The State of SRE and Platform Engineering

Site reliability engineering and platform engineering are now widely implemented and essential to modern cloud operations. Adoption is no longer the question. The challenge now is scaling, integration, and managing complexity during the rapid growth of AI within cloud applications.

Site reliability engineering (SRE) is a software operations discipline that provides IT dependability, monitoring, and incident response, while platform engineering is a practice that produces the internal platforms and tooling for building resilient software. Organizations may perform one or both functions using names like IT operations (ITOps), DevOps engineering, systems administration, or developer experience (DevEx) engineering.

Both disciplines centralize and standardize toolsets for reliability, security, and efficiency. Observability establishes a critical context layer for both, providing common intelligence that unites all objectives.

While SRE emerged in 2003 and platform engineering more recently in 2018, both have reached enterprise-wide adoption and sophistication. But organizations face new challenges as large language models (LLMs), coding assistants, and agentic AI transform how organizations develop and deliver services.

The AI Challenge

Cloud environments have become a critical landing zone for AI applications, from LLM-powered services to agentic workflows built on platforms like Amazon Bedrock, Azure AI Foundry, and Gemini Enterprise Agent Platform. These workloads fail in different ways than traditional software, creating new requirements for the teams responsible for running them.

As AI workloads move from pilot to production, SRE and platform engineering teams face two distinct challenges:

1. Ensuring the AI running in production behaves as expected
2. Using AI to drive automation that manages these dynamic workloads reliably

The Opportunity for Teams

Based on a global survey of 919 global SRE and platform engineering leaders, this report answers:

• How adopting AI is affecting SRE and platform engineering practices and what's holding teams back
• Where SREs and platform engineers are succeeding—and struggling— with automation and scale
• How observability provides a crucial intelligence layer that integrates security, compliance, and resource optimization with efficiency

Observability is the foundation for both. To support this next phase, it must evolve beyond simple monitoring into intelligence that becomes a governance control plane, supplying clear causal context for every operational decision.

Teams are adopting AI technologies to drive proactive responses and autonomous operations, but they're proceeding with deliberation. Both SREs and platform engineers are intentionally prioritizing AI visibility and human oversight before using it to automate. This supervision is a feature of evolving maturity, not a shortcoming.

Key Insights

• AI model monitoring leads SRE priorities: Monitoring AI models for performance and accuracy is SREs' top use case, reflecting how quickly AI workloads have become part of the production environment SRE teams are responsible for.
• Observability anchors AI adoption: Both SREs and platform engineers prioritize AI/machine learning capabilities within observability or AIOps platforms.

The fact that monitoring AI models now leads all SRE priorities reflects something fundamental: AI applications have moved from experimental to mission critical. In cloud environments, these workloads—inference services, agent orchestration layers, LLM APIs—are now part of the production stack that SRE teams own. They behave differently from traditional software: they are probabilistic, they drift, and they can fail silently.

AI adoption by SREs

Because SRE aims to automate reliability at scale, the integrity of AI output for automation is critical. So it’s not surprising that SREs’ top use of AI capabilities (58%) is monitoring AI systems for model performance, accuracy, resilience, and data security, reflecting how central AI workloads have become to the production environments they own.

AI adoption by platform engineers

Platform engineers show similar priorities, using AI most predominantly (55%) to provide developers access to AI‑powered tools, such as chatbots and copilots, alongside monitoring AI tools to ensure data security, model performance, and accuracy.

AI toolchains are closely aligned for SRE and platform engineering

The AI technologies used by both SREs and platform engineers are closely aligned, led by AI/machine-learning (ML) features in observability or AIOps platforms, followed by coding assistants and LLMs.

This emphasis on observability underscores the organizations’ focus on overseeing the accuracy and reliability of AI outputs and enabling teams to apply AI effectively.

AI generally meets expectations, but leaves room for improvement

AI mostly meets expectations for reliability and developer productivity, but is falling short on cost reduction and faster incident detection and resolution.

