Businesses of all sizes - from startups to large enterprises - are rapidly embracing AI to develop new applications or add AI-powered features to existing products - many of which are API-dependent. This post will explore three common key challenges emerging with managing AI products and practical strategies to overcome them.
Testing API performance is particularly challenging with AI-centric APIs for several reasons:
Robust analytics about latency, error rates, and usage patterns are crucial for optimizing performance as AI models evolve and products mature.
Because of these factors, real-time observability of production environment performance is more critical when AI is part of your product stack versus traditional APIs. Therefore, robust analytics about latency, error rates, and usage patterns are crucial for optimizing performance as AI models and products mature.
Engineering teams and development managers should start evaluating and integrating next-gen observability solutions into their product stack to address the needs of traditional APIs and AI data models while minimizing technical overhead. Several alternative approaches exist:
As AI applications advance product complexity, managing scalability is increasingly challenging. Therefore, product managers and dev teams need robust solutions that provide more insight into rapid fluctuations in API usage, ensure consistent performance, and support dynamic user needs. Lacking this expanded performance visibility is equivalent to driving a speeding car on a foggy road.
However, with real-time user experience and app performance data, product managers and infrastructure engineers can proactively monitor and address performance issues. While traditional API and app monitoring tools monitoring can help to some degree, legacy approaches need to catch up in many areas. The most common gaps not addressed by legacy monitoring solutions include:
If you’d like to dig in further on the items above, we recently explored each of these gaps in a separate blog post.
Anyone subscriber to premium services from OpenAI knows that leveraging third-party AI models or developing & operating your own is costly! Although we’re in a bull rush of investment into new AI applications, any investment into AI-dependent features or products must eventually generate a return.
The product & engineering managers we’ve spoken to have described significant challenges just developing and releasing sound AI applications amidst massive competition. Monetization to recapture their investment is often an afterthought. Unfortunately, just like observability, monetizing AI applications is more complex than traditional applications and, therefore, best addressed in parallel with product development. Several unique characteristics drive AI monetization complexity:
Are you curious how Revenium can improve observability and help monetize your AI products? Revenium co-founders Jason Cumberland and John D'Emic presented From Code to Cash: Using a Revenue Mesh to Monetize AI & ML APIs during Gravitee Edge 2023.
The video provides an overview of how Revenium can enable organizations to ease the integration of AI into traditional API products and shows a hands-on demo of the Monetization of an API-first proprietary LLM.
Btw, If you want to jump right to the product demo - start watching at 14:14
Enterprises and startups that embrace AI applications must address new development, product observability, and monetization challenges. Fortunately, solutions such as Revenium are ideal to help fill the gaps in current offerings without adding significant technical overhead.
If you’d like to try Revenium, create a free account (see the form below - no credit card required). Within minutes of connecting your APIs using our low-code agent, you’ll be on the way to simplifying your metering & monetization and exposing new insights about AI products & API performance.