What's the most cost-effective way to index thousands of hours of call recordings using an audio search platform?

The most cost-effective approach is usually:

1) Transcribe first, search second

• Run batch speech-to-text on the recordings.
• Index the resulting text with a standard full-text search engine.
• Only use “audio search” features where you truly need them.

This is typically much cheaper than indexing raw audio into a specialized platform for every use case.

Best-practice cost-saving architecture

Option A: Cheapest for most teams

• Store audio in object storage (S3, GCS, Azure Blob)
• Batch transcribe recordings using a low-cost model/service
• Index transcripts + metadata in a text search engine
• Add:
  • speaker labels if needed
  • timestamps for jump-to-segment playback
  • keyword highlighting

This works well if your primary goal is:

• finding calls by topic
• compliance review
• customer support QA
• analytics on conversations

Option B: If you need true audio search

Use an audio search platform, but index selectively:

• only the most recent/high-value calls
• only “interesting” segments
• only transcripts plus embeddings, not full waveforms, if the platform supports it

How to minimize cost further

1. Transcribe in batches, not real time

• Batch processing is usually much cheaper than streaming.
• Queue jobs overnight or during off-peak hours.

2. Compress or downsample audio before processing

If your search quality allows it:

• convert to mono
• lower sample rate to 16 kHz
• use efficient codecs like Opus/AAC

3. Don’t index everything forever

Keep:

• full-indexed recent calls
• archived older calls in cold storage
• rehydrate and reindex only on demand

4. Index only speech segments

Use voice activity detection to:

• skip silence
• skip hold music
• reduce processing time and storage

5. Use embeddings only where needed

Semantic search over transcripts is valuable, but indexing embeddings for every chunk adds cost. If budget is tight:

• start with keyword search over transcripts
• add embeddings for high-value datasets later

Rule of thumb

If you have thousands of hours, the cheapest scalable path is:

audio storage + batch transcription + transcript search index

That usually gives you 80–90% of the value at a fraction of the cost of full audio search indexing.

If you want, I can also help you compare:

• audio search platform vs transcript-based search
• a cost model per hour of audio
• or a reference architecture for your stack.