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Apache Kafka vs Amazon Redshift ML

Apache Kafka logo

Apache Kafka

Databases

Open-source distributed event streaming platform

From
Free
Free plan
Yes
Visit Apache KafkaFull review
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
Visit Amazon Redshift MLFull review

The short version

  • Apache Kafka is free; Amazon Redshift ML starts at $0.25/mo.
  • Only Apache Kafka lists durable commit log; only Amazon Redshift ML lists CREATE MODEL in SQL.
  • Where Apache Kafka falls short: overkill for straightforward job queues, where a simpler broker is easier to run and reason about.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Kafka and Amazon Redshift ML differ
AttributeApache KafkaAmazon Redshift ML
Pricing modelOpen source, no licence fee; managed services billed separatelyUsage-based
PlatformsLinux, Windows, macOS, Self-hostedWeb
CategoryDatabasesMachine Learning

Identical on both: starting price (Free), free tier (Yes).

Not yet recorded: founded for Apache Kafka.

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 Apache Kafka

  • Durable commit log
  • Horizontal scale
  • Kafka Connect
  • Kafka Streams
  • Replication
  • Low latency

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.

Apache Kafka

  • Moving events between services without point-to-point couplingnot Amazon Redshift ML
  • Feeding analytics and warehouses from operational systems in near real timenot Amazon Redshift ML
  • Replaying history to rebuild state after a consumer bugnot Amazon Redshift ML
  • Buffering bursty producers ahead of slower downstream systemsnot 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 Apache Kafka
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Apache Kafka
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Apache Kafka
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Apache Kafka

Where each one falls short

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

Apache Kafka

  • Operationally heavy to self-host: brokers, storage, rebalancing and upgrades are a standing job, which is why managed Kafka is a large market
  • Overkill for straightforward job queues, where a simpler broker is easier to run and reason about
  • Ordering guarantees hold per partition, not per topic, and getting partitioning wrong is a common and expensive design mistake
  • The ecosystem is fragmented across the Apache project and vendor distributions, so documentation and tooling advice often assume a particular distribution

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

Apache Kafka

Free
  • Apache KafkaFree
    • Full platform
    • Kafka Connect
    • Kafka Streams

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 Apache Kafka if

  • You need durable commit log.
  • You want to start without paying.
  • You work on Linux, Windows, macOS, Self-hosted.
  • You also want horizontal scale.
Apache Kafka 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 Apache Kafka or Amazon Redshift ML better?
Neither clearly leads. Apache Kafka 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, Apache Kafka or Amazon Redshift ML?
Apache Kafka starts at Free and Amazon Redshift ML at Free.
Does Apache Kafka or Amazon Redshift ML run on more platforms?
Apache Kafka runs on Linux, Windows, macOS, Self-hosted. Amazon Redshift ML runs on Web.
Can I use Apache Kafka for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Kafka best used for?
Apache Kafka is most often used for moving events between services without point-to-point coupling, feeding analytics and warehouses from operational systems in near real time, replaying history to rebuild state after a consumer bug, buffering bursty producers ahead of slower downstream systems. Of those, moving events between services without point-to-point coupling and feeding analytics and warehouses from operational systems in near real time are not what Amazon Redshift ML is typically brought in for.
What can Apache Kafka do that Amazon Redshift ML cannot?
Apache Kafka covers Durable commit log, Horizontal scale, Kafka Connect, Kafka Streams. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Apache Kafka: Is Apache Kafka free?

Yes. Kafka is open source under the Apache License v2 with no licence fee. Costs come from the infrastructure you run it on, or from a managed service such as Confluent Cloud.

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.

Apache Kafka: How is Kafka different from a message queue?

A queue usually removes a message once it is consumed. Kafka keeps an ordered, durable log, so consumers track their own position and history can be replayed — which is what makes rebuilding state after a bug possible.

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.

Apache Kafka: Who uses Kafka?

The project reports use by more than 80% of the Fortune 100, with over 5 million lifetime downloads.

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.

Apache Kafka: Do I need to run Kafka myself?

No. Self-hosting is the operationally expensive option; managed services such as Confluent Cloud run the brokers for you and bill on throughput and storage instead.

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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