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AI · head to head

Deepgram vs Amazon Redshift ML

Deepgram logo

Deepgram

AI

Voice AI API platform for speech-to-text, text-to-speech, and voice agents

From
Free
Free plan
Yes
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

  • Each has a real cost: Deepgram pricing is entirely usage-based, so total cost can be harder to predict than flat subscription tools.; 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: Deepgram covers Flux speech-to-text, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Deepgram and Amazon Redshift ML differ
AttributeDeepgramAmazon Redshift ML
Platformsweb, apiWeb
CategoryAIMachine Learning
FoundedUnknown2006

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 Deepgram

  • Flux speech-to-text
  • Flux text-to-speech
  • Voice Agent API
  • Real-time and batch processing
  • Self-hosted deployment
  • Audio intelligence

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.

Deepgram

  • Building real-time voice agents for customer supportnot Amazon Redshift ML
  • Transcribing recorded audio at scale via batch STTnot Amazon Redshift ML
  • Adding conversational text-to-speech to voice applicationsnot Amazon Redshift ML
  • Self-hosting speech models for data residency requirementsnot 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 Deepgram
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Deepgram
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Deepgram
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Deepgram

Where each one falls short

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

Deepgram

  • Pricing is entirely usage-based, so total cost can be harder to predict than flat subscription tools.
  • The Growth plan requires a minimum $4K/year commitment to unlock discounted rates.
  • Enterprise features and custom SLAs require a direct sales conversation rather than self-serve signup.
  • Some promotional per-minute rates are time-limited, meaning long-term pricing may differ from current promotional rates.

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

Deepgram

Free
  • Pay As You Go$undefined/mo
    • $200 free credit to start
    • No minimums or expiration
    • No credit card required to start
  • Growth$undefined/mo
    • Save up to 20% with annual pre-paid credits
    • Minimum $4K/year commitment
    • Credits applied against actual usage
  • Enterprise$undefined/mo
    • Custom pricing for large-scale deployments
    • Dedicated support and contracts

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 Deepgram if

  • You need flux speech-to-text.
  • You want to start without paying.
  • You work on web, api.
  • You also want flux text-to-speech.
Deepgram 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 Deepgram or Amazon Redshift ML better?
Neither clearly leads. Deepgram starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Deepgram or Amazon Redshift ML?
Deepgram starts at Free and Amazon Redshift ML at Free.
Does Deepgram or Amazon Redshift ML run on more platforms?
Deepgram runs on web, api. Amazon Redshift ML runs on Web.
Can I use Deepgram for free?
Both have a free tier, so you can try either at no cost before committing.
What is Deepgram best used for?
Deepgram is most often used for building real-time voice agents for customer support, transcribing recorded audio at scale via batch stt, adding conversational text-to-speech to voice applications, self-hosting speech models for data residency requirements. Of those, building real-time voice agents for customer support and transcribing recorded audio at scale via batch stt are not what Amazon Redshift ML is typically brought in for.
What can Deepgram do that Amazon Redshift ML cannot?
Deepgram covers Flux speech-to-text, Flux text-to-speech, Voice Agent API, Real-time and batch processing. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Deepgram: What does Deepgram cost?

Deepgram uses usage-based pricing starting with $200 in free credit, pay-as-you-go rates per minute or per character, a Growth plan with annual pre-paid credits requiring a $4K/year minimum, and custom Enterprise pricing.

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.

Deepgram: Is there a free plan, and what are its limits?

New users get $200 of free credit with no credit card required, which can be applied to speech-to-text, text-to-speech, or voice agent usage before any payment is needed.

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.

Deepgram: How is usage metered?

Usage is metered per minute of audio for speech-to-text and voice agent calls, and per 1,000 characters for text-to-speech, with add-ons like redaction and entity detection billed separately per minute.

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.

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.

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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