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Business Intelligence · head to head

Periscope Data vs Amazon Redshift ML

Periscope Data logo

Periscope Data

Business Intelligence

SQL and Python analytics platform

From
$1000/month
Rated
-
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
Rated
-
Full reviewWebsite

The short version

  • Only Amazon Redshift ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: Periscope Data the periscopedata.com domain now redirects to sisense.com, so Periscope Data is no longer sold as a standalone product; 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: Periscope Data covers SQL Editor, 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 Periscope Data and Amazon Redshift ML actually diverge.

Attributes where Periscope Data and Amazon Redshift ML differ
AttributePeriscope DataAmazon Redshift ML
Starting price$1000/monthFree
Pricing modelsubscriptionusage-based
Free tierNoYes
PlatformsWeb, CloudWeb
CategoryBusiness IntelligenceMachine Learning
Founded20122006

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 Periscope Data

  • SQL Editor
  • Python/R Integration
  • Version Control
  • Caching
  • Dashboards
  • Redshift
  • BigQuery
  • Snowflake

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.

Periscope Data

  • SQL-based analytics and dashboards over a data warehousenot Amazon Redshift ML
  • Python and R analysis alongside SQL in one workflownot Amazon Redshift ML
  • Shared dashboards for data teamsnot 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 Periscope Data
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Periscope Data
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Periscope Data
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Periscope Data

Where each one falls short

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

Periscope Data

  • The periscopedata.com domain now redirects to sisense.com, so Periscope Data is no longer sold as a standalone product
  • No Periscope Data pricing, plan or seat rate remains published at the original domain
  • Buyers must now purchase through Sisense, whose own pricing is not published as a rate card

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

Periscope Data

$1000/month
  • Team$1000/month
    • SQL Analytics
    • Python/R
    • Dashboards
  • EnterpriseFree
    • Advanced Features
    • Custom Integrations
    • Premium Support

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 Periscope Data if

  • You need sql editor.
  • You work on Web, Cloud.
  • You also want python/r integration.
Periscope Data 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 Periscope Data or Amazon Redshift ML better?
Neither clearly leads. Periscope Data starts at $1000/month and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Periscope Data or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at $1000/month for Periscope Data and Free for Amazon Redshift ML.
Does Periscope Data or Amazon Redshift ML run on more platforms?
Periscope Data runs on Web, Cloud. Amazon Redshift ML runs on Web.
Can I use Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Periscope Data starts at $1000/month.
What is Periscope Data best used for?
Periscope Data is most often used for sql-based analytics and dashboards over a data warehouse, python and r analysis alongside sql in one workflow, shared dashboards for data teams. Of those, sql-based analytics and dashboards over a data warehouse and python and r analysis alongside sql in one workflow are not what Amazon Redshift ML is typically brought in for.
What can Periscope Data do that Amazon Redshift ML cannot?
Periscope Data covers SQL Editor, Python/R Integration, Version Control, Caching. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Periscope Data: Does Sisense offer a free trial?

Yes. Sisense offers a free trial of their Self-Serve plan. You can try features like data warehouse connectivity, built-in AI for natural-language queries, auto-narratives, and customer assistant features.

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.

Periscope Data: What is the difference between Sisense Self-Serve and Enterprise plans?

The Self-Serve plan is for startups and teams and includes basic connectivity and AI features with iframe embedding. The Enterprise plan is for regulated industries and includes multi-tenant architecture, HIPAA readiness, column-level security, single sign-on, on-premise deployment options, 99.99% SLA, and 30-day backups.

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.

Periscope Data: How do I access the Sisense Enterprise plan?

The Enterprise plan requires contacting Sisense for a demo and custom quote. There is no self-service signup for this tier.

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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