The Global “ Container Infrastructure Software Market” 2021 Research report produces information with reference to market size, share, trends, growth, cost structure, capacity, revenue, and forecast 2026. This report also contains the general and comprehensive study of the Container Infrastructure Software market with all its aspects influencing the growth of the market. This report is a full-scale quantitative survey of the Container Infrastructure Software industry and provides data for building strategies to expand the market growth and effectiveness.

Executive summary:

The research report on Container Infrastructure Software market comprises a thorough evaluation of the past and present business scenario to deliver a conclusive overview of the industry’s growth patterns over 2021-2026. It casts light on the sizes and shares the markets and sub-markets, emphasizing on the primary growth stimulants, obstacles, and lucrative prospects impacting the business dynamics.

As per expert analysts, Container Infrastructure Software market is predicted to record notable growth over the study period, bolstering at XX% CAGR throughout.

The research literature also includes actionable insights into the competitive landscape, stressing on the well-known strategies employed by major contenders to enjoy strong profit margins in the upcoming years. In addition, the document answers all queries relating to implications of COVID-19 pandemic.

Market snapshot:

Geographical outlook:

Key regional contributors of Container Infrastructure Software market are Americas, APAC, Europe, Middle East & Africa.
Market share in relation to the consumption volume and value of each regional market is taken into account.
Role of key regions to the overall industry progression is computed.
Product landscape outline:

The document segments the product terrain of Container Infrastructure Software market into Mobile Phone , Tablet and Computer.
Industry share of every product segment is given.
Data relating to overall revenue generated and total sales amassed by every product type are provided.
Application spectrum summary:

The application spectrum of Container Infrastructure Software market is bifurcated into Monitoring and Logging , Security , Continuous Deployment , Management and Orchestration , Networking and Data Management Services and Others.
Estimates for consumption volume and value of every application scope are supported with stats.
Approximations for each application segment’s market share over the analysis period are enumerated as well.
Competitive arena overview:

Top companies in Container Infrastructure Software market is Dell EMC , HPE , Microsoft , Broadcom , Canonical , Oracle Corporation , Cray , Cavium , Docker , Cisco , Mellanox , SUSE , Supermicro , IBM , Intel , Inspur , WiWyn , Lenovo , Huawei and White Box QCT.
The document offers a business overview of all the mentioned players.
Records of net revenue, pricing models, operating profits, sales, and other financial attributes of each organization are systematically sorted.
Manufacturing facilities and operating areas of the listed players are given.
Up-to-date data on partnerships, associations, and new entrants in the domain are also presented in the study.

What will be the Container Infrastructure Software market scale and growth rate in the estimate years from 2021 to 2026?
What are the significant driving factors of the Container Infrastructure Software market?
What are the opportunity and challenges in front of the market?
Who are the key vendors in the Container Infrastructure Software market?
What are the trending features manipulating market equity?
What are the key products of Porter’s five forces model?
what are the global prospects for expanding the global Container Infrastructure Software market?
Reasons why you should buy this report

Understand the current and future of the Container Infrastructure Software Market in both developed and emerging markets.
The report assists in realigning the business strategies by highlighting the Container Infrastructure Software business priorities.
The report throws light on the segment expected to dominate the Container Infrastructure Software industry and market.
Forecasts the regions expected to perceive Rapid growth.
The latest developments in the Container Infrastructure Software industry and details of the industry leaders along with their market share and strategies.
Saves time on the entry level research as the report contains important data concerning about growth, size, leading players and segments of the industry.
Save and cut time carrying out entry-level research by distinguishing the growth, size, leading players and segments within the global Market.

AUSTIN, Texas, March 16, 2021 /PRNewswire/ — Liftr Insights, a pioneer in market intelligence about hyperscale cloud infrastructure, has identified vast market share changes over the past year of which others have been unaware.

What is Cloud Chip Share?

The top four public cloud providers represent a significant portion of semiconductor chip consumption with their hyperscale infrastructure. Their purchase decisions affect the overall chip market, so investment portfolio managers and research analysts, as well as supply chain vendors, pay close attention.

Making otherwise opaque information visible

“Our market intelligence, leveraging unique datasets, provides visibility into infrastructure activity which would otherwise be opaque,” says Tab Schadt, CEO of Liftr Insights. “Our customers never expected to see this dramatic of a shift among the technology providers. It’s been exciting to watch the changes from one of our monthly analyses to the next.”

