DevOps as a Service (DaaS) is a delivery model where an external provider manages the end-to-end software delivery lifecycle for an organization. Instead of building and maintaining an in-house DevOps team, you partner with a specialized firm that provides the necessary toolchains, automated workflows, and engineering expertise as a managed service.

This partnership is designed to implement and operate a robust, automated workflow that bridges the gap between software development and IT operations, accelerating the entire delivery process from code commit to production deployment.

What Is DevOps as a Service, Technically?

From a technical standpoint, DevOps as a Service is the operationalization of DevOps principles through a third-party managed platform. It's an abstraction layer that allows your development teams to focus on writing application code without being burdened by the underlying complexities of CI/CD, infrastructure provisioning, and production monitoring.

A DaaS provider delivers a pre-configured, yet customizable, platform that integrates the disparate tools required for modern software delivery. They take ownership of the entire toolchain, ensuring that processes like continuous integration, delivery, infrastructure automation, and observability function as a cohesive system. This model closes the gap between development and operations by providing a single, managed service that handles the operational heavy lifting.

Core Functions of a DaaS Provider

A DaaS engagement is not merely about providing access to tools; it's about taking ownership of specific operational outcomes. The provider becomes an extension of your engineering team, responsible for the health, efficiency, and security of your software delivery pipeline.

Key technical responsibilities include:

• CI/CD Pipeline Implementation and Management: Building, managing, and optimizing declarative CI/CD pipelines using YAML-based configurations in tools like GitLab CI, GitHub Actions, or Jenkins (via Jenkinsfile). This ensures that every code merge automatically triggers a build, runs a battery of tests (unit, integration, E2E), and executes a deployment strategy (e.g., blue-green, canary).
• Infrastructure as Code (IaC) Automation: Utilizing declarative IaC tools like Terraform or CloudFormation to define, provision, and manage cloud infrastructure. This ensures that every environment—from development to production—is reproducible, version-controlled in Git, and auditable, eliminating configuration drift.
• Observability Stack Implementation: Deploying and managing a full observability stack (e.g., Prometheus for metrics, Fluentd/Loki for logs, Jaeger for traces) to provide deep, real-time visibility into application performance and system health. This allows for proactive issue detection and rapid root cause analysis.
• Integrated Security (DevSecOps): Embedding automated security controls directly into the CI/CD pipeline. This includes Static Application Security Testing (SAST), Software Composition Analysis (SCA) for dependency scanning, and container image vulnerability scanning at build time.

By encapsulating these complex functions into a single managed service, DevOps as a Service acts as a force multiplier for engineering teams, removing operational bottlenecks and allowing developers to focus on feature velocity and innovation.

Why DaaS Is a Technical Imperative

Adopting mature DevOps practices has become a key indicator of an organization's technical capability. As software systems become more complex and distributed, the demand for specialized operational expertise has grown exponentially. Market projections show that by 2025, 80-81% of companies globally will have integrated DevOps practices.

The technical outcomes are significant. Studies show that 99% of organizations adopting DevOps report positive results, with 61% observing a measurable improvement in software quality and stability. DevOps is no longer a "nice-to-have" but a competitive necessity for building and shipping reliable software at scale.

To understand the skillset a DaaS provider embodies, review this DevOps Engineer job application template. The DaaS model provides access to this level of talent without the significant overhead and challenges of recruiting and retaining a specialized in-house team.

The Technical Stack Powering a DaaS Solution

To fully grasp what DevOps as a Service delivers, it's essential to analyze its underlying technical architecture. A DaaS solution is not an arbitrary collection of software; it is a tightly integrated system of tools and automated workflows designed to manage the entire software lifecycle, from code commit to production monitoring.

This integrated stack is what translates DevOps theory into operational reality. Let's perform a technical teardown of the four primary components that constitute a robust DaaS solution and examine how they deliver concrete engineering value.

The table below provides a structured overview of a typical DaaS technical stack, mapping each component to its function, enabling technologies, and the engineering value it generates.

Core Components of a DaaS Solution

Component	Core Function	Key Technologies	Technical Value
Managed CI/CD	Automates the build, test, and release process for every code change.	Jenkins, GitLab CI, GitHub Actions, CircleCI	Reduces deployment lead time, eliminates manual release errors, ensures consistent deployment artifacts.
IaC Management	Defines and provisions all infrastructure (servers, databases, networks) using declarative code.	Terraform, AWS CloudFormation, Pulumi, Ansible	Guarantees environment parity (dev/staging/prod), enables automated disaster recovery, makes infrastructure changes auditable via Git.
Observability	Collects and correlates metrics, logs, and traces to monitor system health and performance.	Prometheus, ELK Stack (or EFK), Grafana, Jaeger, OpenTelemetry	Enables proactive anomaly detection, reduces Mean Time to Detection (MTTD) and Mean Time to Resolution (MTTR), provides deep performance insights.
Integrated DevSecOps	Embeds automated security gates and vulnerability scanning directly into the CI/CD pipeline.	SAST (SonarQube, Snyk Code), SCA (Snyk, Dependabot), Container Scanning (Trivy, Clair)	"Shifts security left" to find vulnerabilities early, reduces security risk exposure, automates compliance checks.

