AICosts.ai
Using lots of AI tools and don’t know where your money’s going? AICosts.ai gives you one simple dashboard to track, manage, and cut down on your AI costs....
What is AICosts.ai?
AICosts.ai is a cost tracking platform designed for organisations and individuals using multiple AI tools and services. It aggregates billing data from different AI platforms into a single dashboard, letting you see exactly how much you're spending across ChatGPT, Claude, API services, and other AI tools. The tool helps identify which tools consume the most budget, track spending trends over time, and find opportunities to reduce costs without sacrificing functionality. It's particularly useful for teams managing shared AI tool subscriptions or companies trying to understand their total AI spending across departments.
Key Features
Centralised dashboard
View spending from all your AI tools in one place
Multi-tool integration
Connect billing accounts from various AI platforms and services
Spending breakdown
See costs categorised by tool, time period, and usage type
Cost alerts
Get notified when spending exceeds set thresholds
Usage analytics
Track which tools deliver the most value relative to cost
Budget management
Set budgets and monitor actual spend against forecasts
Pros & Cons
Advantages
- Solves a real problem for teams using many AI tools; most people don't know their actual spending
- Freemium model means you can test it without commitment
- Centralised view saves time compared to checking individual platform bills
- Helps identify redundant or underused subscriptions
Limitations
- Usefulness depends on how many AI tools you use; limited value if you only use one platform
- Requires connecting billing accounts, which introduces a security consideration that users should evaluate
Use Cases
Teams evaluating whether their AI tool spending is justified
Organisations needing visibility into departmental AI costs for budgeting
Freelancers and agencies tracking client-related AI tool expenses
Companies consolidating AI tools and wanting data on which to cut
Finance teams preparing cost reports that include AI infrastructure spending