What is changing?

Liftr Insights data demonstrates that over the past 18 months, Intel’s share of offerings in the public cloud market has decreased over 12%. While our data shows AMD gaining ground, the true disrupter is Amazon Web Services. AWS is producing, marketing and expanding deployments of their own ARM-based Graviton processors. Liftr Insight’s data shows that AWS Graviton now makes up nearly 4% of offerings across the top four CSPs and almost 14% of AWS’s offerings. This is up from less than 1% 18 months earlier.

Early Insights

“There are rumors that Azure and other providers may follow AWS’s lead,” says Schadt. “Our customers will be among the first to see and track when those changes occur.”

Liftr Insights

Liftr Insights generates market intelligence to portfolio managers and research analysts as well as supply chain customers. Among the data obtained are configurations, components, deployment geo and pricing for:

Server processors: Intel Xeon, AMD EPYC, and AWS’s Arm-based Graviton brands
Datacenter compute accelerators: GPUs, FPGAs, TPUs and AI chips from NVIDIA, Xilinx, Intel, AMD, AWS and Google
Among the companies tracked are Amazon Web Services, Microsoft Azure, Alibaba Cloud, and Google Cloud, as well as semiconductor vendors AMD, Intel, NVIDIA, and Xilinx. Liftr Insights subject matter experts translate company-specific service provider data into actionable alternative data. Technology investors can easily ingest this timely, standardized and operationally-compliant information into their predictive financial models.

Major Cloud Vendors Serving Retail Banking

The competitive landscape in cloud services for retail banking is broad. Hyperscalers and specialised enterprise software vendors both compete for bank workloads. Prominent players include:

• Amazon Web Services (AWS) – Offers dedicated financial-services competency programmes, compliance frameworks (PCI-DSS, SOC 2), and managed database and AI/ML services widely used by banks.
• Microsoft Azure – Provides strong integration with enterprise productivity tools and Azure Government/Sovereign Cloud options for regulated data residency requirements.
• IBM – Long-standing banking partner with hybrid cloud offerings built on Red Hat OpenShift, plus managed services for mainframe workload migration.
• Google Cloud – Increasingly targeting banking analytics and AI workloads, with dedicated financial-services industry solutions.
• Alibaba Cloud, Tencent Cloud, Huawei Cloud, Baidu AI Cloud, China Telecom, China Unicom, Kingsoft Cloud, and UCloud – Dominant cloud providers serving Asia-Pacific retail banks, particularly in China where domestic regulations shape cloud procurement.
• Infosys, TCS, and Wipro – Systems integrators and managed-service providers that deliver cloud migration, DevOps tooling, and banking-platform modernisation on top of hyperscaler infrastructure.
• Oracle, SAP, Salesforce, Workday, and Intuit – SaaS and PaaS vendors supplying core banking, CRM, ERP, and financial-management capabilities as cloud-native or cloud-hosted services.
• Medidata, Veeva Systems, and Ellie Mae – Vertical SaaS providers whose platforms intersect with banking and financial-services data management use cases.
• BBVA and Bankinter – Notable examples of retail banks that have invested heavily in internal cloud platforms and open-banking APIs, making them both consumers and, in some cases, providers of cloud-based financial technology.

Market Segmentation

By Service Type

• Infrastructure as a Service (IaaS) – Compute, storage, and networking resources consumed on demand; commonly used for disaster-recovery, batch processing, and dev/test environments.
• Platform as a Service (PaaS) – Managed runtime environments, databases, messaging queues, and integration layers that reduce operational overhead for development teams.
• Software as a Service (SaaS) – Fully managed applications such as core-banking systems, CRM, fraud-detection engines, and regulatory-reporting tools.

By Deployment Model

• Public Cloud – Shared infrastructure managed by a hyperscaler; cost-efficient for non-sensitive workloads.
• Private Cloud – Dedicated infrastructure operated on-premises or in a co-location facility; preferred for sensitive customer and payment data.
• Hybrid Cloud – Combination of public and private environments connected by orchestration tooling; increasingly the dominant model in banking because it allows regulatory data-residency controls alongside cloud-native scalability.

