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The 2024 CNCF Annual Survey found that cloud-native adoption is moving into mainstream operations: 91% of surveyed organizations run containers in production, 80% run Kubernetes in production, and 93% use, pilot, or evaluate Kubernetes. Published in 2025, the survey also shows that training, culture, security, observability, and platform complexity remain major challenges.
The report’s formal title is Cloud Native 2024: Approaching a Decade of Code, Cloud, and Change. CNCF and Linux Foundation Research conducted the twelfth annual survey in fall 2024 and published the results on April 1, 2025. The report is best read as a directional snapshot of the CNCF-connected cloud-native community—not as a census of every software organization or proof that Kubernetes is the right answer for every workload.
Key takeaways
- According to the 2024 CNCF Annual Survey, 91% of surveyed organizations use containers in production and 80% use Kubernetes in production.
- Kubernetes engagement reached 93% when production use, piloting, and active evaluation are combined, but evaluation is not equivalent to successful production operation.
- Helm was the preferred Kubernetes application-packaging method for 75% of respondents, up from 56% in the report’s 2023 comparison.
- AI/ML on Kubernetes remains early: 48% of respondents said they were not running AI/ML workloads on Kubernetes.
- Release automation is improving, with 38% reporting that 80%–100% of releases were automated and average automation rising from 56.5% to 59.2% in the report’s year-over-year comparison.
- The largest barriers are increasingly organizational and operational, including team culture, training, CI/CD integration, security, monitoring, complexity, and documentation.
What did the 2024 CNCF Annual Survey measure?
The 2024 CNCF Annual Survey measured cloud-native adoption, containers, Kubernetes, CNCF projects, security practices, AI/ML, WebAssembly, serverless, service mesh, stateful applications, CI/CD, GitOps, release automation, and related operating practices. The survey contained 61 questions covering screening, demographics, cloud-native computing, containers, Kubernetes, CNCF projects, and six additional topic areas.
The Linux Foundation Research report page identifies the report as the survey’s twelfth iteration, credits Valerie Silverthorne of CNCF, and says that 750 members of the cloud-native community participated. The official report is 39 pages long and was produced by CNCF and Linux Foundation Research.
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The report’s central thesis is more nuanced than “Kubernetes won.” Cloud-native infrastructure is now widely adopted among the people surveyed, but adoption is exposing a new layer of problems: platform ownership, skills, governance, security, observability, documentation, lifecycle management, and the human effort required to make distributed systems usable.
What were the biggest findings?
The following table summarizes the most useful headline results. Each percentage belongs to a specific survey question, so the figures should not be treated as though they all use the same denominator.
| Area | 2024 survey finding | What the finding means |
|---|---|---|
| Containers | 91% use containers in production | Containers are primarily an operational standard rather than an experiment for this respondent population. |
| Kubernetes | 80% use Kubernetes in production | Kubernetes is mainstream among CNCF-connected respondents. |
| Kubernetes pipeline | 93% use, pilot, or evaluate Kubernetes | Most respondents are engaging with Kubernetes, although engagement includes non-production activity. |
| Helm | 75% prefer Helm for Kubernetes application packaging | Helm remains the dominant packaging choice in the report’s comparison. |
| AI/ML on Kubernetes | 48% are not running AI/ML workloads on Kubernetes | Kubernetes-based AI infrastructure is important but not yet universal or mature. |
| Open-source security | 60% check whether a project has an active community | Project health is increasingly treated as part of dependency-risk assessment. |
| Vulnerability scanning | 57% use a tool to search for vulnerable open-source packages | Automated supply-chain checks are common, though self-reported scanning is not proof of security. |
| WebAssembly | About 34% report some deployment experience | WebAssembly remains selective and early-stage rather than a universal application platform. |
| Release automation | 38% automate 80%–100% of releases | A substantial minority has highly automated release operations. |
| CI/CD | 60% use CI/CD for most or all applications | Pipeline adoption is expanding beyond isolated teams or applications. |
All figures in the table come from the official CNCF and Linux Foundation Research report. The report’s percentages describe respondents, not the entire global technology market.
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Cloud-native adoption is becoming broader and deeper among survey respondents. The report says the share of organizations reporting that much of their application development and deployment is cloud native grew by 7.5% year over year, while the “nearly all” category grew by almost 19%. The share identifying as cloud-native beginners fell from 12% to 11.4%.
