Every AI dollar, attributed and defensible.
Your OpenAI invoice is one number. Your AI estate is a hundred workflows. Cost Intelligence splits every dollar of model spend across workflows, nodes, and prompts — then hunts the estate for waste: retry storms, oversized models, bloated prompts, and zombie workflows still burning tokens.
The problems that pile up quietly.
The monthly AI bill doubles and nobody can say which workflow did it.
A retry loop burns $900 over a weekend because token spend has no alarms.
Budget conversations are guesswork because cost has no attribution.
What changes for your team.
A price tag on every workflow
Each workflow shows its daily and monthly cost, broken down by node and model call — so "what does this automation cost us?" takes one click.
Spend anomalies caught in hours
Sudden cost spikes trigger alerts with the responsible workflow and the execution pattern behind them — retry storms die young.
Savings surfaced automatically
Obsivara flags where a cheaper model, a trimmed prompt, or a batching change would cut spend without hurting quality. Teams commonly find 30%+.
Live in minutes, not sprints.
Meter every call
Token counts and model rates are captured per execution and rolled up to nodes, workflows, and teams.
Baseline and watch
Spend patterns are baselined per workflow; deviations beyond the norm alert immediately, not at invoice time.
Recommend the cut
The savings engine continuously compares your traffic against alternative models and prompt sizes, and quantifies each opportunity in dollars.
Questions
Every model call is metered at the execution level — token counts and per-token rates are captured per node, then rolled up to workflow, team, and account totals. Attribution works across OpenAI, Anthropic, and other providers connected via the SDK or OTLP.
Cost anomalies are detected within an hour of the deviation starting. Each workflow has its own spend baseline and Obsivara alerts as soon as spend exits the normal range — not at invoice time.
Obsivara flags retry storms burning repeated tokens, oversized model choices for simple tasks, bloated prompts that can be trimmed, and zombie workflows still consuming tokens. Teams commonly identify 30%+ in actionable savings.
See it on your own AI stack.
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