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Atlas Cloud vs Amazon Redshift ML

Atlas Cloud logo

Atlas Cloud

AI

One API for 500+ image, video, LLM and audio models

From
On request
Free plan
No
Full reviewWebsite
Amazon Redshift ML logo

Amazon Redshift ML

Machine Learning

SQL statements in Redshift that train models on SageMaker and return them as functions

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Only Amazon Redshift ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: Atlas Cloud not every model accepts every request format. Each model publishes its own supported_apis list, and Gemini models expose their full multimodal capability only on the native Gemini format, so 'change the base URL' does not hold for every model.; Amazon Redshift ML training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
  • They diverge on capability: Atlas Cloud covers 500+ models, one API key, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 12 November 2026.

Where they differ

Only the attributes on which Atlas Cloud and Amazon Redshift ML actually diverge.

Attributes where Atlas Cloud and Amazon Redshift ML differ
AttributeAtlas CloudAmazon Redshift ML
Starting priceOn requestFree
Free tierNoYes
Platformsweb, apiWeb
CategoryAIMachine Learning
FoundedUnknown2006

Identical on both: pricing model (usage-based), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Atlas Cloud

  • 500+ models, one API key
  • OpenAI-compatible LLM endpoint
  • Pay-as-you-go pricing
  • ComfyUI nodes
  • MCP server and CLI
  • Playground and Generator
  • Streaming, batch and webhooks
  • Coding Plan

Only in Amazon Redshift ML

  • CREATE MODEL in SQL
  • Automatic model selection
  • Local inference
  • Bring your own model
  • Algorithm selection
  • Cost ceiling controls
  • Existing warehouse security
  • Batch and interactive scoring

What people use each for

The jobs each tool is most often brought in to do.

Atlas Cloud

  • Adding AI image or video generation to an app without an account at each model providernot Amazon Redshift ML
  • Switching an existing OpenAI, Anthropic or Gemini SDK integration to other models by changing the base URLnot Amazon Redshift ML
  • Running hosted image and video models inside ComfyUI workflows without a local GPUnot Amazon Redshift ML
  • Comparing image and video models side by side in a playground before committing to onenot Amazon Redshift ML
  • Giving coding agents access to open-weight LLMs on a flat-rate plannot Amazon Redshift ML

Amazon Redshift ML

  • Adding a churn or propensity score to an existing dashboard where the data is already in Redshift and nobody needs a bespoke modelnot Atlas Cloud
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Atlas Cloud
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Atlas Cloud
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Atlas Cloud

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Atlas Cloud

  • Not every model accepts every request format. Each model publishes its own supported_apis list, and Gemini models expose their full multimodal capability only on the native Gemini format, so 'change the base URL' does not hold for every model.
  • No Anthropic models were listed when checked in November 2026: the provider directory showed Anthropic with 0 models, so the Anthropic Messages format is a compatibility layer for other models rather than access to Claude.
  • The model count is stated inconsistently across the site: the homepage title says 400+, the vendor says 500+ and the pricing page listed 550, which makes it hard to know what is available without checking the live list.
  • Billing is prepaid. When the balance runs out new API requests are rejected with 402 Payment Required unless auto top-up is enabled, which matters for production workloads.
  • Most models are third-party models resold through the platform, so pricing and availability follow upstream providers and change often; prices on the pricing page are shown against a separate official price and discounts vary by model.

Amazon Redshift ML

  • Training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
  • Autopilot searches many candidate models by default and the duration and cost of CREATE MODEL scale with the data size and the MAX_CELLS setting, so an unconstrained statement against a large table is an expensive accident rather than an experiment.
  • Local inference runs on the Redshift cluster itself, so scoring millions of rows competes for the resources the warehouse exists to provide, and the remote inference alternative adds a per-batch network call plus an hourly SageMaker endpoint charge that persists whether or not anyone queries it.
  • The supported problem types are limited to what the exposed algorithms cover, so anything involving text, images, sequences, a custom loss function or a bespoke evaluation metric is out of scope and has to be built conventionally.
  • There is no retraining schedule, drift detection or model registry, so a model created by a statement stays exactly as trained until somebody remembers to recreate it, and nothing in the warehouse will report that its accuracy has decayed.

Pricing, plan by plan

Atlas Cloud

On request
  • Pay-as-you-go$undefined/usage
    • Billed per image, per second of video or per token; no subscription
    • GPT Image 2 text-to-image: $0.004 per image
    • Seedream 5.5 generation or edit: $0.02 per image
  • Coding Plan$undefined/month
    • Monthly subscription with a monthly total and a weekly limit
    • Or 90-day pay-as-you-go credit packs with no weekly cap
    • Open-weight LLMs including GLM, MiniMax, Kimi and DeepSeek
  • Enterprise$undefined/custom
    • Volume API discounts
    • SLA options through sales
    • Dedicated infrastructure and private hosting on request

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

Which should you pick?

