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Databases · head to head

Immuta vs Sigma Computing

Immuta logo

Immuta

Databases

Attribute-based access control and masking applied inside Snowflake, Databricks and BigQuery

From
On request
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

  • Each has a real cost: Immuta contracts commonly start around one hundred to two hundred thousand US dollars a year for mid-market deployments and exceed five hundred thousand at enterprise scale, which excludes most data teams without a regulatory mandate.; 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: Immuta covers Attribute-based policy, Sigma Computing covers Spreadsheet interface over SQL.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Immuta and Sigma Computing actually diverge.

Attributes where Immuta and Sigma Computing differ
AttributeImmutaSigma Computing
PlatformsWeb, API, CloudWeb
CategoryDatabasesSpreadsheets

Identical on both: starting price (On request), pricing model (quote), free tier (No), 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 Immuta

  • Attribute-based policy
  • Native enforcement
  • Dynamic masking
  • Row-level filtering
  • Purpose-based access
  • Sensitive data tagging
  • Audit logging
  • Multi-platform

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.

Immuta

  • A bank whose Snowflake estate has grown to tens of thousands of roles that no one can review before an auditnot Sigma Computing
  • A healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recordednot Sigma Computing
  • A multinational applying different residency and access rules per jurisdiction to the same tables without duplicating datasetsnot Sigma Computing
  • An organisation running both Snowflake and Databricks that wants one policy set rather than two divergent implementationsnot Sigma Computing

Sigma Computing

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

Where each one falls short

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

Immuta

  • Contracts commonly start around one hundred to two hundred thousand US dollars a year for mid-market deployments and exceed five hundred thousand at enterprise scale, which excludes most data teams without a regulatory mandate.
  • Policy is only as good as the data classification underneath it, so an organisation with poorly tagged columns will spend months on classification before Immuta enforces anything useful.
  • Native enforcement means capability varies by platform, and a feature available on Snowflake may be absent or behave differently on BigQuery, which undermines the promise of one policy set everywhere.
  • Adding an access governance layer creates a new dependency in the path to data: a misconfigured policy silently returns fewer rows rather than erroring, and analysts can act on incomplete results without noticing.
  • It governs cloud data platforms, so personal data in operational databases, files and SaaS applications sits outside its scope and needs separate controls, meaning Immuta is rarely the whole answer.

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

Immuta

On request
  • Immuta Platform$undefined/year
    • Attribute-based policy authoring
    • Native enforcement in supported data platforms
    • Dynamic masking and row-level security

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 Immuta if

  • You need attribute-based policy.
  • You work on Web, API, Cloud.
  • You also want native enforcement.
Immuta review

Choose Sigma Computing if

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

Questions people ask

Is Immuta or Sigma Computing better?
Neither clearly leads. Immuta starts at On request and Sigma Computing at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Immuta or Sigma Computing?
Immuta starts at On request and Sigma Computing at On request.
Does Immuta or Sigma Computing run on more platforms?
Immuta runs on Web, API, Cloud. Sigma Computing runs on Web.
What is Immuta best used for?
Immuta is most often used for a bank whose snowflake estate has grown to tens of thousands of roles that no one can review before an audit, a healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recorded, a multinational applying different residency and access rules per jurisdiction to the same tables without duplicating datasets, an organisation running both snowflake and databricks that wants one policy set rather than two divergent implementations. Of those, a bank whose snowflake estate has grown to tens of thousands of roles that no one can review before an audit and a healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recorded are not what Sigma Computing is typically brought in for.
What can Immuta do that Sigma Computing cannot?
Immuta covers Attribute-based policy, Native enforcement, Dynamic masking, Row-level filtering. Sigma Computing covers Spreadsheet interface over SQL, No extract layer, Input tables and write-back, Workbooks.

Answered from the vendors’ own pages

Immuta: Does Immuta sit in the query path?

No. It compiles policies into the data platform's own native controls, so queries run at normal speed through your existing tools.

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.

Immuta: What does it cost?

Not published. Market data suggests roughly 100,000 to 200,000 US dollars a year for mid-market deployments and considerably more at enterprise scale.

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.

Immuta: Is Immuta still independent?

Yes. It remains independently owned, unlike several competitors in data access governance that have been acquired.

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.

Immuta: Does it work across more than one warehouse?

Yes, one policy set can target Snowflake, Databricks, BigQuery and Starburst, though enforcement capability varies by platform.

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.

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