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Machine Learning · head to head

Amazon Redshift ML vs Together AI

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
Together AI logo

Together AI

AI

Open-source AI at scale

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Together AI free tier limits not clearly specified in pricing documentation
  • They diverge on capability: Amazon Redshift ML covers CREATE MODEL in SQL, Together AI covers Open-source models.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Amazon Redshift ML and Together AI actually diverge.

Attributes where Amazon Redshift ML and Together AI differ
AttributeAmazon Redshift MLTogether AI
PlatformsWebApi, Cloud
CategoryMachine LearningAI
Founded20062022

Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), 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 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

Only in Together AI

  • Open-source models
  • Fine-tuning
  • Fast inference
  • Embeddings
  • REST API
  • Python SDK
  • OpenAI compatible
  • Api support

What people use each for

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

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 Together AI
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Together AI
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Together AI
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Together AI

Together AI

  • LLM inference for production AI applicationsnot Amazon Redshift ML
  • Content generation at scalenot Amazon Redshift ML
  • Code execution and embeddingsnot Amazon Redshift ML
  • Model fine-tuning and trainingnot Amazon Redshift ML
  • Startup and enterprise AI deploymentnot Amazon Redshift ML

Where each one falls short

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

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.

Together AI

  • Free tier limits not clearly specified in pricing documentation
  • Pricing varies significantly by model and use case
  • Requires account setup for production access
  • Batch API discounts apply only to non-urgent workloads

Pricing, plan by plan

Amazon Redshift ML

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

Together AI

Free
  • Serverless Inference$0.03/1M input tokens
    • Chat and Vision models
    • Image generation
    • Video generation
  • Provisioned Throughput$21600/month
    • Up to 83% savings vs commercial alternatives
    • Reserved capacity
    • Guaranteed throughput
  • Dedicated Inference$5.49/hour
    • H100 GPU instance
    • Single-tenant deployment
    • No resource sharing
  • GPU Clusters$3.99/GPU-hour
    • On-demand capacity
    • Volume discounts available
    • Reserved options with up to 35% savings

Which should you pick?

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

Choose Together AI if

  • You need open-source models.
  • You want to start without paying.
  • You work on Api, Cloud.
  • You also want fine-tuning.
Together AI review

Questions people ask

Is Amazon Redshift ML or Together AI better?
Neither clearly leads. Amazon Redshift ML starts at Free and Together AI at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon Redshift ML or Together AI?
Amazon Redshift ML starts at Free and Together AI at Free.
Does Amazon Redshift ML or Together AI run on more platforms?
Amazon Redshift ML runs on Web. Together AI runs on Api, Cloud.
Can I use Amazon Redshift ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Redshift ML best used for?
Amazon Redshift ML is most often used for adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model, letting an analytics team test whether a predictive column has any business value before asking for data science headcount, scoring rows inside a sql pipeline where moving data out to a separate service would add fragility for little benefit, organisations committed to aws whose main constraint is a data science backlog rather than modelling sophistication. Of those, adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model and letting an analytics team test whether a predictive column has any business value before asking for data science headcount are not what Together AI is typically brought in for.
What can Amazon Redshift ML do that Together AI cannot?
Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. Together AI covers Open-source models, Fine-tuning, Fast inference, Embeddings.

Answered from the vendors’ own pages

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.

Together AI: Does Together AI offer a free tier?

Yes, Together AI advertises 'Start for free, scale on demand,' but specific free tier usage limits are not detailed on the pricing page.

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.

Together AI: What are Together AI's highest model prices?

Serverless inference pricing ranges from free for base models up to $4.40 per 1M input tokens for premium models. Video generation costs $0.14 to $3.20 per video depending on resolution.

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.

Together AI: How much can I save with Provisioned Throughput?

Together AI offers up to 83% savings compared to commercial alternatives when using their Provisioned Throughput option with reserved capacity.

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.

Together AI: What is Together AI's fine-tuning pricing?

Standard fine-tuning costs $0.48 to $2.90 per 1M tokens depending on model size, with a minimum charge of $4.00 per job.

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.

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