The gap points to limited workflow automation and weak system-level orchestration for more complex outcomes. Closing this gap requires that teams have greater visibility into and control over AI workloads as teams advance their projects from pilot to production.

To advance proactive and autonomous operations, the benefits—and demands—of AI require that teams shift from reactive monitoring to predictive, automated operations driven by observability that unites AI with other context signals to promote reliability, faster incident resolution, and better developer experiences.

AI Workloads Are Now Production Infrastructure

Agentic AI and cloud-native systems are now mission-critical software. But rising complexity, new telemetry, and novel ways of failing are placing new demands on observability.

• Design for resilience first. Treat reliability, security, and observability as a compound requirement to withstand growing system complexity across both traditional and AI-powered workloads.
• Treat model drift as an operational risk. AI models can degrade silently, producing outputs that appear valid, but are increasingly inaccurate. Continuously monitor model behavior in production, not just deployment-time validation, to catch drift before it impacts users or business outcomes.
• Keep humans in the loop. Pair AI powered operations with oversight and guardrails to promote trustworthy, explainable outcomes, especially as agentic systems take on more autonomous decision-making.
  

AI is now central to both SRE and platform engineering. But for SREs, it’s also straining the limits of SLOs and quality gates.

SRE practices are widespread and mature, but the probabilistic nature of AI outputs are straining systems already grappling with too many metrics and data sources. The challenge for SREs is to reduce MTTR and operational effort; service-level objectives (SLOs) and quality gates must adapt to AI's new demands.

Key Insights

• Monitoring AI models is SRE's #1 use case (67%) ahead of automation, service-level objectives, and quality gates.
• Executive leadership support drives wide adoption for nearly all (92%) SRE respondents.
• Too many data sources, metrics, and inadequate monitoring tools head the list of SRE challenges.

SREs now monitor AI models ahead of other practices

Since its emergence in 2003, SRE has focused on operational performance. Today, monitoring AI models has become the top priority, surpassing traditional SRE concerns and underscoring the need for reliable AI outcomes.

This shift is justified. Unlike deterministic services, AI systems behave probabilistically and can produce unexpected results. SREs must monitor outputs for accuracy and hallucinations alongside traditional reliability metrics to enable safe automation at scale.

Strong leadership support drives broad SRE adoption

Site reliability engineering has strong leadership support, with 92% of executive leadership backing it to some degree and 55% expressing very strong support. When management owns the goals of SRE, organizations show higher levels of operational maturity and strong DevOps fundamentals that promote adaptability and scale.

Nearly all organizations also report realizing the benefits they expected from SRE.

SRE operations are gaining maturity

Reflecting this maturity, 64% say that most or all ITOPs processes are fully automated.

AI workloads expose the gaps in quality gates

Quality gates are automated checkpoints within a CI/CD pipeline that evaluate code against specific, predefined criteria. Respondents use quality gates widely, mainly to validate automated test thresholds, performance metrics, and SLO compliance. More than half of these use cases are standardized across organizations, and most are fully or mostly automated.

However, because AI applications are intrinsically probabilistic, teams can no longer rely solely on quality gates for testing. Real-time monitoring of AI system behavior bridges the gap, with metrics such as model drift serving as the new triggers for alerts, rapid diagnosis, and automated action.

The value of SLOs is undisputed

Service‑level objectives are specific, measurable targets set for software performance and reliability. SLOs are central to SRE, with 89% adoption on at least some teams and more than half reporting broad organizational use. Leading SLO use cases include supporting SLAs and customer commitments, driving reliability goals, and validating overall service quality.

Structured frameworks dominate service level evaluations

When assessing service levels, 92% of organizations use advanced evaluation methods, including service‑level indicators, OKRs, and KPIs. Nearly half also apply DevOps Research and Assessment (DORA) metrics and AI/machine learning‑based tools to support measurement and analysis.