Choose Atlas Cloud if

  • You need 500+ models, one api key.
  • You work on web, api.
  • You also want openai-compatible llm endpoint.
Atlas Cloud review

Choose Amazon Redshift ML if

  • You need create model in sql.
  • You want to start without paying.
  • You also want automatic model selection.
Amazon Redshift ML review

Questions people ask

Is Atlas Cloud or Amazon Redshift ML better?
Neither clearly leads. Atlas Cloud starts at On request and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Atlas Cloud or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at On request for Atlas Cloud and Free for Amazon Redshift ML.
Does Atlas Cloud or Amazon Redshift ML run on more platforms?
Atlas Cloud runs on web, api. Amazon Redshift ML runs on Web.
Can I use Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Atlas Cloud starts at On request.
What is Atlas Cloud best used for?
Atlas Cloud is most often used for adding ai image or video generation to an app without an account at each model provider, switching an existing openai, anthropic or gemini sdk integration to other models by changing the base url, running hosted image and video models inside comfyui workflows without a local gpu, comparing image and video models side by side in a playground before committing to one. Of those, adding ai image or video generation to an app without an account at each model provider and switching an existing openai, anthropic or gemini sdk integration to other models by changing the base url are not what Amazon Redshift ML is typically brought in for.
What can Atlas Cloud do that Amazon Redshift ML cannot?
Atlas Cloud covers 500+ models, one API key, OpenAI-compatible LLM endpoint, Pay-as-you-go pricing, ComfyUI nodes. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Atlas Cloud: How is Atlas Cloud priced?

Pay as you go, per image, per second of video or per token, drawn from a prepaid balance with no subscription required. Examples in November 2026 were $0.004 per image for GPT Image 2 text-to-image, $0.02 per image for Seedream 5.5 and from $0.11 per second for Kling 4.0. Enterprise volume discounts are available through sales.

Source
Amazon Redshift ML: Does it require SageMaker?

Yes. Redshift ML is an interface; the training happens in SageMaker and needs an IAM role and an S3 bucket for the intermediate data.

Atlas Cloud: Is the Atlas Cloud API OpenAI-compatible?

Yes. The base URL https://api.atlascloud.ai accepts OpenAI Chat Completions, Completions, Responses and Images requests, Anthropic Messages and Google Gemini requests with the same API key. Not every model supports every format; each model publishes a supported_apis list.

Source
Amazon Redshift ML: Is there an extra charge?

The SQL interface is part of Redshift, but the training runs as a SageMaker job charged at SageMaker rates, and a remote inference endpoint is billed for as long as it exists.

Atlas Cloud: Can I use Atlas Cloud in ComfyUI?

Yes. The Atlas Cloud ComfyUI nodes, installed from the AtlasCloudAI/atlascloud_comfyui GitHub repository, call hosted models from a ComfyUI graph. Generation runs on Atlas Cloud, so large video and image models work without a local GPU.

Source
Amazon Redshift ML: What kinds of model can it build?

Regression, binary and multiclass classification through the automatic path, plus direct use of XGBoost, linear learner, multilayer perceptron and K-means. Anything beyond structured tabular prediction is out of scope.

Atlas Cloud: What happens when my balance runs out?

New API requests are rejected with 402 Payment Required and you receive low-balance notifications. Service resumes as soon as you top up, and auto top-up can be enabled to avoid interruptions.

Source
Amazon Redshift ML: Can I use a model I trained myself?

Yes, through the bring-your-own-model path, either compiled into the cluster for local inference or called as a remote SageMaker endpoint.

Atlas Cloud: What is the Atlas Cloud Coding Plan?

A set of flat-rate subscriptions and 90-day credit packs for open-weight LLMs such as GLM, MiniMax, Kimi and DeepSeek, designed for coding agents including Claude Code, Codex and Cursor. Monthly subscriptions carry a weekly limit that resets every Monday.

Source
Amazon Redshift ML: Does it retrain automatically?

No. Retraining means running CREATE MODEL again, on a schedule you build yourself, and nothing in the product monitors whether it is needed.

Atlas Cloud: Where is Atlas Cloud based?

Its headquarters are at 1540 Broadway, New York, with offices in Silicon Valley and Calgary.

Source
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