Confidential computing infrastructure protects your most sensitive data not just when it’s stored or moving between systems—but while it’s actually being processed. For AI companies training proprietary models, blockchain teams running validator nodes, and SaaS platforms handling customer data, this technology has moved from “nice to have” to business-critical.
The challenge isn’t whether you need confidential computing—it’s finding infrastructure that makes deployment practical rather than a months-long engineering project. In this post, we’ll break down how confidential computing infrastructure serves different industries and what you should look for when evaluating hosting options.
Why Confidential Computing Infrastructure Matters for Modern Workloads
Traditional security approaches encrypt data at rest and in transit, but leave it exposed during processing. This creates a fundamental gap: your most valuable data becomes vulnerable precisely when it’s delivering value. Recent research confirms that confidential computing promises in public cloud setups often don’t match what users expect. For companies where “good enough” security isn’t sufficient, dedicated infrastructure provides the transparency and control that confidential workloads actually need.
Organizations across a broad range of industries recognize Generative AI’s transformative potential and have made it a top priority. And now these companies are turning to their proprietary data—their strategic moat—to extend the capabilities of AI beyond the foundational models. Confidential computing infrastructure closes this gap using hardware-based Trusted Execution Environments (TEEs). Technologies like Intel TDX and AMD SEV-SNP create isolated computing environments where data remains encrypted even while being actively processed. This means your algorithms, training data, and business logic stay protected from privileged access—including system administrators, cloud provider staff, and potential attackers.
The technology addresses three core security challenges that traditional infrastructure can’t solve:
Memory-level protection: Data encryption persists even when loaded into system memory, preventing memory-scraping attacks and unauthorized access during processing.
Hardware-verified isolation: Cryptographic attestation proves that your workload runs in a genuine, uncompromised environment without interference from the underlying infrastructure.
Zero-trust architecture: Your applications can verify the integrity of the computing environment before processing sensitive data, eliminating the need to trust infrastructure providers.
Confidential Computing Infrastructure for AI: Protecting Models and Training Data
AI workloads present unique security challenges that confidential computing infrastructure directly addresses. Data integration challenges, privacy concerns, and security risks can slow down or derail companies’ GenAI pursuits. These obstacles are particularly acute for companies operating in regulated industries, relying on legacy systems, or managing hybrid computing environments.
Model Protection During Training
Training proprietary AI models requires processing massive datasets that often contain sensitive information. Confidential computing infrastructure protects both your training data and the emerging model parameters from exposure. Confidential Computing protects sensitive AI data by encrypting memory and isolating processing environments, helping ensure data remains secure even while actively in use. This is especially critical across the AI pipeline—during model training, fine-tuning, and inference—when raw data and intermediate representations are most exposed.
OpenMetal’s infrastructure supports this with dedicated hardware that eliminates shared tenancy risks. When you’re training models on customer data, medical records, or proprietary business information, you get isolation at the hardware level. This means no noisy neighbors, no shared memory spaces, and no possibility of data leakage between workloads.
Secure Inference and Model Deployment
Deploying AI models creates additional attack vectors. Proprietary AI models represent a major investment in data acquisition, engineering, compute, and domain expertise. These models—whether foundation models, fine-tuned LLMs, or specialized neural networks—are core intellectual property. Confidential computing infrastructure protects against model inversion attacks where attackers query your model to reconstruct sensitive training data.
With OpenMetal’s Intel TDX support, your inference workloads run inside hardware-verified enclaves that restrict access to authorized code only. This prevents both external attacks and insider threats from extracting model weights or reverse-engineering your intellectual property.
GPU-Accelerated Confidential Computing
Many AI workloads require GPU acceleration for training and inference. OpenMetal’s infrastructure supports PCIe passthrough for H100 GPUs within confidential computing environments. This gives you hardware-accelerated performance without compromising the isolation provided by Intel TDX.
The combination means you can run large language model training, computer vision processing, or recommendation algorithms with both the performance you need and the security your data requires.
Blockchain Infrastructure: Confidential Computing for Validators and DeFi
Blockchain workloads demand consistent performance and security guarantees that traditional cloud infrastructure struggles to provide. Confidential computing infrastructure addresses the specific needs of validator nodes, DeFi protocols, and multi-party computation platforms.
