Home / Directory / Agencies & professional services / Fine-tuning & custom models / Lamini
Lamini
An enterprise platform to run, fine-tune, and train models on the customer's data, including private or air-gapped environments, on AMD or NVIDIA GPUs.
Software teams that want to customize open models on proprietary data and keep training inside their own environment.
Buyers that want a closed frontier-model API only, or a labeling tool with no training stack.
Co-founded by Sharon Zhou.
Verdict
Lamini is a good fit for teams that need to run, fine-tune, and train models on their own data, including in a private or air-gapped environment. Pricing is sales-led and is toward specialized fine-tuning platforms rather than a cheap hosted API. Buyers with a hard private-training requirement find it useful; if a standard hosted API already meets quality, you may not need it.
Score Breakdown
How Lamini scores in the categories that matter to its buyers.
The Field at a Glance
Where Lamini ranks among Agencies & professional services vendors we reviewed, by Overall Score and relative typical engagement cost.
Lamini is strongest for software teams that want to customize open models on proprietary data and keep training inside their own environment. It lands mid-pack among the broader agencies peers we plotted, at 6.2. Relative typical engagement cost is mid-pack on this chart.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Fine-tune open models on private data | Strong | The product job. |
| Air-gapped or private GPU environments | Strong | A published deployment claim. |
| Closed frontier-model API | Weak | A different buy. |
| Labeling without training | Weak | A different data tool. |
Review Excerpts
Below are excerpts from public reviews. Paid reviews and pay-for-play sites such as Clutch were excluded.
“We needed fine-tunes that never left our VPC. Lamini got us there without inventing a whole ML platform team first.”
“Using it to adapt open models on support transcripts we can't send to a public API. The workflow is still fiddly but it's ours.”
“Public proof points are thin. We had to run our own bakeoff because case studies didn't answer the questions our security team asked.”
“If you just want a frontier API with good defaults, skip this. It's for teams that insist on training where the data already lives.”
Company Facts
- Founded
- 2022 (CB Insights)
- Headquarters
- Menlo Park, California, United States (CB Insights)
- Key investors
- First Round Capital, AMD Ventures, Dylan Field, Aglae Ventures, Anthony Schiller (CB Insights)
- Website
- lamini.ai
Key Leadership
Methodology
This page is an independent evaluation of Lamini for buyers comparing options in fine-tuning & custom models. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Lamini did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for fine-tuning & custom models)
- Fine-tune on customer data
- Private and air-gapped fit
- AMD and NVIDIA GPU support
- Public evidence
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites such as Clutch are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the fit pattern described above.
Scope
Lamini is graded here as fine-tuning & custom models. Criteria scores can move as more review volume and product checks are added.