Those changes support a maturity interpretation: some organizations are moving from isolated or partial cloud-native use toward applying the approach across a larger share of development and deployment. The survey does not establish that every organization follows the same path, nor does it define cloud-native maturity solely by Kubernetes adoption.
Adoption was also broadly distributed by company size. The report indicates that cloud-native use was not limited to the largest enterprises. Europe and the Americas led the geographic comparisons, while Asia-Pacific narrowed its previous gap. Regional and company-size comparisons require more caution than the overall headline because those subsets contain fewer respondents.
Why are containers mainstream but still difficult to operate?
Containers are the survey’s clearest success story. According to the 2024 report, 91% of surveyed organizations use containers in production, and 52% use containers for most or all production applications. The report’s comparison also puts the average number of containers per organization at 2,341 in 2024, up from 1,140 in 2023.
The container figures indicate expansion in both adoption breadth and organizational scale. They do not indicate that containers automatically make software delivery simple. Respondents identified the following container-related challenges:
| Challenge | Share of respondents | Practical implication |
|---|---|---|
| Cultural changes within development teams | 46% | Teams need new ownership, collaboration, and operating practices. |
| CI/CD | 40% | Container deployment requires reliable build, test, promotion, and rollback pipelines. |
| Lack of training | 38% | Infrastructure adoption can outpace staff skills. |
| Security | 37% | Image contents, runtime policy, credentials, and supply-chain provenance need attention. |
| Monitoring | 36% | Container environments need usable metrics, logs, traces, and alert ownership. |
| Complexity | 35% | More abstraction layers can increase operational burden. |
The challenge is sharper for organizations with moderate cloud-native experience. In that group, 55% cited cultural challenges with development teams and 51% cited lack of training. The result suggests that the difficult phase is not always initial experimentation; the difficult phase can be scaling practices beyond a small group of specialists.
Has Kubernetes become standard?
Among this survey’s cloud-native-oriented respondents, Kubernetes has crossed the mainstream adoption threshold: 80% reported production use, up from 66% in 2023, and 93% reported production use, piloting, or active evaluation. The 2024 CNCF Annual Survey therefore describes Kubernetes as widely established in its target community, not as universally deployed by all companies.
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The distinction between those figures matters. “Production use” describes a deployed workload. “Pilot” describes testing or limited implementation. “Active evaluation” describes an intention or assessment that may never become production adoption. Saying that 93% “use Kubernetes” without that qualification would overstate the evidence.
The report also contains a project-specific Kubernetes question with a different result: Kubernetes appears at the top of the graduated CNCF projects, with 85% reporting production use and 9% evaluating it. The 80% and 85% figures should not be averaged or presented as contradictory. They come from different questions, respondent filters, or denominators.
Kubernetes usage can also mean different things. An organization may run a managed Kubernetes service, operate clusters itself, use Kubernetes only for selected applications, deploy at the edge, or use it mainly in development. High adoption does not prove that Kubernetes is appropriate for every application. Long-running, distributed, multi-team workloads may benefit substantially; a small, stable application may not justify the platform’s operational cost.
Which Kubernetes and CNCF tools stand out?
The project-level results show a Kubernetes-centered ecosystem. Kubernetes, Helm, Prometheus, etcd, containerd, CoreDNS, Cert-Manager, and Argo are among the prominent projects listed in the report. These projects cover orchestration, application packaging, monitoring, cluster state, container runtime infrastructure, service discovery, certificate management, and delivery workflows.
Popularity is useful evidence of ecosystem momentum, documentation availability, hiring familiarity, and integration—but it is not a universal suitability ranking. Technology leaders should also assess project maintainership, release cadence, security response, documentation quality, integration with the existing platform, support options, skills availability, and the cost of replacing the project later.
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Helm’s preferred-method share increased from 56% in 2023 to 75% in 2024, according to the report’s year-over-year comparison. The report describes that change as 33.9% annual growth. Helm is a Kubernetes application-packaging and templating tool, so the result indicates strong preference for a familiar way to distribute and parameterize Kubernetes applications.