Validator Node Security and Performance
Running blockchain validators requires protecting private keys and consensus algorithms from both external attacks and infrastructure-level threats. Validator nodes and sequencer operations benefit from the predictable latency and dedicated networking that bare metal provides. Unlike public cloud VMs that share network interfaces, OpenMetal’s infrastructure provides consistent speed for consensus algorithms and data availability layers.
OpenMetal’s confidential computing infrastructure gives you dedicated hardware with Intel TDX protection for your validator workloads. This eliminates the performance unpredictability of shared infrastructure while protecting your cryptographic operations inside hardware-verified enclaves.
The dual 10 Gbps networking ensures your validators can participate in consensus without network-induced delays that could result in slashing penalties. With unmetered private networking and VLAN isolation, you can build complex validator setups with geographic distribution and failover capabilities.
Multi-Party Computation and DeFi Protocols
DeFi protocols increasingly rely on multi-party computation to enable secure collaboration between organizations that don’t fully trust each other. Crypto workloads that need GPU acceleration for zero-knowledge proofs benefit from direct hardware access without virtualization overhead. OpenMetal’s infrastructure supports GPU passthrough for TEE environments, giving you secure computation that public cloud confidential computing can’t match.
Confidential computing infrastructure enables these protocols by providing verifiable security guarantees. Participating organizations can cryptographically verify that their sensitive data will only be processed within genuine TEEs, removing the need to trust other parties or infrastructure providers.
For zero-knowledge proof generation, you can combine Intel TDX isolation with GPU acceleration. This setup protects the witness data and proving algorithms while delivering the computational performance needed for real-time proof generation.
SaaS Data Protection: Meeting Compliance While Scaling
SaaS platforms face increasing regulatory pressure around data handling, especially when processing customer data across multiple jurisdictions. Confidential computing infrastructure provides the technical foundation for meeting these requirements while maintaining operational flexibility.
Regulatory Compliance and Data Sovereignty
Industries such as healthcare, finance, and public services face mounting regulatory pressure to ensure responsible AI use, with frameworks like General Data Protection Regulation (GDPR), European Union (EU), Health Insurance Portability and Accountability Act (HIPAA), and others enforcing strict controls on how personal and sensitive data is processed.
OpenMetal’s infrastructure supports compliance frameworks like HIPAA, GDPR, and SOC 2 through dedicated hardware and clear audit trails. When you process customer data inside Intel TDX environments, you can demonstrate to auditors that data remains encrypted and isolated throughout processing.
The platform’s architecture also supports data sovereignty requirements. With dedicated hardware and clear geographic boundaries, you maintain control over where customer data is processed and stored.
Customer Data Isolation in Multi-Tenant SaaS
Traditional SaaS architectures struggle to provide true isolation between customer data, especially during processing. Confidential computing infrastructure lets you create customer-specific TEEs that process data without exposing it to other tenants or your own operations team.
Companies processing sensitive personal information gain end-to-end encryption with hardware-backed key management. OpenMetal’s Hosted Private Clouds support advanced security features like encrypted volumes and tenant-aware monitoring, all running on infrastructure where you control the entire security setup.
This approach scales as your customer base grows. You can provision new TEEs for enterprise customers who require dedicated processing environments, while maintaining cost-effective shared resources for smaller customers.
OpenMetal’s Approach to Confidential Computing Infrastructure
OpenMetal’s infrastructure is built specifically for teams that need more than basic cloud performance. Everything runs on dedicated hardware, powered by OpenStack, and designed to keep workloads isolated and secure. Private networking is unmetered, DDoS protection is included by default, and the hardware is built for consistent, high-throughput performance.
Hardware-Level Security Without Compromise
Our bare metal servers eliminate the shared tenancy that creates security gaps in traditional cloud environments. You get dedicated CPU, memory, and storage resources with Intel TDX support built in. This means no noisy neighbors, no virtualization overhead, and no shared attack surfaces.
The network architecture plays a big role here. Every server gets dual 10 Gbps NICs for private networking. VLAN and VXLAN support is already baked in, and you don’t have to touch the physical underlay to scale across locations. There’s no egress fees either, which makes it practical to build something secure and scalable without unexpected costs.
