AI Platforms: How to Choose

Building GenAI-powered applications involves gluing together a number of components. Much of this work is repetitive and common to most apps—i.e., undifferentiated heavy lifting. It also requires software engineers to tackle new AI-specific concepts that might take time to master. 

An AI platform can take on much of this lifting, shortening the time it takes to bring your generative AI apps from dream to deployment. In this post, we’ll look at what an AI platform does, the features to look for, and how to decide which one’s the best fit for your company.

What is an AI platform?

An AI platform is a unified technology stack for prototyping, developing, releasing, and maintaining GenAI-enabled applications.  It comprises several components, including:

• Large language models (LLMs)
• Data storage for structured, unstructured, or semi-structured data, stored as vectors, knowledge graphs, or other data types
• GenAI services, such as data ingestion, transformations, agents, and memory
• Orchestration between agents, prompts, tools, models, etc. 

Most AI applications will use a general-purpose or bespoke LLM to process and respond to user queries.  However, building an effective and high-quality GenAI app is more than making a call to an LLM. You'll need to use techniques like retrieval-augmented generation (RAG) to add additional context and improve response quality, as well as write additional code to ingest data, create user-facing agents, and manage request/response workflows.

Many of these patterns are universal—or, at least, highly repeatable across solutions. Using an AI platform could reduce or eliminate the work of eliminating some of these universal features. For example, an AI platform that supports RAG may provide support for importing data from multiple sources, converting it to vector embeddings, and performing a vector search.

Selecting an AI platform is different than selecting an LLM.  An AI platform provides a level of abstraction on top of an LLM. A good AI platform should support calling multiple LLMs, enabling you to easily replace one LLM with another and chain different LLMs together into complex workflows.

Benefits of an AI platform

Do you have to use an AI platform? Short answer: no.  Anything an AI platform does can be programmed directly by software engineers. 

However, using an AI platform instead confers multiple benefits:

• Reduces dev time -  Individual AI app engineers can be more productive using an AI platform since it eliminates repetitive coding of common features.
• Reduces ramp-up time -  An AI platform can also reduce the amount of time it takes a new AI app developer to get up to speed.  For example, by implementing logic for such tasks as generating vector embeddings, it enables developers to become productive using vector search technology without having to figure out how to implement it themselves.

Supports production readiness -  An AI platform can also make it easier to build in the robustness and reliability features you need to take a GenAI app from prototype to production.  This can include support for scalability, monitoring and observability,  and security, among other features.

Increases the pool of GenAI app developers -  One big benefit for companies that adopt AI platforms is that more developers can create GenAI apps,  since the framework reduces the knowledge required to stand up a new solution. This makes it easier to scale GenAI app development across an organization or company.

Features of an AI platform

So what features should you look for in an AI platform? While not an exhaustive list, the following should be considered essential:

• Data extraction and storage
• Pluggability 
• Validation
• Composability
• Orchestration

Data extraction and storage

GenAI solutions need data—and lots of it—to power high-quality responses. While an LLM excels at providing general language comprehension and generation capabilities, its knowledge is often too broad and too dated to provide accurate responses for most used cases.

An AI platform can excel here by providing advanced features for extracting data from structured, unstructured, and non-structured sources and storing it in a data store for later retrieval. This includes both storing and providing search capabilities in the format best suited for your use case. For example, some applications might benefit from using RAG techniques such as vector search, while others benefit more from a knowledge graph-based approach.

Pluggability

You'll likely need to integrate with different LLMs in any given solution because some will perform better on different tasks. You may also need to switch out an LLM mid-project if you discover another one that provides higher-quality responses for your use case.

AI platforms should offer an abstraction later for LLMs, handling model I/O to enable easily swapping out one LLM for another.

Validation

GenAI’s quality issues are well documented at this point.  According to McKinsey, nearly a quarter of survey respondents said their organization had suffered negative consequences due to GenAI inaccuracy.

In the absence of sufficient data, AI systems are prone to various forms of hallucination.  Biases in the underlying data can lead to inaccurate or even discriminatory answers.

Prior to releasing to production, GenAI apps should undergo rigorous testing to verify the quality of responses for a given problem domain. AI platforms can help by providing hooks and libraries to easily build in validation checks. 

Composability

GenAI apps have a lot of moving parts. They’re only bound to become more complex as the state of the art advances. Composability enables you to easily build a complex app out of base components, with the ability to rearrange and insert new components at will. 

Orchestration

Orchestration enables combining messages, tools, components, and other abstractions to create production-ready applications that incorporate features such as persistence, streaming, chat memory, and monitoring.

Considerations in choosing an AI platform

There are a number of AI platforms on the market already. When evaluating a platform, the four things that should be top of mind are:

• Ease of development
• Breadth of integrations
• Support for the entire application lifecycle
• Speed

Let's look at each one of these in detail.

Ease of development

A good AI platform makes developers more productive and reduces the ramp-up time required to produce production-quality code. The leading platforms support creating both no-code and low-code solutions. That is, they provide a visual builder to make the easy stuff easy while enabling you to switch to a code view to implement more complex logic.

You should also make sure a platform has support for all the GenAI technology you might need, including support for:

• All forms of data (structured, unstructured, semi-structured) 
• Vector search and knowledge graph traversal
• Multimodal data

Breadth of integrations

The more systems a platform integrates with, the more options you have at your disposal. Pay attention to how many LLMs a platform integrates with and which data stores it supports. 

Additionally, pay attention to which validation libraries, external data providers, and other tools a platform supports. Examples include: 

• Document processors for PDF, Excel, and other formats
• Web data retrieval services
• Custom model embedding 
• Validation and guard frameworks
• Entity resolution for entity-based knowledge graph systems
• Proprietary data sources
• Payment processing

Support for the entire application lifecycle

Prototyping a GenAI app is one thing. Getting it into production is another. An AI platform should provide a range of features to make this transition easier: 

• Hosting and scaling all associated services (e.g., vector databases, agents)
• Deployment pipelines
• Easy deployment to your cloud provider of choice
• Monitoring in production

A good benchmark here is how long it takes to turn a prototype into a releasable product. I.e., how much time do you save thanks to platform support for productizing apps? 

Speed

An AI platform offers convenience—but if it adds too much runtime overhead, the impact to application performance might not be worth it. Test a new platform before adoption to verify it’ll perform well under your expected user demand and data load.

How to build GenAI apps in five minutes

At DataStax, we’ve spent a lot of time thinking about how to make building GenAI apps easier. Our answer, the DataStax AI platform, leverages multiple components to enable quickly building GenAI apps that work at petabyte scale: 

• Astra DB for fast and infinitely scalable vector and knowledge graph data storage, complete with support for hybrid search and data streaming
• LangFlow for no-code and low-code approaches to app building
• Integration with all major LLMs and cloud providers

Using DataStax, you can build a wide range of applications using LangFlow in under an hour. For example, our own Patrick McFadin shows how to create a chat application with memory in just five minutes.  

Want to see how it works for yourself? Sign up for a free account and watch the tutorial to see how a full-featured AI platform can help you create an end-to-end GenAI solution before you leave work.