Helm can make deployments repeatable and easier to distribute across environments. Helm templates can also become difficult to understand, especially when charts contain extensive conditional logic, deeply nested values, or undocumented defaults. Kustomize, managed platform abstractions, Buildpacks, CNAB, and Porter remain alternatives. The right choice depends on whether the team values chart ecosystem compatibility, simpler overlays, higher-level developer workflows, or a different packaging model.
Is AI on Kubernetes mature?
No. AI/ML on Kubernetes remains an early-stage operating model in the 2024 CNCF Annual Survey: 48% of respondents said they were not running AI/ML workloads on Kubernetes. The survey supports the view that Kubernetes is becoming an important AI infrastructure substrate, but it does not support the claim that Kubernetes is already the default platform for enterprise AI.
Among respondents running AI/ML workloads on Kubernetes, the leading reported use cases were:
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|---|---|---|
| Batch jobs for AI/ML pipelines | 11% | Scheduled or queued processing rather than continuously served models. |
| Model experimentation | 10% | Development and testing activity that may not be production operation. |
| Real-time model inference | 10% | Serving predictions with latency requirements. |
| Data preprocessing | 9% | Preparing data before training or inference. |
| Batch model inferencing | 8% | Running predictions over accumulated data. |
Running an inference service on Kubernetes is different from operating a complete machine-learning lifecycle. AI platform decisions also involve GPU availability and scheduling, data pipelines, model governance, experiment tracking, observability, security, cost controls, and model updates. A team should not build a Kubernetes AI platform simply because Kubernetes is popular; the team should first establish workload requirements, accelerator economics, data controls, and operational ownership.
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Are open-source security practices improving?
Self-reported open-source security practices improved across most measures in the survey. According to the 2024 report, 60% checked whether an open-source project had an active community, up from 49%; 57% used a tool to search for vulnerable open-source packages, up from 51%; and 52% checked release and commit frequency, up from 42%.
Other reported practices included examining source code at 55%, unchanged from the report’s 2023 comparison; examining repository ratings or package-download statistics at 37%, up from 29%; and using registry or package-manager information at 33%, up from 27%. The report says 16% used one or more OpenSSF capabilities, compared with 20% in the comparison data shown in the report. Only 3% said they did not check external software security.
These results indicate more attention to dependency risk, but they are not independent audits or compliance certifications. An active community does not guarantee secure code. A vulnerability scanner does not guarantee remediation. Repository popularity does not establish provenance. A practical software-supply-chain program should connect inventory, SBOMs, vulnerability prioritization, signed artifacts, provenance, policy enforcement, patch ownership, and exception management.
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WebAssembly remains promising but selective. Approximately 34% of respondents reported some experience deploying applications with WebAssembly, while the report characterizes mainstream adoption as stalled. Among organizations that had not adopted WebAssembly, 48% cited lack of applicability and 23% cited implementation complexity.
The result does not prove that WebAssembly has failed. WebAssembly can be attractive for particular serverless, cloud, embedded, edge, portability, or performance-sensitive workloads. The business case depends on language support, runtime integration, tooling, security requirements, deployment targets, and whether the workload benefits from WebAssembly’s execution model.
WebAssembly should therefore be evaluated as a workload-specific option rather than as a replacement for containers or Kubernetes across the organization. A pilot with a clear latency, portability, startup-time, isolation, or resource-efficiency objective is more informative than adoption based on industry enthusiasm alone.
What does the survey say about CI/CD, GitOps, and release automation?
CI/CD adoption is expanding: 60% of respondents reported using CI/CD for most or all applications, up from 46% in the report’s year-over-year comparison. The report describes that change as 31% growth. GitHub Actions and Argo were among the tools showing notable growth in the tool comparison.
Release automation also improved. The report says 38% of respondents automated 80%–100% of releases. The organizational average increased from 56.5% automated releases in 2023 to 59.2% in 2024.
The survey associates greater cloud-native maturity with more frequent releases. Among organizations where much or all development and deployment was cloud native, 37% released multiple times a day. Less mature organizations were more likely to release weekly or monthly.
That association is useful, but it is not proof that cloud-native tools caused faster delivery. Organizations that release frequently may already have stronger testing, architecture, product practices, automation, and leadership support. GitOps or CI/CD can standardize delivery, but neither tool installation nor a Kubernetes cluster automatically creates safe releases. Teams still need quality gates, rollback procedures, progressive delivery, secrets management, ownership, and reliable production telemetry.