Simplified Deployment and Management
Most providers make you pick between control and scalability, but with OpenMetal you get both. You can spin up a private cloud from dedicated bare metal in under 45 minutes. That gives you the flexibility to deploy Trusted Execution Environments for training AI models, run secure multiparty computation for blockchain, or protect proprietary SaaS code.
Our infrastructure supports common automation tools like Terraform and Ansible, plus OpenStack APIs for programmatic control. This means you can integrate confidential computing into your existing DevOps workflows without rewriting deployment scripts or learning new management interfaces.
Expert Support When You Need It
When you need help, our team’s here. We’re not just answering tickets—we actually know this technology. If you need help with deployment, optimization, or securing your confidential computing environment, you’re working with engineers who’ve been doing this for years across OpenStack, Ceph, and secure infrastructure.
Making the Business Case for Confidential Computing Infrastructure
The decision to adopt confidential computing infrastructure often comes down to risk management and competitive positioning rather than just technical requirements.
Risk Mitigation and Insurance
Recent research from Andreessen Horowitz shows that running at scale can at least double your infrastructure costs in public cloud compared to private options. For confidential workloads specifically, the hidden costs include premium pricing for confidential computing instances that often cost 2-3x standard VMs, plus data transfer fees and compliance overhead.
Confidential computing infrastructure acts as insurance against data breaches, regulatory violations, and intellectual property theft. The cost of implementing proper security controls is typically far less than the potential costs of a security incident.
Competitive Advantage Through Security
In AI, blockchain, and SaaS markets, security capabilities increasingly differentiate products. Companies that can demonstrate hardware-verified data protection gain advantages in enterprise sales cycles and regulatory discussions.
In the GenAI era, data will serve as a competitive moat. Confidential computing infrastructure lets you leverage sensitive data assets that competitors can’t safely process, creating sustainable competitive advantages.
Future-Proofing Against Regulatory Changes
Regulatory frameworks around data protection continue to evolve, especially for AI and financial applications. Building on confidential computing infrastructure positions you ahead of regulatory changes rather than scrambling to achieve compliance after new rules take effect.
The hardware-based attestation capabilities provide cryptographic proof of security controls that satisfy auditors and regulators across different jurisdictions.
Getting Started with Confidential Computing Infrastructure
Adopting confidential computing infrastructure doesn’t require a complete architecture overhaul. Start with your most sensitive workloads and expand as you gain experience with the technology.
Assessment and Planning
Begin by identifying workloads that process sensitive data, intellectual property, or require regulatory compliance. These are natural candidates for confidential computing deployment. Consider factors like data sensitivity, compliance requirements, performance needs, and integration complexity.
For AI workloads, focus on training and inference tasks that use proprietary data or models. For blockchain applications, prioritize validator nodes and any multi-party computation requirements. SaaS platforms should start with customer data processing and any AI features that analyze user information.
Pilot Deployment Strategy
Start with a pilot deployment that demonstrates confidential computing benefits without disrupting existing operations. OpenMetal’s infrastructure makes this practical with rapid provisioning and flexible configurations.
Deploy a single workload inside a TDX-enabled environment and measure the security, performance, and operational differences compared to your current setup. This gives you concrete data for making broader adoption decisions.
Scaling and Integration
Once you’ve validated the approach with pilot workloads, expand to additional use cases and integrate confidential computing into your standard deployment processes. OpenMetal’s APIs and automation support make this integration straightforward.
Consider how confidential computing capabilities change your product roadmap. New features that were previously too risky from a security perspective become practical when you can guarantee hardware-level protection.
Security Doesn’t Have to Be Complicated
Confidential computing infrastructure represents a fundamental shift in how we protect sensitive data during processing. For AI companies, blockchain platforms, and SaaS providers, it’s becoming essential for competitive positioning and regulatory compliance.
The technology is mature enough for production deployment, especially with infrastructure providers like OpenMetal that handle the complexity of hardware configuration and management. You get the security benefits of confidential computing without the operational overhead of managing TEE deployment yourself.
If you’re building products where data protection creates competitive advantage, or where regulatory compliance demands hardware-verified security controls, confidential computing infrastructure deserves serious evaluation. The question isn’t whether these capabilities will become standard—it’s whether you’ll adopt them before or after your competitors.
Ready to explore confidential computing infrastructure for your workloads? Talk to our team about your specific requirements and let’s discuss how OpenMetal can support your security and performance goals.
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