Track OpenAI usage like infrastructure spend.
Shared provider keys make it hard to know which team, tool, or product feature created token spend. OggyCloud gives developers managed keys while centralizing usage visibility.
What teams use it for
Issue safer keys
Give teams OggyCloud-managed keys instead of raw provider credentials.
Attribute token spend
Track usage by model, project header, user, and managed key.
Inspect request logs
Enable prompt and response samples where policy allows it.
Start with a focused OpenAI cost review.
Use OggyCloud to connect the provider, review evidence-backed spend signals, and decide whether the savings workflow is valuable before expanding coverage.
AI feature launch
Track tokens by project, model, and managed key
Team-level attribution
Separate product, eval, support, and internal tool usage
Prompt cost review
Inspect token-heavy requests, latency, retries, and model choice
How it works
Store your OpenAI provider key
Create managed team keys
Point SDKs at the OggyCloud base URL
Review usage, logs, and cost
Common questions
Does this track ChatGPT Plus usage?
No. It tracks OpenAI API traffic that goes through your OggyCloud gateway.
Can prompt logging be disabled?
Yes. Prompt and response logging are opt-in per managed key.
How OggyCloud compares for openai usage tracking.
OpenAI dashboard
Useful for provider totals, but limited for internal team and workflow attribution.
Application logs
Can capture details but usually require custom pipelines and cost math.
Shared API keys
Fast to start but hide who created spend.
Want to shape the LLM management workflow?
Share what you need around API keys, prompt visibility, token budgets, and team-level usage controls.
Bring cost intelligence into one operating workflow.
Create a free workspace, connect one provider, and review cloud, SaaS, and AI usage signals together.