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Why is organizational execution becoming the main bottleneck?
The 2024 survey’s most important practical message is that cloud-native problems are moving up the stack. Infrastructure adoption is no longer the only challenge; team culture, training, platform ownership, documentation, security, observability, and operational complexity determine whether adoption produces useful outcomes.
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For CNCF projects specifically, 46% cited concern that open-source projects could become inactive, 46% cited complexity, and 45% cited lack of supporting documentation. Those concerns explain why a technically popular project can still be a poor organizational choice. A team may be able to install a tool but lack the staff to upgrade it, secure it, document it, integrate it, and support application teams.
Leaders should treat a platform as an internal product. A platform team needs a defined customer, a supported golden path, upgrade and incident ownership, documentation, security controls, service-level expectations, and a way to measure whether developers can deliver safely with less cognitive load.
What does the survey mean for platform buyers?
The survey is not a vendor recommendation, but its findings point to a concrete buying decision: reduce undifferentiated operational work without pretending that a product eliminates platform responsibility.
| Option | Likely fit | Advantages | Trade-offs to model |
|---|---|---|---|
| Managed Kubernetes | AWS-, Google Cloud-, or another public-cloud-centered organization | Less control-plane maintenance and integration with the selected cloud ecosystem | Worker compute, storage, networking, observability, labor, cloud coupling, and platform fees remain. |
| Enterprise Kubernetes platform | Organizations needing supported hybrid, on-premises, or multi-cloud operations | Commercial support, governance, integrated platform features, and a formal lifecycle | Subscription cost, platform complexity, and vendor-specific operating practices. |
| Platform-as-a-service layer | Teams that want developers to deploy without managing Kubernetes details | Improved developer experience and centralized policy | Abstraction limits, migration considerations, and platform-team ownership. |
| No Kubernetes for a workload | Small, stable, simple, or highly managed applications | Lower operational burden and fewer platform concepts | Less portability or control if requirements later become more complex. |
For a managed Kubernetes comparison, buyers can review Amazon EKS pricing and Google Kubernetes Engine pricing. AWS lists standard Kubernetes version support at $0.10 per cluster-hour and extended support at $0.60 per cluster-hour; worker nodes, storage, networking, public IPv4, and other AWS resources are additional. Google Cloud lists a $0.10 per cluster-hour cluster-management fee and a $74.40 monthly free-tier credit per billing account for eligible zonal and Autopilot cluster charges; compute, storage, networking, and other services remain separate.
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For hybrid or enterprise platform requirements, Red Hat OpenShift pricing advertises cloud-service pricing as little as $0.076 per hour under a particular four-vCPU, three-year contract assumption. That figure is not a general OpenShift total-cost estimate; edition, sizing, subscription, infrastructure, and contract terms matter.
Observability should be modeled separately. Grafana Cloud pricing lists free tiers and a Pro application-observability starting price of $0.025 per host-hour, described as $18 per host, with volume discounts. Enterprise pricing is custom, and usage-based metrics, logs, traces, retention, tests, and platform fees can materially change the bill.
What should buyers include in total-cost calculations?
- Cluster-management or control-plane charges.
- Worker compute, accelerators, storage, backups, networking, and public IP resources.
- Logging, metrics, traces, retention, alerting, and incident-management costs.
- Platform engineering, SRE, security, networking, and upgrade labor.
- Training, documentation, developer enablement, and migration work.
- Commercial support, subscription commitments, contract assumptions, and exit costs.
A free tier or low hourly control-plane price is not a complete operating-cost estimate. The survey’s findings about culture, training, complexity, and documentation are reminders that the labor and organizational costs can matter as much as the infrastructure line item.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should technology leaders use the survey?
Use the survey as a benchmark and a question generator, not as an automatic architecture decision. A practical review should ask:
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- Does the workload fit Kubernetes? Classify the workload as long-running, batch, stateful, event-driven, latency-sensitive, edge, or GPU-intensive before choosing the platform.
- Does the team have the required capability? Account for platform engineering, SRE, security, networking, observability, and application support—not just the people who can create a cluster.
- Should Kubernetes be managed? Compare the value of reduced control-plane work against cloud coupling, data-transfer costs, operational limits, and remaining labor.
- Is portability a real requirement? Multi-cloud or on-premises portability can be valuable, but portability goals should be specific enough to justify their added complexity.
