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Atlas Cloud vs Apache Spark MLlib

Atlas Cloud logo

Atlas Cloud

AI

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

From
On request
Free plan
No
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Only Apache Spark MLlib 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.; Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • They diverge on capability: Atlas Cloud covers 500+ models, one API key, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 12 November 2026.

Where they differ

Only the attributes on which Atlas Cloud and Apache Spark MLlib actually diverge.

Attributes where Atlas Cloud and Apache Spark MLlib differ
AttributeAtlas CloudApache Spark MLlib
Starting priceOn requestFree
Pricing modelusage-basedopen-source
Free tierNoYes
Platformsweb, apiLinux, macOS, Windows
CategoryAIMachine Learning
FoundedUnknown1999

Identical on both: 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 Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

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 Apache Spark MLlib
  • Switching an existing OpenAI, Anthropic or Gemini SDK integration to other models by changing the base URLnot Apache Spark MLlib
  • Running hosted image and video models inside ComfyUI workflows without a local GPUnot Apache Spark MLlib
  • Comparing image and video models side by side in a playground before committing to onenot Apache Spark MLlib
  • Giving coding agents access to open-weight LLMs on a flat-rate plannot Apache Spark MLlib

Apache Spark MLlib

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Atlas Cloud
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Atlas Cloud
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Atlas Cloud
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot 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.

Apache Spark MLlib

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

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

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

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 Apache Spark MLlib if

  • You need dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
Apache Spark MLlib review

Questions people ask

Is Atlas Cloud or Apache Spark MLlib better?
Neither clearly leads. Atlas Cloud starts at On request and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Atlas Cloud or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at On request for Atlas Cloud and Free for Apache Spark MLlib.
Does Atlas Cloud or Apache Spark MLlib run on more platforms?
Atlas Cloud runs on web, api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Apache Spark MLlib for free?
Yes. Apache Spark MLlib 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 Apache Spark MLlib is typically brought in for.
What can Atlas Cloud do that Apache Spark MLlib cannot?
Atlas Cloud covers 500+ models, one API key, OpenAI-compatible LLM endpoint, Pay-as-you-go pricing, ComfyUI nodes. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

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
Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

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
Apache Spark MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

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
Apache Spark MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

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
Apache Spark MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

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
Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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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