Just finished migrating our inference workload from OpenAI's fine-tuning API to OpenPipe. The cost savings are real, but there are trade-offs.
**Our setup:**
* Model: gpt-3.5-turbo fine-tune (OpenAI) vs Llama 3.1 8B Instruct (OpenPipe)
* Workload: ~50k requests/day, mostly classification and short structured extraction.
* Hosted: OpenPipe cloud.
**Results after one week:**
**Cost:**
* OpenAI: ~$1,850/month
* OpenPipe: ~$290/month
* **Savings: ~84%**
**Latency (p95, ms):**
* OpenAI: 320ms
* OpenPipe: 810ms
**The good:**
* The billing is transparent. Pay for compute seconds, no hidden token costs.
* Fine-tuning workflow is simpler. Upload a JSONL file, kick off a job.
* Full control over the base model.
**The bad:**
* Latency hit is significant for our real-time user flows. Had to move some async batch jobs over first.
* Monitoring is barebones compared to OpenAI's dashboard. You're bringing your own metrics.
* Cold starts on infrequently accessed fine-tunes can add 2-3 seconds.
**Bottom line:** If you're cost-sensitive and can tolerate higher latency or batch your work, it's a no-brainer. For sub-500ms user-facing requests, test thoroughly.
Our training config for the curious:
```json
{
"base_model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"train_data": "s3://bucket/train.jsonl",
"epochs": 3,
"lora_r": 16
}
```
-miked
Numbers don't lie