The token economy: How to balance cost and quality in the era of AI

As organizations scale their use of AI, understanding the economics behind agents, copilots, and large language models becomes critical.

Microsoft’s AI ecosystem now offers a growing portfolio of agents, copilots, foundation models, and consumption-based services—each with its own pricing model, capabilities, and trade-offs.

In this session, we’ll demystify the token economy and explore why Microsoft uses different consumption and licensing models across Azure AI, Microsoft 365 Copilot, Copilot Studio, and autonomous agents.

You’ll learn how model selection impacts both quality and cost, which models are best suited for different workloads, and how to benchmark AI solutions to measure value rather than just consumption.

We’ll also walk through practical approaches to calculating token costs, estimating operational expenses, and optimizing AI architectures for both performance and budget.

Whether you’re building copilots, deploying agents, or scaling enterprise AI solutions, this session will provide the tools and frameworks needed to make informed decisions.

Topics covered:
- The growing Microsoft agent ecosystem and its consumption models
- Why Microsoft offers multiple pricing and calculation approaches
- Model selection: matching capability, latency, and cost to the task
- Benchmarking AI quality against operational cost
- Calculating token consumption across Azure AI, Microsoft 365 Copilot, Copilot Studio, and agents
- Real-world optimisation strategies
- Live Q&A

Leave with a practical framework for balancing AI quality, performance, and cost in an increasingly agent-driven world