For business leaders and developers alike, the question isn’t why generative artificial intelligence is being deployed across industries, but how—and how can we put it to work faster and with high performance?
Not all generative AI services are created equal. Generative AI models are tailored to handle different tasks. Most organizations need a variety of models to generate text, images, video, speech, and synthetic data. They often choose between two approaches to deploying models:2. Self-hosted solutions that rely on open-source and commercial tools.
Ensuring an optimal user experience with high throughput, low latency, and security is often difficult to achieve on existing self-hosted solutions, where high throughput denotes the ability to process large volumes of data efficiently and low latency refers to the minimal delay in data transmission and real-time interaction.
Easy-to-use inference microservices that run data through trained AI models connected to small independent software services with APIs can be a game-changer. They can provide instant access to a comprehensive range of generative AI models with industry-standard APIs, expanding into open-source and custom foundation models, that can seamlessly integrate with existing infrastructure and cloud services.