1. End-to-End Telemetry Flow
When an AI request starts in your Python application, Feenion creates lightweight span contexts with millisecond timestamps and token metadata. Traces are queued in a background worker thread and flushed in compressed batches over HTTP.
2. Storage Engine: SQLite WAL vs PostgreSQL
Feenion supports both zero-config embedded SQLite (with Write-Ahead Logging for high concurrency) and external PostgreSQL for enterprise deployments.
Runs inside a single Docker container with ~40MB RAM footprint. Perfect for local debugging, CI/CD test suites, and small teams.
High-throughput production setup supporting millions of traces per day with asynchronous Redis worker ingestion.
3. Fault Tolerance & Non-Blocking Guarantees
Feenion is designed to never crash your application. If the Feenion server becomes unavailable or experiences network blips, the Python SDK gracefully drops or buffers traces without throwing uncaught exceptions to user requests.