By Application

• Core banking system modernisation
• Customer relationship management (CRM) and personalisation
• Fraud detection and anti-money-laundering (AML) analytics
• Regulatory compliance and reporting
• Mobile and digital banking platforms
• Risk management and stress testing
• Data warehousing and business intelligence

Regional Landscape

Cloud adoption patterns in retail banking vary significantly by region, driven by local regulation, infrastructure maturity, and competitive pressure from fintech entrants.

North America (United States, Canada, Mexico)

North America represents one of the most mature markets. Large US banks have invested heavily in multi-cloud strategies, and regulators such as the OCC and FFIEC have issued guidance on cloud risk management rather than outright restrictions. Canada’s OSFI and Mexico’s CNBV each impose their own data-residency and third-party risk requirements.

Europe (Germany, France, UK, Russia, Italy)

GDPR, EBA outsourcing guidelines, and the EU Digital Operational Resilience Act (DORA) heavily influence cloud procurement decisions. Many European banks favour hybrid or sovereign-cloud arrangements to maintain data control. The UK’s post-Brexit regulatory environment, managed by the PRA and FCA, adds additional complexity for cross-border deployments.

Asia-Pacific (China, Japan, South Korea, India, Southeast Asia)

Asia-Pacific is one of the fastest-growing regions for cloud adoption in banking. China’s domestic cloud providers (Alibaba, Tencent, Huawei, Baidu, China Telecom, China Unicom) dominate the local market due to data-localisation requirements. India’s RBI has issued cloud-adoption frameworks encouraging managed migration. Southeast Asian markets show rapid growth driven by digital-only bank licensing regimes.

South America (Brazil, Argentina, Colombia)

Brazil’s LGPD privacy law and the Central Bank of Brazil’s open-finance initiatives are accelerating cloud investment. Hyperscalers have expanded local infrastructure to meet data-residency requirements.

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa)

Cloud adoption is growing from a lower baseline. The UAE and Saudi Arabia have established clear cloud-first government policies that indirectly influence banking. Nigeria and South Africa represent the leading African markets, with central banks developing cloud-risk guidance.

Key Growth Drivers

• Legacy system debt – Decades-old mainframe and on-premises applications are expensive to maintain and difficult to integrate with modern APIs, creating a strong business case for cloud migration.
• Fintech competition – Digital-native challengers operate at lower cost bases using cloud-native architectures, pressuring incumbent banks to modernise.
• Customer experience demands – Real-time payments, personalised offers, and always-on mobile services require the elastic scalability that cloud platforms provide.
• Regulatory reporting volume – Increasing regulatory complexity demands analytics platforms capable of processing large, rapidly changing datasets—a strength of cloud data warehousing.
• AI and machine learning adoption – Fraud detection, credit scoring, and customer segmentation models require compute resources that are cost-prohibitive to provision on-premises at scale.
• Operational resilience requirements – Regulators increasingly demand active-active disaster-recovery capabilities that public cloud regions and availability zones can deliver more economically than traditional data-centre pairs.

Key Restraints and Challenges

• Data sovereignty and residency rules – Regulations in many jurisdictions restrict where customer financial data can be stored or processed, limiting hyperscaler flexibility.
• Concentration risk – Regulators worry that widespread dependence on a small number of hyperscalers creates systemic risk for the financial sector.
• Security and compliance complexity – Banks must maintain PCI-DSS, SOC 2, ISO 27001, and local regulatory certifications across cloud environments, increasing operational overhead.
• Skills gaps – Cloud-native development, DevSecOps, and FinOps expertise remain scarce in many banking organisations.
• Migration risk – Moving mission-critical core-banking systems carries significant operational risk and requires extensive testing and rollback planning.

Technology and Innovation Trends

• API-led open banking – Cloud platforms provide the managed API gateways and event-streaming infrastructure (e.g., Kafka-as-a-service) that open-banking mandates require.
• Containerisation and Kubernetes – Banks are increasingly packaging workloads in containers to gain portability across cloud providers and reduce vendor lock-in.
• Serverless and event-driven architectures – Reducing idle compute costs for transaction-processing pipelines with variable load profiles.
• Confidential computing – Hardware-based trusted execution environments (TEEs) allow sensitive banking data to be processed in the cloud without exposing plaintext to the cloud provider.
• Generative AI integration – Banks are piloting large-language models for customer service automation, document processing, and code generation, typically through managed cloud AI services.