- Can delivery quality be measured? Track deployment frequency alongside change failure rate, recovery time, reliability, security remediation, developer experience, and cost.
- Are software dependencies governable? Check project activity, maintainership, documentation, release cadence, vulnerability response, provenance, and an exit path.
- Are AI workloads ready for production? Establish data governance, GPU capacity, model lifecycle controls, observability, and cost accountability before building a broad AI platform.
- Who owns the platform? Assign responsibility for upgrades, incidents, documentation, security policies, support, and deprecation.
What are the survey’s methodology limitations?
The 2024 CNCF Annual Survey is valuable directional evidence, but the respondent population is connected to the CNCF cloud-native community. The survey is not a census of all technology organizations, a neutral market-share study of every vendor, a controlled experiment, or evidence that cloud-native adoption causes lower costs or faster delivery.
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The report uses question-specific samples rather than 750 respondents for every question. Examples include Q15 cloud-native adoption with 409 valid cases, Q19 data-center machines with 408, Q20 container usage with 408, Q22 container challenges with 373, Q32 CNCF projects with 689, Q34 CNCF project challenges with 552, Q36 dependency-security practices with 689, Q39 WebAssembly deployment with 403, Q50 serverless platforms with 55, and Q56 CI/CD tools with 689.
Many calculations exclude “don’t know” or “not sure” responses, and some questions apply filters that separate end users from vendors, systems integrators, consultants, or other respondent groups. The 2024 report also does not always use identical wording, filters, or respondent definitions to the 2023 survey. Year-over-year figures should therefore be described as the report’s comparisons, not as perfectly controlled longitudinal measurements.
The safest wording is “among respondents,” “the survey reports,” or “the report’s comparison shows.” The unsafe wording is “the industry has,” unless independent evidence supports a broader claim.
What the 2024 CNCF Annual Survey really says
The survey shows a cloud-native ecosystem approaching operational maturity, not an ecosystem that has solved cloud-native operations. Containers and Kubernetes are mainstream within the CNCF community, Helm and familiar CNCF projects remain central, CI/CD and release automation are advancing, and security practices are receiving more structured attention.
At the same time, AI/ML on Kubernetes is not yet universal, WebAssembly remains selective, and the hardest problems increasingly concern people and systems: training, team culture, documentation, support, observability, security, complexity, cost, and ownership. The most defensible conclusion is that technology leaders should select Kubernetes and adjacent tools because they fit a measurable workload and operating model—not because an adoption statistic makes them mandatory.
Frequently Asked Questions
When was the 2024 CNCF Annual Survey published?
The 2024 CNCF Annual Survey was published on April 1, 2025. The formal report title is Cloud Native 2024: Approaching a Decade of Code, Cloud, and Change, and the survey responses were collected in fall 2024.
Does the survey prove that 93% of organizations use Kubernetes in production?
No. The survey reports that 93% of respondents used, piloted, or actively evaluated Kubernetes. Production use alone was reported at 80% in the broad Kubernetes question, and a separate project-specific question reported 85% production use with a different denominator or filter.
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No. Kubernetes is widely adopted among CNCF-connected respondents, but the survey does not prove universal suitability. Workload fit, team capability, operational cost, portability requirements, security maturity, and platform ownership should determine whether an organization uses managed Kubernetes, self-managed Kubernetes, another platform, or no Kubernetes for a particular application.
How mature is AI/ML on Kubernetes according to the survey?
AI/ML on Kubernetes remains early-stage in the survey: 48% of respondents said they were not running AI/ML workloads on Kubernetes. Reported workloads included batch pipeline jobs, model experimentation, real-time inference, data preprocessing, and batch model inferencing.
Are the survey’s security results independent audits?
No. The security results are self-reported practices. Checking project activity or using vulnerability-scanning tools can improve software-supply-chain risk management, but those actions alone do not prove that dependencies are secure or that an organization meets a compliance standard.
The Bottom Line
Bottom line: The 2024 CNCF Annual Survey shows that cloud-native adoption has entered the mainstream, especially for containers and Kubernetes, while operational maturity is still uneven. Treat the percentages as evidence of direction within the CNCF community, pair every adoption statistic with its denominator and caveat, and make platform decisions based on workload fit, team capability, security, cost, and ownership.
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