The job each one does
Predibase optimizes the case where you want to ship many LoRAs behind one inference endpoint and have someone else operate the GPUs. kolm optimizes the case where the deliverable is the artifact - signed, portable, with the evaluator that gated it included in the file - so it can run inside the customer's network or on a phone with proof of what it does.
Both are reasonable answers to "I want a small task-specific model." The right answer depends on whether you want a hosted endpoint or a sealed artifact.
If you operate dozens of LoRAs as a multi-tenant SaaS and the right primitive for you is "one inference endpoint, many adapters," Predibase's LoRAX serving is the better answer. We do not try to compete on multi-LoRA serving infra. The right composition is to use Predibase as a training back-end and ship the LoRA inside a .kolm for the on-device or in-VPC tier.
Side-by-side
| Predibase | kolm | |
|---|---|---|
| What it is | Hosted LoRA fine-tune + multi-tenant serve | Compiler that produces a signed task artifact |
| Output | Inference endpoint on Predibase cloud | A signed .kolm file (≤3 GB) you own |
| Where it runs | Predibase cloud (or BYO inference for the LoRA) | Phone, laptop, server, edge - or any GGUF-compatible runtime |
| Quality gate | user-supplied evals | K-score on a held-out set, gated at 0.70 default, included in the artifact |
| Receipts / signing | no | HMAC-SHA256 receipt chain over every output |
| Multimodal recall | bring your own RAG | bundled - sqlite-vec index ships in the artifact |
| Multi-LoRA serve | LoRAX, dozens of adapters per host | single artifact per task; serve --mcp on top of it |
| Air-gap / private VPC | VPC deploy is enterprise-tier | artifact runs offline by construction |
| Pull-out cost | extracting the LoRA + a comparable runtime is project-sized | the artifact is a file. Mount it. Done. |
| License | Predibase commercial; LoRAX OSS | RS-1 spec MIT, SDK MIT, paid managed compile |
When to use Predibase
Use Predibase when the problem you have is "I want to fine-tune and operate many LoRAs as a hosted endpoint and not run GPUs."
# predibase: ship a LoRA, hit it via REST predibase fine-tune --model llama-3-8b --dataset s3://... ok deployed to https://serving.predibase.com/...
When to use kolm
Use kolm when the deliverable has to be a portable artifact - because it runs on a phone, in a customer's VPC, on an edge box, or in a regulated environment where the evaluator and signature matter.
# compile a task-specific artifact your customer can audit: kolm compile "redact PHI from clinical notes" \ --examples ./redaction-fixtures.jsonl \ --base qwen2.5-7b ok wrote phi-redact.kolm k_score=0.93 signature=hmac-sha256 kolm verify phi-redact.kolm kolm run phi-redact.kolm "<clinical-note>" --receipt
Can I use both?
Yes. The cleanest composition: use Predibase (or a comparable trainer) as a training back-end, then wrap the resulting LoRA inside a .kolm with the K-score evaluator and recall index baked in. Same LoRA, but the deliverable is now a signed file with proof of what it does. The Predibase endpoint can stay in place for cloud traffic; the .kolm handles the on-device, VPC, or air-gapped tier.
Verdict
If the deliverable is an endpoint on someone else's cloud, Predibase is the better answer. Operating LoRAs at multi-tenant scale is what they are best at.
If the deliverable is a file the customer keeps - signed, portable, evaluator-included - that is what kolm compiles. The compile step turns a LoRA into a release artifact instead of a checkpoint.
Adjacent comparisons: vs Ollama · vs fine-tuning · vs OpenPipe · full comparison table