These structured frameworks promote consistency, repeatability, and automation, and provide important benchmarks for observability analysis.

Too many data sources and metrics threaten SLO success

The main challenge for SLOs is managing numerous data sources and metrics, a challenge that’s only worsening with the explosion of telemetry from AI workloads.

Telemetry volume is not the only problem. SLOs must now account for probabilistic outputs—accuracy, hallucination, latency, and cost—forcing teams to define reliability for systems that don't behave deterministically, without established patterns or tooling.

These difficulties highlight the importance of elevating observability from a supporting tool to the shared system of record for defining, automating, and validating SLOs.

Optimizing and automating SLOs and quality gates for complex, high-volume AI workloads requires unified observability that applies AI-driven intelligence to automatically pinpoint root causes and trigger action, enabling auto-remediation, auto-prevention, and auto-optimization within guardrails teams define.

AI workloads strain SLOs and quality gates for SREs. For platform engineers, the challenge is IDP integration and scale for AI’s special needs.

Focus SRE with intelligent SLOs and quality gates

Reliable SLOs and quality gates depend on integrating observability data with context awareness across AI, cloud, test, and production.

• Streamline SLO and quality gate design starting with the four golden signals—latency, traffic, errors, and saturation—then extending with user, service, or workload-centric SLOs to reflect real user experience and system health.
• Use guidance and templates from a trusted observability intelligence solution to select meaningful SLIs and avoid relying on uptime alone.
• Automate continuous evaluation of SLOs and quality gates, including AI-specific signals such as model accuracy, output consistency, and inference cost, to support safer, data-driven release decisions and faster root-cause detection while avoiding "death by dashboard”.

Platform engineering teams are navigating a new requirement: building internal development platforms (IDPs) that support AI coding tools and application development. But even as AI tools ramp up demands, teams are struggling to integrate existing systems and scale these platforms across the organization.

Key insights

• IDPs are widely established: 89% of organizations with platform engineering have an IDP, and 60% report broad adoption across teams.
• Observability leads self‑service: Monitoring and observability dashboards are the most common self‑service capability.
• Integration remains the bottleneck: Platform engineers cite tool integration, standards enforcement, and security or compliance complexity as their top challenges.

60% have deployed an IDP broadly across teams

74% Currently provide self-service access to observability/monitoring dashboards

37% have integration challenges with existing tools or systems

Development at scale craves structure and consistency

Since it emerged in 2018, platform engineering has evolved rapidly. Among organizations practicing it, 89% have implemented an internal developer platform (IDP), with 60% reporting broad adoption across departments.

The leading platform engineering use case is providing teams with observability and monitoring dashboards (74%), underscoring the central role of real‑time operational data in platform initiatives. The beneficiary of this structure is repeatability: at least three‑quarters of these capabilities are automated.

Platform engineering practices are mostly meeting expectations, but leave some shortcomings

Expectations of platform engineering practices largely align with observed benefits, signaling overall maturity. Increased automation and reduced toil, and deployment reliability are top priorities, yet show lower realized impact, indicating a need for greater orchestration and intelligence.

Templates provide predictable and repeatable app deployment—and maintenance is mandatory

Organizations with an IDP rely on standardized templates and reusable assets—most often for CI/CD and applications—to ensure predictable deployments. Teams typically manage these components through structured, mandatory platform controls rather than ad hoc, team‑by‑team maintenance.

Integration and scale present a next-level challenge as platform engineering evolves beyond initial adoption

Platform teams face their greatest pain points integrating existing systems, maintaining standards, and managing security and compliance.

AI tools exacerbate these challenges because they behave fundamentally differently from traditional application stacks and require their own provisioning and governance methods.

Fewer respondents cited issues with self‑service reliability, tool adoption, and production debugging, signaling progress on early-stage challenges and a shift toward more complex later-stage issues.

