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SageMaker results without the AWS setup — and $98/month less per model

SageMaker Autopilot is powerful if you have an ML engineer and an AWS account ready. Datazimuts gives the same kind of result to people who do not: it runs on AWS for you, on the cheapest option, and a model costs nothing while nobody calls it.

Datazimuts vs. Amazon SageMaker Autopilot
SageMaker AutopilotDatazimuts
SetupAWS account, IAM roles, S3 buckets, a notebook — about 16 hours of an expertSign up, upload or connect your data
ServingA real-time endpoint billed every hour: about $98/monthPay per call on Lambda: nothing when idle
First year for one modelAbout $3,179Free during launch
ChecksUp to youLeakage, baselines, personal data, drift: built in and explained
Text and photosSeparate services and codeSame flow: label or import examples, train, go live
Use itYour own integration codeAPI, Sheets, Zapier, schedules, Claude and ChatGPT

Questions

Does Datazimuts run on AWS?
Yes: training runs on Lambda, Fargate Spot or Spot GPUs, whichever is cheapest for the job, and models are served from Lambda.
Can I export my model?
Yes: every model is a package you can download, with its metrics and schema.
What about big data?
Large tables train on Fargate Spot; you see the estimated cost before anything above a few cents runs.
Where do these numbers come from?
AWS list prices ($98/month for a 24/7 ml.m5.large endpoint), shown with their date in “How we compute this”.

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:

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.

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