Softwr

Databases · head to head

Apache Pinot vs Sigma Computing

Apache Pinot logo

Apache Pinot

Databases

Real-time distributed OLAP datastore for analytics

From
Free
Rated
-
Full reviewWebsite
Sigma Computing logo

Sigma Computing

Spreadsheets

Spreadsheet-style analytics that queries a cloud data warehouse directly with no extract layer

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Apache Pinot has a free tier, so it costs nothing to try first.
  • Each has a real cost: Apache Pinot self-hosted and distributed, so running it means operating a cluster rather than consuming a service; Sigma Computing every user interaction is a live warehouse query, so compute costs rise with adoption and land on the warehouse invoice rather than the BI line item, which routinely surprises the team that approved the purchase.
  • They diverge on capability: Apache Pinot covers Real-time Analytics, Sigma Computing covers Spreadsheet interface over SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Pinot and Sigma Computing actually diverge.

Attributes where Apache Pinot and Sigma Computing differ
AttributeApache PinotSigma Computing
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsLinux, Docker, KubernetesWeb
CategoryDatabasesSpreadsheets
Founded1999Unknown

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 Apache Pinot

  • Real-time Analytics
  • Column-oriented
  • Distributed Processing
  • SQL Support
  • Pluggable Indexing
  • Star-tree Index
  • Upsert Support
  • Kafka

Only in Sigma Computing

  • Spreadsheet interface over SQL
  • No extract layer
  • Input tables and write-back
  • Workbooks
  • Embedded analytics
  • Version control and dbt awareness

What people use each for

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

Apache Pinot

  • Sub-second analytics queries on freshly ingested datanot Sigma Computing
  • User-facing dashboards inside a productnot Sigma Computing
  • Real-time metrics at high ingest ratesnot Sigma Computing
  • Petabyte-scale analytics as run at LinkedIn and Ubernot Sigma Computing

Sigma Computing

  • A finance or operations team that lives in spreadsheets and needs warehouse-scale data without exporting CSVsnot Apache Pinot
  • An organisation that has consolidated on Snowflake or Databricks and wants one governed access layer rather than several desktop toolsnot Apache Pinot
  • Planning and scenario work where users need to type assumptions back into governed tables rather than into a local filenot Apache Pinot
  • Embedding customer-facing analytics into a product where each tenant must only see their own rowsnot Apache Pinot

Where each one falls short

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

Apache Pinot

  • Self-hosted and distributed, so running it means operating a cluster rather than consuming a service
  • Managed hosting comes from third parties such as StarTree rather than from the project
  • Built for user-facing real-time OLAP, so it is not a general purpose database

Sigma Computing

  • Every user interaction is a live warehouse query, so compute costs rise with adoption and land on the warehouse invoice rather than the BI line item, which routinely surprises the team that approved the purchase.
  • It cannot work without a supported cloud data warehouse, so an organisation running analytics on an on-premises database or a plain application database has nothing to connect to.
  • Pricing is not published and seats are differentiated by capability, so budgeting requires a sales cycle and a headcount forecast before you can compare it with a tool you could simply buy.
  • Performance is only as good as the warehouse behind it, which means slow or badly modelled tables surface as a slow interface and the fix is a data engineering project, not a Sigma setting.
  • Statistical and scientific visualisation is limited compared with dedicated tools, so anything beyond standard business charting has to be done elsewhere and brought back in.

Pricing, plan by plan

Apache Pinot

Free
  • Open SourceFree
    • Real-time analytics
    • SQL queries
    • Horizontal scaling

Sigma Computing

On request
  • Sigma Computing$undefined/year
    • Quoted per organisation
    • Seat types are differentiated by whether a user views, explores or authors
    • Warehouse compute is billed separately by your cloud data warehouse vendor

Which should you pick?

Choose Apache Pinot if

  • You need real-time analytics.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want column-oriented.
Apache Pinot review

Choose Sigma Computing if

  • You need spreadsheet interface over sql.
  • You also want no extract layer.
Sigma Computing review

Questions people ask

Is Apache Pinot or Sigma Computing better?
Neither clearly leads. Apache Pinot starts at Free and Sigma Computing at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Pinot or Sigma Computing?
Apache Pinot has a free tier; the other does not. Paid plans start at Free for Apache Pinot and On request for Sigma Computing.
Does Apache Pinot or Sigma Computing run on more platforms?
Apache Pinot runs on Linux, Docker, Kubernetes. Sigma Computing runs on Web.
Can I use Apache Pinot for free?
Yes. Apache Pinot has a free tier, so you can try it without paying. Sigma Computing starts at On request.
What is Apache Pinot best used for?
Apache Pinot is most often used for sub-second analytics queries on freshly ingested data, user-facing dashboards inside a product, real-time metrics at high ingest rates, petabyte-scale analytics as run at linkedin and uber. Of those, sub-second analytics queries on freshly ingested data and user-facing dashboards inside a product are not what Sigma Computing is typically brought in for.
What can Apache Pinot do that Sigma Computing cannot?
Apache Pinot covers Real-time Analytics, Column-oriented, Distributed Processing, SQL Support. Sigma Computing covers Spreadsheet interface over SQL, No extract layer, Input tables and write-back, Workbooks.

Answered from the vendors’ own pages

Apache Pinot: How much does Apache Pinot cost?

Apache Pinot is free and open-source. It is provided under the Apache License, which allows free use, modification, and distribution.

Source
Sigma Computing: Does Sigma store a copy of my data?

No. Queries are executed on your warehouse and results are returned for display, which is why there is no extract refresh to manage.

Apache Pinot: Is Apache Pinot free for commercial use?

Yes. Apache Pinot is licensed under the Apache License, which explicitly permits commercial use at no cost.

Source
Sigma Computing: What does it actually cost?

The vendor does not publish prices. Expect an annual contract priced by seat type, plus a warehouse compute increase you should model separately.

Apache Pinot: Can I run Apache Pinot locally or with Docker?

Yes. Apache Pinot offers a Docker quickstart and free downloads of the latest version (1.5.1 at the time of the page). You are responsible for hosting and infrastructure.

Source
Sigma Computing: Can business users break the warehouse?

They can run expensive queries. Warehouse sizing, query limits and materialised tables are the controls, and they need configuring before a wide rollout.

Apache Pinot: Are there restrictions on how I can use Apache Pinot?

The Apache License permits unrestricted use, but requires retention of license notices and statements. No usage limits or feature restrictions are enforced.

Source
Sigma Computing: Is it a replacement for a modelling layer like dbt?

No. Sigma works best on top of modelled tables and is usually deployed alongside dbt rather than instead of it.

Share

Related pages

More on Apache Pinot

More on Sigma Computing

Keep looking

Other head to heads