Velum Labs

Name: Velum Labs
Brand: Velum Labs

AI Native

Velum Labs is a TIER-I Cyber Intelligence & Loss Prevention company based out of Southeast Asia.

Est 2025|Raised $500K

LinkedIn 378|Website|FTEs 2|HQ San Francisco, California, US

MarketsData Security Platforms

01Competitive Landscape

Because Velum’s public materials span both policy-based redaction and encrypted compute, the comparison below addresses both frames.

Narrative analysis

Velum vs Zama (Concrete ML): - Zama provides a documented open-source FHE ML stack with public releases and documentation. - Velum’s public performance claims for encrypted compute are not independently validated; organizations should treat those statements as vendor-provided claims until benchmarked. - If the primary requirement is FHE ML compilation or inference, Zama has more public artifacts. If the primary requirement is centralized policy enforcement and redaction across AI and humans, Velum’s firewall framing is more directly relevant, subject to confirmation in a pilot. Velum vs Duality: - Duality positions itself as an established PETs provider with enterprise/government procurement experience. - Velum appears earlier stage and may not match the operational maturity of larger PETs vendors. Velum vs AI DLP/redaction vendors (Nightfall / Private AI): - Many DLP/redaction vendors publish clearer API documentation, integration patterns, and have broader review footprints. - Velum’s differentiation is its claims of a unified policy layer for both AI and human recipients and natural-language policy authoring; these capabilities should be validated in proof-of-concept tests.

Market adoption trends

Public data is insufficient to estimate Velum’s market share.
The broader market for LLM gateways and AI DLP is active, with increasing demand for centralized policy enforcement for AI workflows.

	Velum Labs (vendor materials)	Zama (Concrete ML)	Duality Technologies
Starting price	Not public (beta / sales-led)	Open-source (self-host/DIY); enterprise options vary	Not public (enterprise / government procurement)
Primary use case	Content-level policy enforcement and redaction prior to AI or human access	FHE tooling for compiling and running ML on encrypted data (developer-focused)	PETs platform for secure collaboration and analytics (enterprise/government)
Key strength	Vendor emphasizes a policy enforcement layer and legal/privacy documentation	Mature open-source artifacts, public releases, and documentation	Enterprise/government alignment and broader PETs focus
Key weakness	Limited public proof points: few reviews, no case studies, and unclear product boundaries	FHE complexity and performance/compatibility constraints for some workloads	Sales-led procurement and longer implementation cycles

01Competitive Landscape

Because Velum’s public materials span both policy-based redaction and encrypted compute, the comparison below addresses both frames.

Narrative analysis

Velum vs Zama (Concrete ML):

Zama provides a documented open-source FHE ML stack with public releases and documentation.
Velum’s public performance claims for encrypted compute are not independently validated; organizations should treat those statements as vendor-provided claims until benchmarked.
If the primary requirement is FHE ML compilation or inference, Zama has more public artifacts. If the primary requirement is centralized policy enforcement and redaction across AI and humans, Velum’s firewall framing is more directly relevant, subject to confirmation in a pilot.

Velum vs Duality:

Duality positions itself as an established PETs provider with enterprise/government procurement experience.
Velum appears earlier stage and may not match the operational maturity of larger PETs vendors.

Velum vs AI DLP/redaction vendors (Nightfall / Private AI):

Many DLP/redaction vendors publish clearer API documentation, integration patterns, and have broader review footprints.
Velum’s differentiation is its claims of a unified policy layer for both AI and human recipients and natural-language policy authoring; these capabilities should be validated in proof-of-concept tests.

Market adoption trends

Public data is insufficient to estimate Velum’s market share.
The broader market for LLM gateways and AI DLP is active, with increasing demand for centralized policy enforcement for AI workflows.

	Velum Labs (vendor materials)	Zama (Concrete ML)	Duality Technologies
Starting price	Not public (beta / sales-led)	Open-source (self-host/DIY); enterprise options vary	Not public (enterprise / government procurement)
Primary use case	Content-level policy enforcement and redaction prior to AI or human access	FHE tooling for compiling and running ML on encrypted data (developer-focused)	PETs platform for secure collaboration and analytics (enterprise/government)
Key strength	Vendor emphasizes a policy enforcement layer and legal/privacy documentation	Mature open-source artifacts, public releases, and documentation	Enterprise/government alignment and broader PETs focus
Key weakness	Limited public proof points: few reviews, no case studies, and unclear product boundaries	FHE complexity and performance/compatibility constraints for some workloads	Sales-led procurement and longer implementation cycles