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Cybersecurity · head to head

Baffle vs BigID

Baffle logo

Baffle

Cybersecurity

Transparent proxy that encrypts, tokenises and masks database fields without application code changes

From
On request
Rated
-
Full reviewWebsite
BigID logo

BigID

Cybersecurity

Data discovery and classification across cloud, on-premise and unstructured stores

From
On request
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Baffle the proxy sits in the production data path, so it becomes a latency contributor and a failure domain, and any deployment needs load and failover testing that customers routinely underestimate.; BigID pricing scales with data sources and volume, so the cost rises exactly as the estate you need to scan grows, and the modules shown in a demo, including AI security posture and headless deployment, are frequently separate licences that appear only in the final quote.
  • They diverge on capability: Baffle covers Transparent proxy deployment, BigID covers Structured and unstructured scanning.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Baffle and BigID actually diverge.

Attributes where Baffle and BigID differ
AttributeBaffleBigID
PlatformsLinux, WebWeb, API, Self-hosted

Identical on both: starting price (On request), pricing model (quote), free tier (No), user rating (Not yet rated), category (Cybersecurity).

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 Baffle

  • Transparent proxy deployment
  • Field-level encryption
  • Tokenisation
  • Format-preserving de-identification
  • Dynamic data masking
  • Bring your own key
  • Analytics and pipeline support
  • AI pipeline protection

Only in BigID

  • Structured and unstructured scanning
  • Identity correlation
  • Data security posture management
  • Access intelligence
  • Privacy request support
  • Retention and minimisation
  • AI data controls
  • Policy and remediation workflow

What people use each for

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

Baffle

  • A bank with a legacy application it cannot safely refactor that has an audit finding requiring field-level encryption of account datanot BigID
  • A company wanting to take a reporting database out of PCI scope by tokenising card fields before they landnot BigID
  • A healthcare organisation that must ensure database administrators and cloud operators cannot read patient identifiers in the tables they administernot BigID
  • A team moving regulated data into a warehouse or an AI retrieval pipeline that needs identifiers de-identified in transit without rewriting the ingest jobsnot BigID

BigID

  • A bank that has to prove which of thirty year old file shares contain customer identifiers before a data centre migrationnot Baffle
  • A privacy team that cannot fulfil deletion requests because nobody knows which unstructured stores hold a given customer’s recordsnot Baffle
  • A security team wanting to find sensitive data sitting in publicly readable object storage buckets before an attacker doesnot Baffle
  • A company building retrieval augmented AI that must exclude regulated personal data from the index it feeds to a modelnot Baffle

Where each one falls short

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

Baffle

  • The proxy sits in the production data path, so it becomes a latency contributor and a failure domain, and any deployment needs load and failover testing that customers routinely underestimate.
  • What you can still do in SQL depends on the protection mode chosen, and stronger modes restrict comparisons, joins and aggregations on protected columns, which can quietly break existing reports and analytics.
  • Database and driver coverage is finite, so an organisation with an unusual engine, an old driver or heavy use of stored procedures may find its most important system is exactly the one not supported.
  • Pricing is unpublished and scales with protected data stores, which means an enterprise trying to protect a long tail of small databases pays disproportionately compared with protecting a handful of large ones.
  • Key management is your responsibility under bring your own key, and while that is the correct security posture, it moves a real operational burden and a genuine data-loss risk onto the customer.

BigID

  • Pricing scales with data sources and volume, so the cost rises exactly as the estate you need to scan grows, and the modules shown in a demo, including AI security posture and headless deployment, are frequently separate licences that appear only in the final quote.
  • Scanning large unstructured estates is slow and computationally expensive, so most organisations sample rather than scan everything, which reintroduces uncertainty into the very question they bought the tool to settle.
  • Classification accuracy on messy unstructured content requires tuning, and out of the box false positives on things like reference numbers create a large triage backlog that a small governance team cannot clear.
  • It discovers and reports but does not remediate, so realising value requires a separate process and often separate tooling to actually delete, restrict or move the data it flags.
  • It is built for large enterprises and both the price and the administrative overhead are disproportionate below a few thousand employees, where a lighter DSPM tool covers the security use case for far less.

Pricing, plan by plan

Baffle

On request
  • Baffle Data Protection Services$undefined/year
    • Quoted by protected data stores and deployment scale
    • Self-managed and cloud marketplace deployment options
    • Annual subscription

BigID

On request
  • BigID Platform$undefined/year
    • Priced by number of data sources and data volume
    • Discovery and classification core
    • Optional DSPM, access intelligence and AI security modules licensed separately

Which should you pick?

Choose Baffle if

  • You need transparent proxy deployment.
  • You work on Linux, Web.
  • You also want field-level encryption.
Baffle review

Choose BigID if

  • You need structured and unstructured scanning.
  • You work on Web, API, Self-hosted.
  • You also want identity correlation.
BigID review

Questions people ask

Is Baffle or BigID better?
Neither clearly leads. Baffle starts at On request and BigID at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Baffle or BigID?
Baffle starts at On request and BigID at On request.
Does Baffle or BigID run on more platforms?
Baffle runs on Linux, Web. BigID runs on Web, API, Self-hosted.
What is Baffle best used for?
Baffle is most often used for a bank with a legacy application it cannot safely refactor that has an audit finding requiring field-level encryption of account data, a company wanting to take a reporting database out of pci scope by tokenising card fields before they land, a healthcare organisation that must ensure database administrators and cloud operators cannot read patient identifiers in the tables they administer, a team moving regulated data into a warehouse or an ai retrieval pipeline that needs identifiers de-identified in transit without rewriting the ingest jobs. Of those, a bank with a legacy application it cannot safely refactor that has an audit finding requiring field-level encryption of account data and a company wanting to take a reporting database out of pci scope by tokenising card fields before they land are not what BigID is typically brought in for.
What can Baffle do that BigID cannot?
Baffle covers Transparent proxy deployment, Field-level encryption, Tokenisation, Format-preserving de-identification. BigID covers Structured and unstructured scanning, Identity correlation, Data security posture management, Access intelligence.

Answered from the vendors’ own pages

Baffle: Do applications need code changes?

No. That is the central design choice. Baffle intercepts traffic as a proxy rather than requiring an SDK call at every read and write.

BigID: What does BigID cost?

It is quoted by data source count and volume. Reported contracts run from roughly 15,000 to 175,000 US dollars a year, and add-on modules such as AI security posture are licensed on top.

Baffle: Can you still query encrypted columns?

Partly, and it depends on the protection mode. Some modes preserve equality matching and format, stronger modes restrict what SQL operations remain possible, so this must be tested against your actual queries.

BigID: Does it handle unstructured data?

Yes, and that is its main advantage. It classifies data in files and shares and correlates findings back to individuals, not just to data types.

Baffle: Does it take systems out of PCI scope?

Tokenisation can reduce scope by ensuring card data never lands in the protected system, but scope reduction is an assessor judgement, not a product setting.

BigID: Will it delete the data it finds?

Not by itself in most deployments. It identifies and routes findings; deletion and remediation happen through your own processes or connected systems.

Baffle: Who holds the encryption keys?

You do, through your own key management service. Baffle supports bring your own key rather than holding customer keys itself.

BigID: Is it a privacy tool or a security tool?

Both are sold from the same discovery core. Buyers increasingly come from security wanting data security posture management rather than from legal wanting privacy.

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