Anyone else using Workato to build sophisticated enterprise automations that leverage various AI platforms? You might have recipes integrating OpenAI, Azure AI, Google Vertex AI, Cohere, or other specialized services to automate complex business processes.
Workato excels at connecting similar systems, but tracking the *consolidated AI cost* across all those different vendors and APIs used within your recipes can become a significant challenge, especially at enterprise scale. How much does that automated document processing recipe *really* cost per month, factoring in all the different AI calls it makes? Getting a clear, unified view often requires manual data wrangling across finance and IT.
This lack of centralized visibility into AI spend, particularly when orchestrated through powerful platforms like Workato, prompted me to build AIBillingDashboard.
So how it can help Workato users:
- Unified Enterprise AI Cost View: Aggregate spending data (via direct connections or log uploads) from the various AI services your Workato recipes utilize (OpenAI, Azure, Google Cloud, Cohere, etc.) into a single dashboard.
- Recipe Cost Analysis (via Logging): By implementing logging within your Workato recipes to capture AI service usage (e.g., API calls, tokens used) and exporting this data, you can upload it to the dashboard to analyze AI costs associated with specific recipes or business units.
- Provider & Model Comparison: Understand cost drivers across different AI providers and models used in your automations. Identify potential savings by optimizing AI service usage within recipes.
- Budget Management & Alerts: Track overall AI spend against departmental or project budgets and receive consolidated billing alerts for all connected AI platforms.
How are you currently managing the tracking and allocation of AI costs generated by your Workato recipes across different departments or projects? What are the main obstacles?