Conclusion

Cloud computing is no longer an emerging concept in retail banking—it is an operational reality for institutions of all sizes. The combination of legacy modernisation pressure, fintech competition, and demand for real-time, data-driven services means that cloud adoption will continue to accelerate across all regions. For DevOps and platform engineering teams working in financial services, understanding the regulatory context, vendor landscape, and architectural patterns specific to banking is essential for designing resilient, compliant, and cost-effective cloud environments.

The Hardware Underneath the Cloud

“There is no cloud; it’s just someone else’s computer” is a well-worn phrase in infrastructure circles, but it carries real weight at events like VMworld. Beneath every abstraction layer — every virtual machine, container, and software-defined network — sits physical hardware. At VMworld in San Francisco, networking and storage vendors gathered to remind the industry that hardware choices still shape what is possible in multicloud architectures.

Bob Ghaffari, General Manager of Intel’s Enterprise & Cloud Networking Division, used the event to outline how Intel is positioning its networking portfolio for hybrid and multicloud environments — from foundational network interface cards (NICs) to programmable FPGA-based accelerators and edge computing solutions.

A Decade of Network Transformation: Intel’s Role in DPDK

Ghaffari traced Intel’s networking involvement back roughly ten years, to the industry’s shift away from proprietary network processors toward standard, open architectures. A pivotal contribution was Intel’s open-sourcing of the Data Plane Development Kit (DPDK) — a set of libraries and drivers for fast packet processing that runs in user space and bypasses the kernel networking stack for dramatically higher throughput.

DPDK became foundational infrastructure for software-defined networking (SDN) and network functions virtualization (NFV). By enabling high-speed packet processing on commodity x86 hardware, it helped precipitate the broader shift toward software-defined, cloud-native networking that VMware NSX and similar platforms depend on today.

“We open-sourced [DPDK], which is really this fast packet processing library that we basically enabled the industry on. And with that, there’s basically been this revolution in terms of how networking has come together.”

— Bob Ghaffari, Intel

Intel and VMware NSX: Enabling Hybrid and Multicloud Networking

Ghaffari highlighted VMware NSX as a key integration point, noting that NSX had matured significantly in the preceding year and was enabling newer, more sophisticated networking use cases. For Intel, NSX represents a direct enabler of hybrid cloud and multicloud connectivity — the ability to extend consistent networking policies across on-premises infrastructure and multiple public cloud environments.

Intel’s strategy is to offer a tiered hardware portfolio — from cost-optimized NICs to high-performance FPGA-based SmartNICs — so that operators can match hardware capability to workload requirements regardless of which cloud or edge location they are targeting.

“We think we have a really good position here with our platforms — giving you this mix-and-match capability but easily be able to scale up and do what our customers want.”

— Bob Ghaffari, Intel

Microsegmentation: Security Through Policy Isolation

Interviewer Justin Warren raised microsegmentation — a topic that was generating significant interest and some frustration among enterprise customers at the time. Microsegmentation divides a network into small, isolated zones so that a breach or exploit in one segment cannot propagate laterally to others. While the security benefits are compelling, operational complexity has been a common barrier to adoption.

Ghaffari acknowledged the complexity but framed microsegmentation as ultimately simplifying the security posture for enterprises that implement it correctly:

• Policy containment: Security and compliance policies are enforced within defined segments rather than relying on a single network perimeter.
• Threat containment: A successful attack or exploit is confined to its originating segment, limiting blast radius.
• Environment clarity: Segmentation can make it easier to reason about what is running where and who has access to what.

The challenge, as Warren noted, is that many customers find initial configuration and ongoing management difficult — which has created a market for third-party tools that layer policy management on top of NSX and competing SDN platforms.

The Intel FPGA N3000: Programmable Acceleration for Virtual Switching

One of the more technically specific topics in the interview was Intel’s FPGA-based network adapter. Ghaffari cited the Intel FPGA N3000 as an example of how customers can go beyond standard NIC capabilities to accelerate custom networking functions and virtual switches in hardware.

FPGA-based SmartNICs occupy a middle ground between fixed-function ASICs and general-purpose CPUs:

• They can be reprogrammed to accelerate specific workloads — including virtual switching, security functions, or 5G baseband processing — without replacing the physical card.
• They offload networking functions from the host CPU, freeing compute resources for application workloads.
• They allow infrastructure operators to adapt hardware behavior as software-defined networking requirements evolve.