Unified observability gives platform engineers a shared intelligence layer that applies AI-driven analysis to integrate systems, enforce standards, automate workflows, and prove business value at scale.

As platform engineering advances developer self-service, both SRE and platform engineers come together on centralized governance for security, compliance, and resource optimization.

Progress integration and scale with observability‑driven platform engineering

An effective IDP provides templates, containerized delivery, infrastructure as code, embedded security and compliance, and observability as a foundational enabler of reliable automation for conventional and AI workloads.

• Create standard golden-path templates that embed telemetry, SLOs, and security controls by default to reduce cognitive load and enable fast feedback.
• Embed observability across cloud-native services and pipelines to improve reliability, governance, and developer experience while supporting self‑service at scale.
• Further integrate observability into AI coding tools for model accuracy, inference latency, cost per request, and behavioral consistency
  

The fundamentals of SRE and platform engineering are strong, creating a solid reliability framework for AI monitoring and optimization. Future direction for both disciplines builds on DevOps maturity that accommodates AI workloads: security and compliance are built in, and resource optimization is a key metric.

Key insights

• SRE and platform engineering are tightly linked, and most organizations with SRE programs also have platform engineering programs that collaborate and share responsibilities.
• Security and compliance are built in for two thirds of SRE and platform engineering teams, and around 60% build in compliance.
• Observability is widespread but not yet fully embedded across workflows and systems, which could slow or limit AI projects.
  

92% Of organizations with SRE programs also have or are initiating platform engineering programs. 82% of organizations with platform engineering programs also have or are initiating SRE programs

73% Of SRE and platform engineering teams collaborate and share responsibilities

Nearly 70% Of SRE and platform engineering teams embed security into their platforms as code

60+% Of SRE and platform engineering teams build regulatory compliance into infrastructure and CI/CD workflows

The future for SRE and platform engineering is optimization and automation

Optimization is the next horizon for site reliability and platform engineering, and both disciplines show strong strategic and directional alignment. While optimizing cloud and AI token usage, incident response, and infrastructure costs rise to the top for SREs, all use cases carry similar importance.

Platform engineers place greater emphasis on reducing operational toil using AI and expanding observability capabilities. All these areas will benefit from AI optimization, as teams seek to expand automation into more complicated use cases.

Security policies and compliance automation are widely aligned and embedded

SREs and platform engineers observe very similar advanced strategies to integrating and embedding security policies as code into all stages of development, deployment, and operations. This integration indicates a broader shift toward treating security as a platform-level capability and creates the accountability framework teams need to manage AI risk.

As AI workloads become mission-critical, governance requirements expand beyond application security and compliance. AI systems introduce new requirements like explainability, model versioning, and output auditing that require SRE and platform engineering teams to extend their governance frameworks into new territory. The organizations that have embedded security and compliance as code are best positioned to extend those same practices to AI workload governance.

Compliance automation is likewise broadly built-in and automated, with platform engineers integrating and automating at slightly higher rates than SREs. These high levels of integration reinforce the advantage of centralizing governance for common requirements and building guardrails for explainable AI.

Resource optimization concerns focus on energy usage and sustainable design

SREs and Platform engineers both prioritize resource optimization and cost management in similar proportions with a goal of reducing carbon emissions and optimizing costs and AI token usage. These priorities will become increasingly important as energy demands are set to soar with increasing AI adoption across industries.

Centralized governance through unified observability—with AI-driven intelligence enforcing standards automatically—facilitates automation of application security, compliance, and resource efficiency for SRE and platform engineering teams, turning shared standards into consistent, scalable control.

Centralized governance starts with unified observability, but neither discipline is fully leveraging the AI-driven intelligence it makes possible.

Security, compliance, and resource optimization begin with building in capabilities as‑code

Adopting machine-readable definition files to build in key capabilities using observability intelligence promotes and scales SRE and platform engineering priorities, such as optimizing cloud infrastructure and AI token usage, compliance requirements, and security vulnerability management.

