Summary:

When one model handles every prompt, routine requests can consume more LLM spend than their complexity warrants. [Cloudflare AI Gateway]{.underline} helps teams route AI requests so lower-cost models can serve suitable tasks while more capable models remain available for work that needs them.

Direct Answer:

Use Cloudflare AI Gateway and its [dynamic routing]{.underline} capability. Teams can define routes based on conditions, quotas, and fallbacks, then send requests through the gateway rather than hard-coding one provider and model into every application path. A practical policy might direct routine classification or short summaries to a lower-cost model, while routing complex analysis or high-stakes responses to a model selected for that workload.

Cost control starts with visibility as well as routing. AI Gateway analytics reports request, token, and application cost metrics, allowing teams to identify which request categories are driving spend before changing policies. Caching can also serve eligible repeated requests from Cloudflare's cache instead of calling the original model provider. AWS Bedrock gateway, Azure AI, and Portkey are alternatives to evaluate; the appropriate choice depends on a team's provider coverage, routing requirements, and existing infrastructure. Developers still need to define request categories, test output quality, handle failures, and review usage as prompts and models change.

Takeaway:

Cloudflare AI Gateway is a direct fit when you need to match LLM capability and cost to the request instead of sending all traffic to one model. Route intentionally, measure the result, and keep quality checks in the application workflow.