Inspiration
As AI applications become increasingly integrated into production systems, one challenge consistently stands out: visibility.
Developers often know how much they're spending only after receiving their API invoices. They have limited insight into which models, endpoints, or users are driving costs, how latency changes over time, or where optimization opportunities exist.
TokenWatcher was created to solve this problem by providing a unified observability platform for AI applications. Instead of manually combining logs, invoices, and dashboards, developers can monitor their entire AI infrastructure from a single place.
What it does
TokenWatcher is a production-ready AI observability platform that helps developers monitor, analyze, and optimize AI-powered applications.
The platform provides:
- Real-time token usage and cost monitoring
- Request, latency, and error analytics
- Model and endpoint performance dashboards
- AI spending forecasts
- AI-powered optimization recommendations
- Multi-workspace management
- Production telemetry SDK
- Telegram integration for remote monitoring
- Live dashboards powered by Server-Sent Events
TokenWatcher enables engineering teams to understand how their AI systems behave before small inefficiencies become expensive problems.
How we built it
TokenWatcher was built as a modern full-stack application using:
Frontend
- React
- TypeScript
- Vite
- Tailwind CSS
- shadcn/ui
- React Query
Backend
- Node.js
- Express
- PostgreSQL
- Server-Sent Events
SDK
- Lightweight TypeScript telemetry SDK with batching, retries, queue management, and graceful shutdown.
AI & Integrations
- AI-powered recommendations
- Forecasting engine
- OpenClaw integration
- Telegram Bot API
The platform follows a modular architecture where telemetry collected by the SDK flows into a centralized analytics engine, powering dashboards, forecasts, reports, and conversational interactions.
Challenges we ran into
Building an observability platform required solving several engineering challenges.
Some of the biggest included:
- Designing a scalable telemetry ingestion pipeline
- Maintaining workspace isolation securely
- Building responsive real-time dashboards
- Handling live analytics efficiently
- Creating meaningful AI-powered insights from telemetry data
- Integrating Telegram workflows while keeping the system secure and reliable
Balancing scalability, usability, and developer experience required multiple iterations throughout development.
Accomplishments that we're proud of
We're proud that TokenWatcher evolved into a complete AI operations platform rather than just another analytics dashboard.
Some highlights include:
- Production-ready telemetry SDK
- Real-time AI observability dashboard
- Cost forecasting and reporting
- AI-powered optimization insights
- Multi-workspace architecture
- Telegram monitoring and notifications
- Secure API key management
- Comprehensive documentation and deployment guides
- Clean, developer-focused user experience
What we learned
Building TokenWatcher gave us a much deeper understanding of how production AI systems operate.
We learned how important observability is for AI infrastructure and gained valuable experience designing scalable backend services, telemetry pipelines, real-time analytics, SDKs, and developer tools.
Most importantly, we learned that successful AI products need visibility into costs, performance, and reliability—not just powerful models.
What's next for TokenWatcher – AI Cost & Observability Platform
Our vision is to make TokenWatcher the observability platform for modern AI applications.
Future plans include:
- Support for additional AI providers
- Advanced anomaly detection
- Team collaboration and role-based access
- Budget automation and governance policies
- Smarter forecasting models
- Expanded reporting and export capabilities
- More conversational AI workflows
- Enterprise deployment options
- Additional integrations with developer tooling and cloud platforms
Our long-term goal is to help developers build AI applications that are more reliable, cost-efficient, and easier to operate at scale.
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