• Configuration‑as‑code versions and automates observability and platform settings across environments with full auditability.
• Observability‑as‑code (also referred to as monitoring-as-code) embeds telemetry and SLOs into golden paths for consistent visibility, clear ownership, and faster feedback across environments.
• Infrastructure‑as‑code automates infrastructure provisioning using templates for specific requirements, such as architecture, function, and traffic, to ensure consistency, scalability, and safer deployments.
  

SRE and platform engineering teams recognize the value of observability and have adopted it widely, but it remains underutilized. As more AI workloads come online and teams seek to optimize existing SRE and platform engineering practices, observability intelligence will become a crucial differentiator.

Key insights

• Observability is implemented broadly, but not deeply with about three‑quarters of platform engineers embedding observability into at least some services.
• Deployment is the top focal point for observability where situational awareness is critical, although teams use it at every phase of the software delivery life cycle (SDLC).
• A majority of SREs have adopted observability tools with 65% using one or more commercial or open‑source observability solution
  

40% Of platform engineers embed observability into all deployments

65% Of platform engineers use observability in the deployment phase of the software delivery lifecycle

97% Of SREs use observability, either for minimal logging/monitoring or full observability tools

Observability is vital for critical platform engineering functions

AI workloads in production make observability intelligence urgent. For conventional services, observability shows when systems degrade or fail. For AI, it must also detect model drift, unexpected agent behavior, and rising inference costs—before users feel the impact.

Platform engineering teams use observability across the software delivery lifecycle, but adoption is uneven. While 77% embed observability in at least some services, only 40% have it fully integrated across all deployments.

Teams rely most on observability during deployment, when they need immediate visibility into performance and risk as software reaches production. More broadly, it provides shared context across the SDLC, supporting insight, optimization, and automation.

Observability is widely adopted in SRE practices, but not yet fully embedded

Nearly all SRE teams use observability in some measure, either minimally as part of a logging and monitoring program, or more widely, using commercial or open-source observability tools.

As teams mature and integrate more AI workloads, the ability to apply AI-driven intelligence to what observability sees will become essential for scaling and governance. Observability must evolve into a control plane—a unified, AI-driven intelligence layer that grounds every service and LLM action in clear causal context—enabling trusted, agentic operations across SRE and platform engineering.

Observability is the critical intelligence layer for effective SRE and platform engineering—and provides the catalyst for detecting, diagnosing, and responding to the unique behaviors of AI systems.

Observability as the control plane for SRE and PE

Observability is becoming a shared control plane for SRE and platform engineering AI adoption and automation—turning insight into governed action.

• Use observability as a unified, AI-driven intelligence layer to correlate telemetry, topology, and business context. Include AI-specific signals such as model accuracy, inference latency, and output consistency for trusted, automated decisions across conventional and AI-driven services.
• Feed high‑fidelity observability data into remediation workflows to enable safe, explainable automation.
• Embed observability into platform pipelines to validate releases, enforce reliability standards, and continuously optimize at scale.
  

SRE and platform engineering are maturing modern cloud operations, but AI is ushering in a new era of innovation. The challenge now is oversight, control, and scale. Observability is the catalyst.

• Cloud-hosted AI workloads are redefining what SRE and platform engineering teams are responsible for. As AI applications become mission-critical, the teams that operate them will need observability that spans traditional reliability signals and AI-specific behavior like accuracy, cost, and accountability, in a unified, automated platform.
• SRE and platform engineering are reaching operational maturity. Adoption is no longer the defining challenge. Instead, organizations are contending with scale, system integration, and growing decision complexity as environments become more distributed, automated, and AI‑driven.
• Observability must evolve into connective infrastructure. No longer just an operational capability, observability is increasingly the AI-driven control plane that links SRE, platform engineering, security, compliance, and AI workloads—providing shared context, trust, and coordination across domains.
• Teams are prioritizing visibility and control before autonomous operations. The deliberate focus on AI monitoring, transparency, and human oversight reflects maturity, not hesitation. Organizations are putting foundational controls in place before advancing toward autonomous operations. This is especially true for AI workloads in production, where the stakes of premature automation are highest.
  

