Speech recognition pipelines are moving toward LLM-backed architectures for accuracy, but latency and cost become critical at scale. Long-form audio generates lengthy token sequences that inflate bills on token-based inference providers. Oxlo.ai offers a flat per-request pricing model and a fully OpenAI-compatible audio API that includes Whisper variants, making it a strong fit for teams building low-latency ASR systems.
The Latency and Cost Problem in Speech ASR
Traditional ASR services charge by the minute or by the token. When you layer an LLM for formatting, speaker diarization, or entity extraction, costs multiply because both the audio transcription and the downstream prompt are metered by length. Latency spikes come from cold starts on lesser-used audio models, and from sequential billing that discourages preprocessing audio into overlapping chunks for speed.
Why Request-Based Pricing Changes Audio Economics
Oxlo.ai charges one flat cost per API request regardless of prompt length or audio duration. For speech workloads, this is significant. A one-hour audio file might produce thousands of tokens. On token-based providers, that long input and output directly scale the invoice. On Oxlo.ai, the same transcription job costs one request. When you add a second request to clean the transcript with a reasoning model like DeepSeek R1 or Kimi K2.6, the price remains predictable. This predictability lets architects prioritize latency over token budgeting.
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