Google Confidential Computing
Encryption software
Confidentiality software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is Google Confidential Computing
Google Confidential Computing is a set of Google Cloud capabilities that helps protect data in use by isolating workloads and encrypting memory while code runs in trusted execution environments. It targets security, compliance, and platform teams running sensitive workloads on Google Cloud services such as virtual machines, containers, and managed data/analytics services. The product focuses on reducing exposure to privileged access and certain host-level attacks by using hardware-backed attestation and workload isolation. It is typically used for regulated data processing, multi-party collaboration scenarios, and protecting secrets during computation.
Hardware-backed isolation for workloads
The offering uses trusted execution environments to isolate workloads and protect memory while applications run. This addresses a different risk area than at-rest or in-transit encryption by focusing on data-in-use. It can reduce exposure to certain classes of attacks involving host compromise or unauthorized administrative access. It is well-suited to workloads that must process sensitive data without revealing it to the underlying infrastructure.
Attestation and policy enforcement
Google Cloud provides attestation mechanisms that allow customers to verify the security posture of the environment before releasing secrets or processing sensitive data. This supports designs where access to keys, tokens, or datasets depends on measured runtime conditions. It can be integrated into automated deployment pipelines to enforce security gates. This capability is a key differentiator versus tools that primarily focus on tokenization or file-level controls.
Integration across Google Cloud services
Confidential computing features are available across multiple Google Cloud compute and data services, enabling consistent patterns for protecting sensitive workloads. This reduces the need to assemble separate point solutions for different runtime environments. Centralized cloud controls and IAM can be used alongside confidential computing to manage access and operations. For organizations already standardizing on Google Cloud, this can simplify adoption compared with standalone confidentiality platforms.
Google Cloud platform dependency
The capabilities are delivered as part of Google Cloud services, so portability to other cloud providers or on-premises environments is limited. Organizations pursuing multi-cloud or hybrid standardization may need additional tools or different implementations elsewhere. This can increase architectural complexity when consistent controls are required across environments. Vendor-specific service availability also affects which workloads can use the features.
Workload and performance constraints
Not all workload types, machine families, or managed services support confidential computing features, and supported configurations can vary by region. Using trusted execution environments can introduce performance overhead and operational constraints compared with standard compute. Some applications may require code changes or specific deployment patterns to benefit from attestation and secret release workflows. These factors can limit applicability for latency-sensitive or legacy workloads.
Does not replace data governance
Confidential computing protects data during processing but does not by itself provide full data discovery, classification, masking/tokenization, or rights management. Organizations still need complementary controls for data lifecycle governance, access reviews, and policy enforcement at the application and data layer. It also does not inherently prevent misuse by authorized applications once data is decrypted inside the trusted boundary. Buyers should evaluate it as part of a broader confidentiality and privacy control stack.
Plan & Pricing
Pricing model: Pay-as-you-go Free tier/trial: Google Cloud Free Trial: $300 welcome credit for new customers (valid for 91 days); Google Cloud Free Tier (always-free products) exists but Confidential VMs are not listed as an always-free product. Example costs (official Google Cloud Confidential VM pricing, on-demand and spot shown where available):
- AMD SEV (N2D, C2D, C3D, C4D series): vCPU – $0.005479 per vCPU/hour (on‑demand); $0.0012822 per vCPU/hour (spot). Memory – $0.0007342 per GiB/hour (on‑demand); $0.0001712 per GiB/hour (spot).
- AMD SEV‑SNP (N2D series): vCPU – $0.0027502 per vCPU/hour (on‑demand); $0.000436 per vCPU/hour (spot). Memory – $0.0003686 per GiB/hour (on‑demand); $0.0000584 per GiB/hour (spot).
- Intel TDX (C3 series): vCPU – $0.0033982 per vCPU/hour (on‑demand); $0.001155 per vCPU/hour (spot). Memory – $0.0004555 per GiB/hour (on‑demand); $0.0001549 per GiB/hour (spot).
- NVIDIA Confidential Computing (A3 series with NVIDIA H100 GPUs): vCPU – $0.0025498 per vCPU/hour (on‑demand); $0.00102 per vCPU/hour (spot); DWS flex-start $0.001189482 per vCPU/hour. RAM – $0.000222034 per GiB/hour (on‑demand); $0.0000888 per GiB/hour (spot); DWS flex-start $0.000103579 per GiB/hour. H100 GPU – $0.979655057 per GPU/hour (on‑demand); $0.39186 per GPU/hour (spot); DWS flex-start $0.457009084 per GPU/hour.
- Confidential Balanced (Autopilot pod) pod pricing (Confidential Autopilot pods / mCPU & memory): Confidential Autopilot pod mCPU requests – $0.00645 per 1,000 hour (on‑demand); $0.00194 per 1,000 hour (spot). Confidential Autopilot pod memory – $0.00071354 per GB/hour (on‑demand); $0.00021406 per GB/hour (spot).
Notes & key features:
- Confidential VM pricing is an additional flat per‑vCPU and per‑GiB (memory) charge on top of underlying Compute Engine (or GKE/GKE Autopilot/other) resource charges.
- Spot prices are variable and can change (typical discounts ~60–91% vs on‑demand) per Google Cloud documentation.
- Confidential Space: there is no additional product charge for Confidential Space itself; you are billed for the underlying Confidential VM and other resources used.
- Discounts and committed pricing that apply to underlying Compute Engine resources still apply; Google Cloud also offers contact‑sales/custom quotes.
(Information sourced exclusively from Google Cloud official pages: the Confidential VM pricing page and related Google Cloud Free program and Confidential Space pricing pages.)
Seller details
Google LLC
Mountain View, CA, USA
1998
Subsidiary
https://cloud.google.com/deep-learning-vm
https://x.com/googlecloud
https://www.linkedin.com/company/google/
