Compare

A first model in minutes, not 3 weeks — and about $27,000 less in the first year

A good consultant is worth it for hard, one-off problems. Most SME models are not that: they are churn, demand, lead scoring or ticket routing on your own data. Datazimuts does those end to end, with the same rigour, and leaves you in control.

Start free

Datazimuts vs. hiring an ML consultant
	ML consultant or freelancer	Datazimuts
Time to a first model	About 3 weeks (15 days of work)	Minutes to hours
Cost for one model	About 15 days at $1,000	Free during launch
Upkeep	A day or so a month, at $1,000	Monitoring and retraining built in
First year	About $27,000	Free during launch
Rigour	Depends on the person	Leakage checks, baselines and held-out tests on every model
When they leave	Knowledge leaves too	Everything recorded: data, decisions, audit trail

Questions

Is it as good as an expert?: On standard business problems, it follows the same method an expert would — and refuses to ship a model that does not beat a simple baseline.
Can a consultant still help?: Yes: many use Datazimuts to deliver faster. Under the hood, every setting is available.
What does it cost after launch?: Pricing comes later; everything is free during the launch, with fair-use limits only against abuse.
Where do these numbers come from?: Our planning assumption: 15 days to deliver one model at $1,000 a day, plus a day a month of upkeep.

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.