The next phase is orchestration and scale. Success will come from connecting observability, automation, and AI into a trusted system that helps humans make faster, better‑informed decisions with confidence.

This report is based on a global survey of 919 senior leaders, decision makers, managers, and supervisors directly involved in or responsible for site reliability engineering, platform engineering, or IT operations in large enterprises with annual revenues of $500 million or more. It was conducted and analyzed by Qualtrics partner Y2 on behalf of Dynatrace during October 2025 to January 2026.

Industry verticals: Software, Consulting – Technology, Manufacturing, Retail and Wholesale, Banking and Finance, Logistics and Transportation, Telecommunications, among others.

Respondent roles: C-suite leaders, mid-and high-level executives, managers, and supervisors responsible for IT operations, IT security management, software/hardware procurement, software quality assurance, platform engineering, site reliability engineering, regulation compliance, product management, supply chain and logistics, and sales.

Respondent countries: United States (200), Brazil (35), Mexico (50), United Kingdom (100), France (50), Germany (100), Italy (50), Austria (30), Netherlands (27), UAE (29), Saudi Arabia (18), Qatar (17), Kuwait (17), Australia (26), Japan (100), India (70).

All findings were analyzed to uncover success patterns, competitive advantages, and enterprise strategies for site reliability engineering and platform engineering practices across global markets.

The margin of error is ±3.2% at a 95% confidence level.

The Americas

US (200), Brazil (35), Mexico (50)

• Organizations in the Americas lead the other regions in the use of AI technologies.
• With SRE programs, organizations based in the Americas observe faster incident detection and response in operations (57%) than their counterparts in other regions (APAC 49%, EMEA 43%). They also experience improved service uptime and availability at a greater rate (54%) than organizations in APAC (48%) and EMEA (45%).
• Organizations in the Americas experience similar levels of SRE maturity (65%) as their global counterparts (APAC 63%, EMEA 61%), reporting that most or all ITOps, SLOs, and quality/security gates are automated.
• For SRE automation, organizations in the Americas are more likely to focus on building regulatory compliance into infrastructure (67%) than their global counterparts.
• Platform engineering respondents in the Americas deploy applications via pipelines at a greater rate (73%) than respondents in other regions (APAC 68%, EMEA 52%). They also use infrastructure provisioning at the same rate as EMEA respondents (73%), a greater rate than APAC respondents (65%).
• Platform engineering respondents in the Americas observe the greatest benefits in improved efficiency in provisioning infrastructure.
• When automating regulatory compliance for platform engineering, respondents in the Americas are more likely to build compliance requirements built into infrastructure (72%) than respondents in both EMEA and APAC (65%).
• The greatest barrier to platform engineering among respondents in the Americas is maintaining standardization across teams (46%) than respondents in both EMEA and APAC (26%).
• Platform engineering respondents in the Americas use observability more in the development stage (66%) than APAC (59%) and EMEA (55%).

Europe, Middle East, Africa (EMEA)

UK (100), France (50), Germany (100), Italy (50), Austria (30), Netherlands (27), UAE (46) Saudi Arabia (35)

• Organizations in EMEA experience similar levels of SRE maturity (61%) as their global counterparts (Americas 65%, APAC 63%), reporting that most or all ITOps, SLOs, and quality/security gates are automated.
• 73% of platform engineering respondents in EMEA use infrastructure provisioning, matching respondents in the Americas (73%), contrasting with APAC respondents (65%).
• Platform engineering respondents in EMEA observe the greatest benefits in reduced operational complexity.
• Platform engineers in EMEA and APAC experience less difficulty maintaining standardization across teams or environments (26%) than respondents in the Americas (46%).

