Deploying vector databases for generative AI on premises is one way to ensure those qualities persist.
Today, most generative AI customers use cloud-based LLM (large language model) services hosted by such providers as OpenAI or Anthropic. But an increasing number of enterprises are experimenting with running open source LLMs such as Llama on prem - or so-called SLMs (small language models), which require fewer resources and are easier to fine tune.
The primary reason for this shift is data privacy and compliance. Some of the most valuable generative AI applications use sensitive data customers want to keep on prem – or must do so, for regulatory reasons. Also, although cloud generative AI services generally charge low rates, the costs of scaling up becomes substantial at a certain point. Consistently high-use applications, from customer-facing chatbots to coding assistants to generative image processing, are top candidates to reel in-house.
Implementing generative AI on prem is not a trivial endeavor. Generative AI expertise, such as the skills required to use frameworks for building AI applications, does not come cheap. Running an open source LLM or SLM on prem requires GPU-equipped servers – and storing generative AI data requires a vector database. To support mission-critical generative AI, on-premises vector database capability must be fully operationalized – to minimize downtime, keep data safe, and avoid excessive administrative overhead.
Navigating vector solutions
Vector database offerings can be divided into two groups, which Forrester Research refers to as “native” and “multi-modal” solutions. The native variety was built from the ground up for one job: to store, index, and search vector embeddings, which are numeric representations of data objects (from documents to images to video to audio to text) that include semantic context. The multi-modal variety has those same capabilities, yet integrates them into an existing database solution, such as an enterprise RDBMS.
One obvious advantage of choosing an on-premises, multi-modal solution is that in-house database engineers and administrators don’t need to become experts in a separate database offering. They can focus on developing vector database skills in a familiar context and rely on existing enterprise-grade tech support.
How fast a vector database can retrieve vector embeddings is important due to its impact on the user experience. As a user interacts with an AI application – entering a prompt that delivers a result that provokes the user to enter another prompt, etc. – vector database searches occur each step of the way. Wait times for results must be as brief as feasible.
The frequency of data updates also has an impact. Encoding vector embeddings is handled at the AI application layer using one of several models and is a substantial workload. A constant flow of new vector embeddings can be thought of as a flow of transactions, the handling of which is the core purpose of enterprise databases.
Enterprise reliability
A multi-modal vector database solution doesn’t demand customers spin up a net-new AI stack. Transactional, analytical, and AI workloads can coexist within the same environment, all of it supported by enterprise dependability that acknowledges the increasingly mission-critical nature of generative AI.
If possible, that means no downtime at all, planned or unplanned. High availability is assumed with proven enterprise database platforms.
Enterprise security looms just as large. Inadvertent leakage of sensitive data has already emerged as a problem for generative AI. If the database solution you choose includes such enterprise features as state-of-the-art authorization, fine-grained access control, and data encryption, you don’t have to try and layer those safeguards onto your vector engine.
Moreover, enterprise-grade support will provide invaluable assistance to those venturing into the brave new vector world. And a vibrant open source community around the vector database solution you choose fosters rapid innovation and quick problem resolution in a rapidly evolving space.
Generative AI is not an island. Data that fuels generative AI includes proprietary data used for other enterprise purposes, so stringent data protection, security, and life cycle policies must apply. Enterprise databases are purpose-built for that, which is why integrating vector capability into them makes more sense than a separate, net-new implementation.