16 model fine tuning apps in one list: the established products, and every new one as it launches.
Updated
7 apps launched in the last 12 months, by month.
The apps we added to this market most recently.
| # | App | Added |
|---|---|---|
| 1 | Together AI Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research. | |
| 2 | Replicate Run open-source machine learning models with a cloud API | |
| 3 | Mistral The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. | |
| 4 |  Soup CLI LoRA keeps the base model frozen: read, never written. So Soup keeps it in system RAM and streams it into the GPU one decoder layer at a time. Peak VRAM becomes one layer instead of the whole model. Measured on an RTX 3050 Laptop 4 GB: Llama-3.1-8B trains at 119.6 tok/s in 3.32 GB peak. One YAML, one command. SFT, DPO, GRPO, KTO, plus eval, gating and export. Apache-2.0. Every number is published, including the ones I measured and threw away. | |
| 5 |  Freesolo Freesolo helps enterprise teams turn generic model capability into AI features that belong in the product. We make reinforcement learning a commodity so any team can train a small, specialized model for their task. | |
| 6 |  Forgelet Define your task and get a fine-tuned model you can run anywhere | |
| 7 |  AlgoFly AI AlgoFly is an AI consulting company that helps businesses build and deploy AI solutions. | |
| 8 | Mindpathtech AI Model Fine Tuning Services | |
| 9 | Pioneer Fine-tune SLMs in minutes. Describe your task in plain English and our agent handles everything: data generation, training, evals, and deployment. Models deployed on Pioneer also keep improving automatically from live inference data. With Pioneer, anyone who can write a prompt can now build production-grade AI that gets smarter over time. | |
| 10 | fal Easiest & most cost-effective way to use Gen AI. fal.ai is how devs integrate dozens of generative media models. FLUX, King, Hailuo +600 more | |
| 11 |  TuneTrain.ai TuneTrain.ai - Fine-tune AI models with your data TuneTrain.ai lets anyone fine-tune small language models easily - no coding or huge datasets needed. Create example records, augment them into large datasets, and train your own custom AI. | |
| 12 |  FinetuneFast Finetune ML models in days, not weeks |
The products every new one is compared with.
| App | Launched | Monthly visits | Store ratings |
|---|---|---|---|
| * | 63,843 | 1,276 | |
| * | 37,031 | - | |
| * | 34,126 | - |
* Launch date estimated.
Grouped by what each app says it does. 8 of 16 apps fit a group so far.
| Type | Apps | Share | Examples |
|---|---|---|---|
| Model fine-tuning | 3 | 37.5% | FinetuneFast, Forgelet, TuneTrain.ai |
| AI infrastructure platforms | 2 | 25.0% | Mistral, Together AI |
| Model hosting and inference | 2 | 25.0% | fal, Replicate |
| AI consulting | 1 | 12.5% | AlgoFly AI |
What we could detect on their websites.
| Framework | Apps | Share |
|---|---|---|
| Next.js | 8 | 88.9% |
Of 9 apps whose framework we could detect.
| Host | Apps | Share |
|---|---|---|
| Vercel | 8 | 88.9% |
Of 9 apps whose host we could detect.
How many of the 16 apps show a price, and how many sell to large companies.
| Measure | Apps | Share of 16 |
|---|---|---|
| Publish a pricing page | 5 | 31.3% |
“model fine tuning” is searched 170 times a month on Google in the US. These are the searches around it.
| Search | Per month | Cost per click |
|---|---|---|
| fine-tuning meaning | 1,900 | - |
| fine-tuning vs rag | 260 | $1.32 |
| fine tuning or fine tuning | 210 | - |
| what is fine-tuning in ai | 170 | - |
| fine-tuning vs transfer learning | 70 | - |
| llm fine-tuning methods | 40 | - |
| llm fine-tuning vs rag | 40 | - |
| llm fine-tuning huggingface | 30 | $3.01 |
| what is fine-tuning in generative ai | 30 | - |
| what is fine-tuning in llm | 30 | - |
| fine-tuning deep learning | 20 | $13.06 |
| llm fine-tuning example | 20 | $4.86 |
| fine-tuning model example | 10 | $8.76 |
| llm fine-tuning book | 10 | - |
| llm fine-tuning code | 10 | - |
Cost per click is what advertisers bid for one visit from that search. Figures from Oct 2026.
Teams use fine tuning to make models follow their instructions more reliably and reflect brand or domain knowledge.
Customer support: Train on past tickets and knowledge articles so the assistant answers in your voice, cites internal policies, and escalates correctly.
Document workflows: Classify contracts by clause, extract fields from invoices, or summarize clinical notes with domain terminology.
Code and data tasks: Enforce a code style, generate boilerplate from templates, or translate SQL to the warehouse dialect used in your stack.
Creative generation: Match a brand tone for marketing copy, keep character consistency in stories, or adapt prompts for product imagery; platforms like fal help with media models.
Agents and assistants: Tune tool usage, function-calling formats, and step-by-step reasoning for internal copilots; Mistral highlights customization for assistants.
Open-source model operations: Start from a public checkpoint and adapt to your niche, then serve it via API on Replicate or train and host on Together AI.
ML engineers and data scientists use these platforms to run training jobs, compare experiments, and deploy APIs without managing GPU clusters.
Software teams integrate tuned models into apps through SDKs and REST endpoints, benefiting from versioning, monitoring, and rollback controls.
Product managers and domain experts curate examples and acceptance tests to shape tone, safety, and task coverage without touching low-level training code.
Operations and support teams improve deflection and response quality by grounding models in internal procedures and knowledge bases.
Compliance and security teams get org-wide controls, audit logs, and content filters to align outputs with policies.
Consultancies and system integrators, such as AlgoFly AI, help businesses select models, prepare data, and deliver tuned solutions.
Enterprises standardize on platforms like Together AI or Mistral for training and inference at scale, while startups often prototype and ship quickly using hosted APIs from Replicate.
Vendors mix pay-as-you-go for training and inference with subscriptions for platform features.
| Pricing model | How it works |
|---|---|
| Compute-based | Bills for training time, GPU hours, or tokens processed during fine tuning and evaluation. |
| Inference usage | Charges per request or generated tokens when calling the deployed tuned model. |
| Subscription tiers | A monthly plan unlocks features like higher limits, private networking, or advanced observability. |
| Per seat | Each user with access to projects, datasets, or deployments adds to the bill. |
| Project or job-based | Each fine tuning job or deployment counts as a paid unit with included resources. |
| Managed service | Custom pricing for white-glove setup, data prep, and ongoing support. |