AI is moving quickly from experimentation to everyday use across state and local government. As teams expand from proofs of concept to operational deployments, the infrastructure decision becomes harder to ignore. A cloud first model can work well for many workloads, but it can also introduce latency, increase ongoing costs, and raise new questions about data handling and control.

This on demand webinar breaks down where local compute fits in an AI strategy built for public sector realities. You will hear practical guidance on how to evaluate which AI workloads belong in the cloud, which are better suited for local environments, and how a hybrid model can help agencies balance performance, governance, and cost predictability.

Watch this session to learn:

• Why AI workloads often behave differently than traditional applications in cloud environments

• When local compute can reduce latency and improve responsiveness for time sensitive workflows

• How to keep sensitive data closer to where it is generated and used without slowing innovation

• How to avoid cost surprises as AI usage grows across departments and use cases

• What to consider in AI ready endpoint, workstation, and infrastructure requirements

• A decision framework for choosing local, cloud, or hybrid compute by workload and risk profile

• How to scale AI access for more teams while maintaining consistent performance and oversight

Watch the on demand webinar to make more informed compute decisions as your AI initiatives expand.