Fine-Tuning Platform

Fine-Tune LLMs Without
the Infrastructure

Upload an Axolotl config and your dataset. We handle GPU provisioning, distributed training, and model delivery — with real-time visibility and transparent costs.

Sound familiar?

Fine-tuning is hard enough without the ops tax

GPU setup eats your day

Provisioning instances, installing CUDA, configuring distributed training — all before you've written a line of fine-tuning code.

Costs spiral without visibility

Runaway training jobs rack up cloud charges before you notice. There's no native way to estimate cost upfront.

DevOps before ML

Most teams spend more time on infrastructure than on the models themselves. That's the wrong problem to be solving.

The Process

How It Works

From config upload to trained model in four steps — no cloud account, no DevOps, no surprises.

01

Upload your config

Drag and drop your Axolotl YAML and optional dataset. We validate the config and show you a cost estimate before you commit.

02

We provision the GPUs

The right instance is auto-selected for your job and running within minutes. No AWS account, no CUDA setup, no DevOps.

03

Watch training live

Streaming logs, loss and learning rate charts, GPU utilization, tokens per second — all in one real-time dashboard.

04

Download your model

When training completes you get a secure download link. The instance terminates automatically; you pay only for what you used.

Config Builder

Not sure how to write an Axolotl config?

Use the interactive config builder to generate a valid Axolotl YAML from a form — no YAML knowledge required. Pick your base model, method, and hyperparameters, then download or paste the result.

Open config builder →

Pricing

Pay Only for GPU Time Used

Billed per minute with no seat fees and no minimum spend. You see the exact estimated cost before you submit a job.

LoRA / QLoRA

g5.xlarge · 24 GB GPU

Best for 7B models

$3 – $8typical job
  • LoRA & QLoRA fine-tuning
  • Real-time dashboard
  • Model retained 7 days

Full Fine-Tune

g5.2xlarge · 24 GB GPU

Best for 13B+ models

$15 – $40typical job
  • Full fine-tune support
  • Real-time dashboard
  • Model retained 7 days

Exact cost is calculated from your config and shown before any job is submitted. No surprises.

Data handling

Your data stays yours

Encrypted end-to-end

Datasets are encrypted in transit (TLS) and at rest (AES-256) in isolated S3 buckets. Your data never touches a shared filesystem.

Ephemeral instances

Each job runs in a dedicated GPU instance provisioned only for that run. The instance self-terminates on completion — no shared compute.

Auto-deleted after 7 days

Datasets and output models are automatically removed from S3 after 7 days. Download your model when training finishes and we handle the rest.

Never used for training

Your data is used solely to run your job. We never use customer datasets to train or improve any other model.

Scope

What Heulistic Is (and Isn't)

Built for a specific job. Here's exactly what fits, and what doesn't.

Supported

  • LoRA, QLoRA, and full fine-tuning
  • Axolotl YAML configs
  • 7B – 70B parameter base models
  • Hugging Face model hub checkpoints
  • Custom JSONL datasets (up to 5 GB)
  • Real-time logs, loss curves, and GPU metrics
  • Secure model download after training

Not supported

  • Inference hosting or model serving
  • Dataset labeling or curation
  • Frameworks other than Axolotl (e.g. LLaMA-Factory, Unsloth)
  • Multi-node distributed training (coming later)
  • Models larger than ~70B (single-GPU limit)
  • Reinforcement learning fine-tuning (RLHF / PPO)
  • Custom training scripts or arbitrary Python code

Ready to start?

Run your first fine-tune today

Create an account, upload your Axolotl config, and your model will be training in under five minutes.

Consulting

Need Custom MLOps Help?

When self-service isn't enough, our team works directly with you to build, migrate, and maintain production ML systems on any cloud.

01

MLOps Infrastructure

Design and build scalable ML infrastructure — cloud architecture, container orchestration, and infrastructure as code.

  • Cloud architecture design
  • Container orchestration
  • Auto-scaling configuration
  • Infrastructure as code
02

CI/CD Pipelines

Automate your ML workflow from data ingestion to model deployment with robust testing and validation.

  • Automated testing
  • Model validation
  • Deployment automation
  • Rollback strategies
03

Model Monitoring

Comprehensive observability for production models — performance tracking, drift detection, and custom alerting.

  • Real-time monitoring
  • Data drift detection
  • Performance tracking
  • Custom alerting
04

Performance Optimization

Optimize models for speed and efficiency so they can handle production load without latency spikes.

  • Model compression
  • Latency optimization
  • Resource efficiency
  • Load testing

Get in touch

Let's Talk

Questions about the platform, or interested in consulting? Drop us a message and we'll get back to you within 24 hours.

Email: business@heulistic.com