New Splunk Agent Observability capability helps organizations track AI token usage, costs and productivity in real time.
Dubai, United Arab Emirates, October, 2026 – Cisco is introducing a dedicated Tokenomics capability within Splunk Agent Observability, helping organizations track and attribute AI token expenditure in real time as they look to optimize AI at scale.
AI Tokenomics focuses on understanding and optimizing token consumption while connecting AI spend to business outcomes. The new capability brings usage, cost and productivity insights together to support more data-driven budgeting and optimization decisions.
“Understanding how AI resources are being used and what they are costing is becoming increasingly important as organizations scale their AI investments,” said Ahmed El Saadi, Vice President MEA & TRC at Splunk, a Cisco Company. “Tokenomics gives teams the insights they need to better understand consumption, manage spend and connect AI investments to business outcomes.”
Helping organizations better understand AI consumption
Key capabilities include:
- Track AI token expenditure in real time: Organizations can track and attribute token spend while gaining insight into coding agent usage, cost, adoption and productivity at both team and user level.
- Consolidate AI consumption across tools and providers: Splunk provides one place to view the cost of employee usage of Claude Code, Codex and other AI coding tools, while consolidating consumption across providers into a single view.
- Forecast future AI spend: Using the Cisco Deep Time Series Model, the capability will forecast where spend is heading before the billing period ends, helping organizations anticipate costs and support budgeting and optimization decisions.
These insights can also help organizations identify opportunities to route workloads to the most cost-effective model.
The new Tokenomics capability is offered in Cisco Cloud Control and in Splunk Agent Observability on-premises and in Splunk Observability Cloud. It forms part of Splunk Agent Observability’s broader capabilities to evaluate agent behavior, monitor performance across the AI stack and optimize token usage and cost.
