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AI · head to head

Deepgram vs Apache Spark MLlib

Deepgram logo

Deepgram

AI

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

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

Scalable machine learning on Apache Spark

From
Free
Rated
-
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.; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
  • They diverge on capability: Deepgram covers Flux speech-to-text, Apache Spark MLlib covers Classification.

Where they differ

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

Attributes where Deepgram and Apache Spark MLlib differ
AttributeDeepgramApache Spark MLlib
Pricing modelusage-basedopen-source
Platformsweb, apiLinux, macOS, Windows
CategoryAIMachine Learning
FoundedUnknown1999

Identical on both: starting price (Free), 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 Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

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 Apache Spark MLlib
  • Transcribing recorded audio at scale via batch STTnot Apache Spark MLlib
  • Adding conversational text-to-speech to voice applicationsnot Apache Spark MLlib
  • Self-hosting speech models for data residency requirementsnot Apache Spark MLlib

Apache Spark MLlib

  • Machine learningnot Deepgram
  • Data sciencenot Deepgram
  • Distributed computingnot 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.

Apache Spark MLlib

  • Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.

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

Apache Spark MLlib

Free

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

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 Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Questions people ask

Is Deepgram or Apache Spark MLlib better?
Neither clearly leads. Deepgram starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Deepgram or Apache Spark MLlib?
Deepgram starts at Free and Apache Spark MLlib at Free.
Does Deepgram or Apache Spark MLlib run on more platforms?
Deepgram runs on web, api. Apache Spark MLlib runs on Linux, macOS, Windows.
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 Apache Spark MLlib is typically brought in for.
What can Deepgram do that Apache Spark MLlib cannot?
Deepgram covers Flux speech-to-text, Flux text-to-speech, Voice Agent API, Real-time and batch processing. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.

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
Apache Spark MLlib: How much does Apache Spark MLlib cost?

MLlib is completely free and open source, licensed under the Apache License Version 2.0. There are no subscription, licensing, or usage fees.

Source
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
Apache Spark MLlib: What licensing does MLlib use?

MLlib is licensed under Apache License Version 2.0, making it freely available for all users regardless of organization size or use case.

Source
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
Apache Spark MLlib: How do I use MLlib?

MLlib is built into Apache Spark. Download Spark, which includes MLlib as a module, and deploy on your choice of infrastructure including Hadoop, Mesos, Kubernetes, standalone, or cloud.

Source
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