One operating layer for AI token spend.
Modern teams use multiple model providers. OggyCloud gives them one place to route, monitor, and govern API-based LLM usage.
What teams use it for
Centralize providers
Route OpenAI-compatible providers through a consistent internal gateway.
Control budgets
Set key-level limits and revoke access without rotating provider secrets.
Compare models
Track cost, tokens, latency, errors, and usage trends across providers.
Start with a focused LLM cost review.
Use OggyCloud to connect the provider, review evidence-backed spend signals, and decide whether the savings workflow is valuable before expanding coverage.
Single-provider AI app
Track OpenAI-compatible traffic by key and model
Multi-provider AI platform
Compare OpenAI, Gemini, Perplexity, and other model costs
Evaluation and agent control
Budget scheduled jobs, retries, and long-context workflows
How it works
Save provider credentials
Create managed keys for teams
Route API traffic through OggyCloud
Review token and cost trends
Common questions
Can this work with custom endpoints?
Yes. Custom OpenAI-compatible endpoints can be configured with a base URL.
Is this a replacement for model providers?
No. It is an observability and governance layer above provider APIs.
How OggyCloud compares for llm token management.
Provider dashboards
Each provider shows its own slice of usage.
Raw gateway proxy
Can forward requests but may not provide cost intelligence.
Finance-only reporting
Shows the bill after the behavior happened.
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.