Asia-Pacific (APAC)

Australia (30), Japan (100), India (70)

• Organizations in APAC use autonomous agents more commonly (58%) than organizations in EMEA and the Americas (51% each).
• With SRE programs, organizations based in APAC are more likely to observe increased automation and reduced manual toil (54%) than their counterparts in other regions (Americas 47%, EMEA 45%).
• Organizations in APAC experience similar levels of SRE maturity (63%) as their global counterparts (Americas 65%, EMEA 61%), reporting that most or all ITOps, SLOs, and quality/security gates are automated.
• For SRE automation, APAC-based companies place more emphasis on security policies being embedded in platforms-as-code (74%) than their global counterparts. Likewise, organizations in APAC focus more on integrating security fully into all stages of development, deployment, and operations (68%).
• Regarding challenges to SLOs, organizations based in APAC report greater challenges with having too many data sources (55%), too many metrics (50%), and not knowing what makes a good SLO than other regions (21%). However, APAC-based organizations are less likely to have difficulty knowing what metrics to track than their global counterparts (11%).
• Platform engineering respondents in APAC observe the greatest benefits in improved cloud costs and carbon footprint considerations.
• When automating regulatory compliance for platform engineering, APAC-based organizations are much more likely to automate just some compliance tasks (74%) than Platform engineers in the Americas (65%), and EMEA (57%).
• The greatest barrier to platform engineering among respondents in APAC is resource or headcount constraints (37%) than respondents in EMEA (24%) and the Americas (26%).
• Platform engineering respondents in APAC use observability more in the testing and staging stage (71%) than respondents in the Americas (61%), and EMEA (56%).
  

• Lower revenue orgs ($500M-$999M USD) are more likely to use AI features in observability (67%), while higher revenue orgs ($10B USD or more) more commonly use LLMs (58%), autonomous agents (57%), and GPTs (56%).
• For SRE maturity, large-revenue organizations ($10B or more annually) are more likely to report having all IT operations processes automated (32%) than their lower-revenue counterparts (16% for $1B-$9.9B and 18% for $500M-$999M). Correspondingly, organizations with fewer than 500 employees are more likely to have fully automated all IT processes (37%) than organizations with 501 or more.
• For executive leadership support of SRE practices, high-revenue organizations ($10B or more annually) see higher support (65% very strong support) from their leaders than lower revenue organizations.
• For SRE quality gates, organizations with both lower employee numbers and lower annual revenue report lower utilization of most quality-gate use cases. However, these smaller companies are also more likely to have standardized, organization-wide quality gates.
• For SRE testing practices, end-to-end testing is more commonly utilized by organizations with both larger revenue and more employees, suggesting that larger companies tend to have more mature testing practices.
• For platform engineering security, smaller revenue organizations ($500M-$999M USD) are more likely to implement ad-hoc security checks, while organizations with annual revenue over $1B are more likely to use automated scanning in CI/CD pipelines.
• The greatest barrier to platform engineering for large-revenue organizations ($10B or more) is insufficient documentation (36%) vs 20% for medium-revenue organizations ($1B-9.9B) and 24% for small-revenue organizations ($500M-$999M).
• Among platform engineering respondents, the higher annual revenue an organization has the more likely they seem to be to use observability in the post-deployment/production stage of the SDLC.
  

As AI workloads become production infrastructure, SREs and platform engineers are accountable for a new class of mission-critical systems. The solution requires unifying observability into a control plane for trusted automation, software delivery, and operations.

• Discover how Dynatrace, the observability platform powered by AI and built for AI, helps SREs and platform engineers scale reliability, embed oversight, and turn insight into automatable action.
• Experience how Dynatrace works in practice—Use the Dynatrace Playground to experience how observability powers reliability, automation, and control at scale for your SRE and platform engineering use cases.
  

To experience the Dynatrace difference, visit www.dynatrace.com/platform for assets, resources, and a free 15-day trial.