We replaced 5 ML models with 1 shared encoder on an $11/month VPS

Show HN: We replaced 5 ML models with 1 shared encoder on an $11/month VPS

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We were running 5 fine-tuned MiniLM models for a resume-to-job matching pipeline — 455MB on an $11/month VPS, one per task. I consolidated them into a single shared encoder with 5 lightweight heads: 25MB total, same latency, zero API calls. The matching score went up, not down.The 5 tasks:1. Classify JD lines (requirement vs boilerplate) 2. Split requirements into required vs preferred 3. Disambiguate skill mentions (Python-the-language vs Python-the-ecosystem) 4. Textual entailment (does resume experience satisfy a requirement?) 5. Semantic embeddings for similarity searchAll five share MiniLM-L6 (22M params). Before: 5 x ~91MB fine-tuned copies — essentially the same encoder with different

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