Generate embeddings for a dataset
Run offline batch embedding generation over a set of text records — for building a vector index, search, or RAG pipeline — in a single job with one receipt.
Estimated $0.0005–$0.003 per record · Scales with record count — typically 1,000–5,000 records/min
What inputs you need
- Text records (CSV/JSONL)
- Embedding model choice
- Output format (vectors + IDs)
What Badgr returns
- Vector embeddings keyed by record ID
- Batch run log
- Single receipt on completion
Recommended GPU routes
Estimated cost: $0.0005–$0.003 per record · Estimated runtime: Scales with record count — typically 1,000–5,000 records/min
Example command
badgr run "python batch_embed.py --input records.jsonl --model bge-large" \
--gpu L40S \
--max-cost 8 \
--max-runtime 45Common setup failures Badgr avoids
Dimension mismatch when swapping embedding models mid-project — output includes the model name and vector dimension in the manifest so downstream indexes catch mismatches early.
Throughput lower than expected — increase batch size to saturate GPU utilization, or move to a higher-tier GPU route.
Ready to run this?
Launch from the dashboard, CLI, or Compute API. Max-cost protection included.