For operators running VMware NSX or other SDN overlays at scale, offloading virtual switch processing to a SmartNIC can meaningfully reduce CPU overhead on the host.

Edge Computing: Challenging the “All Intel Does Is Big Xeons” Assumption

Ghaffari described a common misconception among customers evaluating edge deployments: the assumption that Intel hardware is only cost-effective at the high end. Customers planning small-footprint deployments — in retail locations, branch offices, or distributed edge sites — often assume Intel’s price points are prohibitive.

Intel’s response has been to develop add-on processors and purpose-built platforms that hit lower price points while retaining the scalability and software ecosystem compatibility that x86 architecture provides. The implication for DevOps and infrastructure teams is that a consistent programming model — including DPDK-based networking — can be maintained from the core data center out to distributed edge nodes, even at constrained cost targets.

5G and What Comes Next

The full interview also touched on 5G wireless technologies and their intersection with edge computing — a topic that was early-stage in mid-2019 but has since become central to infrastructure planning for carriers and enterprises alike. The core thesis: as 5G shifts more processing to the network edge, the same DPDK-based, software-defined networking capabilities Intel has been building for data centers become relevant for radio access network (RAN) infrastructure.

Key Takeaways for Infrastructure and DevOps Teams

• DPDK remains foundational: If you are running any high-throughput SDN overlay — NSX, OVS-DPDK, or similar — understanding the underlying packet processing library matters for performance tuning.
• SmartNICs are a practical offload option: FPGA-based and ASIC-based SmartNICs are increasingly viable for offloading vSwitch and security functions, particularly in high-density virtualization environments.
• Microsegmentation complexity is real: Plan for operational tooling when deploying microsegmentation at scale; policy management does not end at initial configuration.
• Edge hardware has matured: x86-based edge platforms are no longer exclusively high-cost server-class hardware; purpose-built edge processors have closed the price gap for lightweight deployments.
• Multicloud networking requires a hardware strategy: Consistent connectivity across clouds and edge sites depends on NIC capabilities, driver support, and offload features — not just software configuration.

The original interview was conducted by theCUBE at VMworld, San Francisco. Full interview and transcript available via SiliconANGLE.

The Problem: Data In Use Remains Exposed

Enterprise security teams have well-established tools for encrypting data at rest (on disk) and data in transit (over the network). The third state — data in use, actively loaded into memory and being processed by a CPU — has historically been left unencrypted and therefore vulnerable.

As Lorie Wigle, Intel’s VP overseeing platform security product management, put it in a blog post at the time:

“Whether running on your own servers on-prem, in an edge deployment or in the heart of a cloud service provider’s data center, this ‘in-use’ data is almost always unencrypted and potentially vulnerable.”

Lorie Wigle, Intel

This gap becomes especially significant in multi-tenant cloud environments, where multiple workloads share the same physical hardware and a compromised hypervisor or operating system could, in principle, read another tenant’s in-memory data.

Introducing the Confidential Computing Consortium

To address this problem, the Linux Foundation announced the formation of the Confidential Computing Consortium (CCC) in August 2019. The founding members committed to making open-source contributions and included:

• Alibaba
• Arm
• Baidu
• Google Cloud
• IBM
• Intel
• Microsoft
• Red Hat
• Swisscom
• Tencent

Jim Zemlin, Executive Director of the Linux Foundation, framed the initiative as foundational industry work:

“The earliest work on technologies that have the ability to transform an industry is often done in collaboration across the industry and with open-source technologies. The Confidential Computing Consortium is a leading indicator of what’s to come for security in computing and will help define and build open technologies to support this trust infrastructure for data in use.”

Jim Zemlin, Executive Director, The Linux Foundation

What Is Confidential Computing?

Confidential computing refers to hardware and software techniques that allow systems to process sensitive data in memory while isolating it from the rest of the system — including the operating system, the hypervisor, and the cloud provider’s management stack. The goal is to ensure that even a privileged administrator or a compromised system layer cannot access data while it is being computed on.

The consortium’s work spans three areas:

• Technical standards: Defining common APIs and attestation mechanisms so that confidential computing solutions interoperate across hardware architectures and cloud platforms.
• Regulatory frameworks: Working with standards bodies to help regulated industries (finance, healthcare, government) adopt these technologies with confidence.
• Open-source tooling: Producing and maintaining libraries, SDKs, and frameworks accessible to any developer or organization.

