Understanding AI Workload Cost Considerations
When embarking on the journey of integrating artificial intelligence (AI) into your applications, one pivotal question often arises: What will this cost? Much like many intricate questions in pricing, the answer is a resounding “it depends.” Diving into the landscape of AI-enabled applications, various design choices significantly sway the cost—ranging from considerations in traditional architecture components to the selection of AI models.
Deconstructing AI Application Architecture
The initial step in building an AI application is recognizing that AI is a singular component within a larger architecture. The blueprint for robust architecture often begins with well-defined requirements, ensuring every piece of the application stack is appropriately sized to fulfill business needs. In mission-critical or customer-facing applications, anticipating additional costs from vital design components that enhance resilience, availability, and redundancy, such as geo-redundancy and load balancing, is important.
Examples of AI Applications
E-commerce Interfaces: Consider an application with a versatile front end serving both customers and administrators. It incorporates REST APIs, a RabbitMQ message queue, and MongoDB databases, complemented by console apps simulating traffic. Learn more about this architecture here.
Service-Based Architectures: Look at a .NET reference application implementing an e-commerce site using a service-based structure with .NET Aspire. Explore the details of this implementation.
Podcast Transcription Services: Examine an application designed to convert podcast audio files into text transcripts. Dive into the architectural specifics for processing audio files.
Navigating AI Service Pricing Structures
Understanding how AI services incur costs begins with recognizing that AI models predominantly employ a cost structure based on “tokens.” Tokens serve as the billing currency for AI models—excluding text-to-speech models that are billed by the number of characters processed. Fine-tuning models carry hourly rates from deployment, without the flexibility of suspending them such as virtual machines.
Deciphering the Tokenization Process
Tokens are pivotal in defining how an AI model interprets and navigates sequences of characters in both input and output. While humans perceive words, AI models interpret tokens, predicting the next token in the sequence. The evolution of models has yielded more efficient tokenization, such as interpreting “I’m” as a singular token instead of two separate entities. The consistency of tokens across languages, despite varying conversion rates per language, underscores their utility.
In a real-world application, estimating the number of tokens utilized and factoring in user interaction with AI—such as chatbots on a website—can provide clarity on potential costs.
Architecture, Usage, and Cost Insights
Applying the FinOps Framework, which entails architecting for the cloud and accurate planning and estimation, proves instrumental for all workloads, AI included. Adequately estimating the usage of AI workloads, particularly if new to your organization, might necessitate launching smaller pilots or proofs of concept to test assumptions about usage patterns.
An accurate grasp of billing nuances will guide cost-optimized design decisions, possibly involving strategies like optimizing or caching input/output tokens.
Addressing Carbon Emissions
Amid the conversation of costs, the environmental footprint of AI workloads should not be ignored. Whether already embedded in formal environmental, social, and governance reporting (ESG) or not, acknowledging the significant computing power AI services require is vital. The visibility of the carbon emissions associated with these services is now facilitated through the Azure Carbon Optimization tool, reinforcing sustainable practices.
Looking Ahead
We are committed to peeling back more layers in our understanding of AI costs. Our next exploration will delve into cost-control mechanisms—understanding what exists in our toolset to either cap our AI expenses or gain a granular insight into them.
For more insights into managing such AI-related financial elements, look into resources like the FinOps Framework, Microsoft Azure Pricing Calculator, and dedicated learning content addressing sustainability and environmental governance.
Join our next discussion as we explore cost controls for AI services and how to strategically manage financial commitments.
By Lars Svensson