Deepgram Performance Tuning

Prerequisites

• A baseline for latency, throughput, quality, error/throttle rate, and a named service owner.
• Licensed non-sensitive fixtures, approved load window, and a rollback threshold.

Examples

Measure the development/staging transcription baseline with short fixtures, change one concurrency, streaming, or model parameter, and compare aggregate quality/latency/error results. Keep the change only within the signed threshold; revert on regression and never use customer recordings as performance fixtures.

Overview

Optimize Deepgram transcription performance through audio preprocessing with ffmpeg, model selection for speed vs accuracy, streaming for large files, parallel processing, result caching, and connection reuse. Targets: <2s latency for short files, 100+ files/minute batch throughput.

Performance Levers