OPAQUE

Name: OPAQUE
Brand: OPAQUE

AI Native

OPAQUE is the confidential AI platform unblocking AI value using sensitive data by making trust verifiable.

Est 2020|Valuation $300M|Raised $55.5M

LinkedIn 6K|Website|Web traffic 2K|FTEs 49|HQ San Francisco, California, US

MarketsData Security Platforms

01Competitive Landscape

Narrative analysis

- OPAQUE addresses a gap between cloud confidential infrastructure primitives and the controls many enterprises require for AI workloads, by adding attestation and audit artifacts tied to workload execution. - Versus hyperscalers: hyperscalers provide confidential infrastructure primitives; OPAQUE focuses on adding evidence and workflow governance on top of those primitives. - Versus platform AI builders: OPAQUE is framed as complementary — focused on data protection and evidence rather than replacing model hosting or model management platforms. - Versus infrastructure-focused confidential Kubernetes orchestration: some competitors emphasize orchestration; OPAQUE emphasizes AI workload governance and attestation evidence.

Market adoption signals

Press coverage and partner announcements indicate continued ecosystem and market interest in confidential computing and encrypted analytics. Vendor participation in industry events and partner integration announcements indicate ecosystem engagement, though broad independent enterprise adoption metrics were not verifiable on public review platforms during this research pass.

	OPAQUE	Fortanix	Enclaive
Starting Price	Not publicly listed (enterprise/quote required)	Not publicly listed (enterprise/quote typical)	Published pricing available for confidential VMs/Kubernetes (varies by provider and region)
Typical Use Case	Enterprises seeking cryptographic attestation and runtime controls for AI workloads running on sensitive data	Organizations prioritizing unified key management, HSM/tokenization, and secrets management alongside confidential compute	Customers seeking transparent, pay-as-you-go confidential compute resources and orchestration
Primary Capabilities	Attestation workflows, TEE-based encrypted computation, and auditable signed evidence artifacts for workload execution	Mature KMS/HSM and tokenization features with confidential compute controls	Confidential VM/Kubernetes compute resources with published pricing and orchestration tooling
Considerations	Limited independent review footprint on public review platforms; enterprise procurement model; reliance on confidential compute availability and operations	Can be more focused on cryptographic services; may be broader than required for purely AI-workflow governance	May not directly provide AI workflow governance or attestation evidence artifacts without additional integration

01Competitive Landscape

Narrative analysis

OPAQUE addresses a gap between cloud confidential infrastructure primitives and the controls many enterprises require for AI workloads, by adding attestation and audit artifacts tied to workload execution.
Versus hyperscalers: hyperscalers provide confidential infrastructure primitives; OPAQUE focuses on adding evidence and workflow governance on top of those primitives.
Versus platform AI builders: OPAQUE is framed as complementary — focused on data protection and evidence rather than replacing model hosting or model management platforms.
Versus infrastructure-focused confidential Kubernetes orchestration: some competitors emphasize orchestration; OPAQUE emphasizes AI workload governance and attestation evidence.

Market adoption signals

	OPAQUE	Fortanix	Enclaive
Starting Price	Not publicly listed (enterprise/quote required)	Not publicly listed (enterprise/quote typical)	Published pricing available for confidential VMs/Kubernetes (varies by provider and region)
Typical Use Case	Enterprises seeking cryptographic attestation and runtime controls for AI workloads running on sensitive data	Organizations prioritizing unified key management, HSM/tokenization, and secrets management alongside confidential compute	Customers seeking transparent, pay-as-you-go confidential compute resources and orchestration
Primary Capabilities	Attestation workflows, TEE-based encrypted computation, and auditable signed evidence artifacts for workload execution	Mature KMS/HSM and tokenization features with confidential compute controls	Confidential VM/Kubernetes compute resources with published pricing and orchestration tooling
Considerations	Limited independent review footprint on public review platforms; enterprise procurement model; reliance on confidential compute availability and operations	Can be more focused on cryptographic services; may be broader than required for purely AI-workflow governance	May not directly provide AI workflow governance or attestation evidence artifacts without additional integration