Enterprise AI scalability is running into a hard budget wall for some customers. As agentic workloads multiply, cloud token bills are consuming allocations faster than planned, prompting companies to rethink where inference should run. That cost pressure is reshaping enterprise AI deployment strategy, with on-premises infrastructure and open-weight models moving back into the conversation alongside […] The post Dell and Intel weigh the costs of enterprise AI scalability appeared first on SiliconANGLE.
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How OpenSmartRoute helps
The router treats agents, tools and skills as targets in the same catalogue as models. An MCP tool or an A2A agent is one entry; the router picks it when it fits the request and learns from how it performs. The same guard and resource limits apply to a tool's arguments as to a prompt.
For a team that routes through the router, a release like this is one catalogue entry. The new model competes on the next request against what the team already runs, on quality, cost and speed. If it answers well it earns more traffic; if it fails it gets less, with no code change in the app.