Use cases
Retail and shops
Stores run on guesses: how much to order, when to promote, who will come back. A small model trained on your own sales turns those guesses into numbers you can act on — and it improves as sales come in.
Models businesses like yours build first
- Forecast a number
Forecast weekly demand per product
Order what will sell: forecasts per product and store, with holidays and weather added when they help.
Build this — free - Predict a column
Spot customers about to stop buying
From purchase history, know who is drifting away early enough to win them back.
Build this — free - Replace an LLM task
Tag product reviews automatically
Replace the LLM prompt that tags reviews by topic and sentiment with your own model: same tags, a fraction of the cost.
Build this — free - Sort photos
Check shelf or product photos
Sort photos into your own categories: missing items, wrong labels, damaged goods.
Build this — free
Why Datazimuts
- vs. prompting a frontier LLM
About 500× cheaper and 10× faster per prediction
For sorting texts or photos into your own categories, a small model trained on your examples is typically more accurate than a frontier LLM prompted without examples, and it never sends your data to a third party at prediction time.
Cost of 10,000 text classifications a month
- Frontier LLM$25.16
- Datazimuts$0.04
- vs. SageMaker Autopilot
No AWS setup, and $90/month saved on the always-on endpoint
No AWS account, IAM roles, S3 buckets or notebooks (about 16 hours of an expert's time). Training runs on the cheapest fitting option — Lambda, Fargate Spot or a Spot GPU — and a model costs nothing while nobody calls it.
First-year cost for one model
- SageMaker$3,179
- Datazimuts$0.51
- vs. hiring a consultant
$25,000 saved in the first year
A first model in minutes instead of about 3 weeks, with the leakage checks, baselines, monitoring and retraining a careful ML engineer would set up.
First-year cost for one model
- Consultant$27,000
- Datazimuts$0.00
Free during launch: every feature, no card.
How we compute this
Frontier LLM: the average of gpt-5.5, claude-opus-4-8, claude-sonnet-4-6, gemini-2.5-pro at $3.56 per million input tokens and $20.00 per million output tokens (LiteLLM model price map 1.102.0); one classification reads about 650 tokens (instructions, categories, the text) and writes about 10, so $2.52 per 1,000, against < $0.01 per 1,000 for a small model served on AWS Lambda. Typical response: 1,500 ms for the LLM, 80 ms here. Accuracy, from published research:
- ChatGPT: Jack of all trades, master of none (Kocoń et al., Information Fusion, 2023) — Across 25 NLP tasks, ChatGPT scored about 25% below task-specific fine-tuned models.
- Fine-tuned 'small' LLMs (still) significantly outperform zero-shot generative AI models in text classification (Bucher & Martini, 2024) — Smaller fine-tuned models beat zero-shot GPT-class models on every classification task tested.
- Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021) — A classifier trained on the image features beats zero-shot prompting on most of 27 image datasets.
SageMaker: a real-time endpoint on ml.m5.large runs 24/7 ($98 a month); an Autopilot job costs $0.92–$9.22 on ml.m5.2xlarge; setup takes about 16 hours of an ML-literate engineer ($2,000). AWS SageMaker and Lambda list prices, us-east-1, retrieved 2026-09-21.
Consultant (our planning assumption): 15 days to deliver one model at $1,000 a day, then one day a month of upkeep ($1,000).
Ratios are rounded down to a round number, never up. Prices retrieved 2026-09-21.