Trusted Execution Environments (TEEs): The Core Mechanism

A central concept in confidential computing is the Trusted Execution Environment (TEE), sometimes called a trusted enclave. A TEE is a hardware-isolated region of memory where code and data are protected from access by any other software — including the operating system and cloud management stack.

As Wigle explained:

“Combined with encrypted data storage and transmission methods, TEEs can create an end-to-end protection architecture for your most sensitive data.”

Lorie Wigle, Intel

Intel SGX: Hardware-Backed Enclaves

Intel contributed the software development kit (SDK) for Intel Software Guard Extensions (Intel SGX) to the consortium. SGX is a set of CPU instructions that lets an application create a private, hardware-protected memory region — an enclave — with a direct hardware interface that bypasses the OS and hypervisor.

Wigle distinguished SGX from full memory encryption approaches:

“Unlike full memory encryption technologies that leave the data within the attack surface of the OS and cloud stack, Intel SGX allows a specific application to create its own protected enclave with a direct interface to the hardware, limiting access and minimizing the overall performance impact for both the application and any other virtual machines or tenants on the server.”

Lorie Wigle, Intel

At the time of the announcement, Intel SGX was available in Xeon E-2100 processors and was supported by cloud instances from Microsoft Azure, IBM Cloud Data Guard, Baidu, Alibaba Cloud, and Equinix. Intel also disclosed plans for an SGX add-in card for servers and future SGX support in mainstream Xeon chips.

Microsoft Open Enclave SDK

Microsoft contributed the Open Enclave SDK, an open-source framework that allows developers to write enclave-based applications once and run them across multiple TEE implementations — abstracting away the differences between hardware platforms.

Red Hat Enarx

Red Hat contributed Enarx, a project designed to support running “private, fungible serverless” applications inside TEEs. Enarx aims to let workloads run in trusted environments without developers needing to rewrite code for each specific hardware TEE implementation.

Known Limitations and Vulnerabilities in Intel SGX

It is worth noting that Intel SGX is not without a security track record of its own. Research groups had previously reported vulnerabilities against the technology:

• Foreshadow (L1TF): A speculative execution vulnerability that could leak enclave data. Intel stated this had been mitigated through microcode and OS patches.
• Malicious code execution in enclaves: Researchers demonstrated that malicious code could be loaded and run inside an SGX enclave. Intel’s position was that this falls outside SGX’s threat model, since SGX is designed to protect enclave code from the outside system — not to validate that the code loaded into the enclave is itself trustworthy. Intel’s guidance: “In all cases, we recommend utilizing programs, files, apps, and plug-ins from trusted sources.”

These disclosures highlight an important nuance for practitioners: TEEs protect against external access to enclave memory, but they do not independently verify the integrity or trustworthiness of the workload running inside. Attestation mechanisms — which allow a remote party to verify what code is running in an enclave — are a separate and complementary concern that the CCC’s work also addresses.

Industry Reception

Miles Ward, CTO at SADA Systems (a Google Cloud partner), welcomed the initiative:

“Vendors working together, in the open, to drive secure-by-default solutions that work coherently across providers is a big step forward for customers. Building on initiatives like homomorphic encryption (already available in Google BigQuery), CSEK and CMEK controls, secure enclaves for trusted execution can only accelerate progress towards these critical requirements.”

Miles Ward, CTO, SADA Systems

Why This Matters for DevOps and Platform Engineers

For teams running workloads in public cloud environments, confidential computing represents a shift in the trust model. Historically, using a cloud provider required trusting the provider’s entire infrastructure stack, including its hypervisor and privileged system software. TEEs allow sensitive computation to be isolated at the hardware level, so that:

• Regulated data (PII, financial records, health records) can be processed in the cloud without relying solely on contractual trust in the provider.
• Multi-party computation scenarios become more practical — different organizations can contribute data to a shared computation without any party (including the cloud host) seeing the raw inputs.
• Zero-trust architectures gain a hardware-backed foundation for workload isolation.

The Confidential Computing Consortium’s ongoing work — including the Open Enclave SDK, Enarx, and Intel SGX SDK — is available through the Linux Foundation. Engineers evaluating confidential computing for their workloads should review the CCC’s current project portfolio for the latest status of each tool, as the ecosystem has continued to evolve since the consortium’s 2019 launch.