It's the tight integration of these components that creates a powerful, automated software delivery platform. They are not isolated services but interconnected systems working in concert.

Managed CI/CD Pipelines

The core of any DaaS platform is the managed Continuous Integration and Continuous Delivery (CI/CD) pipeline. This is the automated assembly line that transforms source code into a deployable artifact. When a developer merges code into a main branch, a webhook triggers the pipeline, initiating a sequence of automated jobs.

A typical pipeline sequence includes:

1. Build: Compiling the code and packaging it into an executable or a container image (e.g., Docker).
2. Test: Running automated test suites (unit, integration, and end-to-end tests) to validate code quality and functionality.
3. Deploy: Pushing the artifact to a container registry and deploying it to various environments (staging, production) using strategies like canary or blue-green deployments.

Providers leverage powerful, declarative tools like GitLab CI or GitHub Actions to define these workflows as code (.gitlab-ci.yml, .github/workflows/main.yml), ensuring the entire process is version-controlled and transparent.

Infrastructure as Code (IaC) Management

Modern applications require dynamic, elastic infrastructure that is impossible to manage manually. Infrastructure as Code (IaC) is the practice of managing and provisioning infrastructure through machine-readable definition files. DaaS providers use tools like Terraform or AWS CloudFormation to define all cloud resources—from VPCs and subnets to Kubernetes clusters and databases—in declarative code.

An engineer simply writes configuration files specifying the desired state of the infrastructure. Running terraform apply translates this desired state into reality by making the necessary API calls to the cloud provider. This approach delivers:

• Reproducibility: Spin up identical development, staging, and production environments with a single command.
• Version Control: Track every infrastructure change through Git, enabling rollbacks and audits.
• Automation: Eliminate manual configuration, which is slow and prone to human error.

This methodology effectively solves the "it works on my machine" problem by ensuring perfect parity between all environments. A great technical deep-dive on managing infrastructure with Terraform, ECS, and AWS demonstrates the power of this approach.

Automated Observability and Monitoring

You cannot operate a system reliably without deep visibility into its internal state. A comprehensive observability and monitoring system is a critical pillar of DaaS. This goes beyond simple uptime checks to provide detailed, real-time insights into system performance and application health.

A typical DaaS observability stack includes:

• Metrics: A time-series database like Prometheus scrapes metrics from applications and infrastructure (CPU, memory, request latency).
• Logs: A log aggregation system like the ELK Stack (Elasticsearch, Logstash, Kibana) or Loki centralizes logs from all services, making them searchable for troubleshooting.
• Traces: A distributed tracing tool like Jaeger or OpenTelemetry follows a single request as it travels through multiple microservices, identifying performance bottlenecks.

This data is visualized in dashboards (typically using Grafana), providing a single pane of glass for monitoring the entire system's health.

Integrated DevSecOps

Modern DevOps integrates security as a continuous, automated process throughout the software lifecycle, a practice known as DevSecOps. Security is "shifted left," meaning it is addressed early in the development process rather than being a final gate before release.

A DaaS provider automates several critical security checks directly within the CI/CD pipeline:

1. Static Application Security Testing (SAST): Tools like SonarQube scan source code for common security vulnerabilities (e.g., SQL injection, XSS) on every commit.
2. Software Composition Analysis (SCA): Tools like Snyk scan open-source dependencies for known vulnerabilities (CVEs).
3. Container Scanning: Tools like Trivy or Clair inspect container images for OS-level vulnerabilities before they are pushed to a registry.

By automating these checks, DevSecOps ensures security is a shared responsibility, not a bottleneck, hardening the application without slowing down development velocity. Explore the DevOps services offered by OpsMoon for a real-world example of this integrated approach.

Translating DaaS Into Business and Technical Wins

Adopting DevOps as a Service is a strategic technical decision designed to produce measurable improvements in engineering efficiency and business outcomes. The value is realized when the implementation of automated pipelines and infrastructure management directly translates into enhanced velocity, stability, and security. It's about converting complex engineering practices into a quantifiable return on investment.

The market reflects this urgency. The DevOps market is projected to grow from USD 12.54 billion in 2024 to USD 38.11 billion by 2029, a clear signal that optimizing the software delivery lifecycle is a top priority for modern enterprises. You can explore the data in this detailed DevOps market report.

Accelerated Time-to-Market

The most immediate technical benefit of a DaaS partnership is a dramatic reduction in the software delivery lifecycle. Deployments that previously required weeks of manual coordination can be executed in hours or even minutes. This is a fundamental shift in an organization's ability to deliver value to users.

This acceleration is achieved through a fully automated CI/CD pipeline that handles everything from code compilation to production deployment. This allows engineering teams to ship features multiple times a day, enabling rapid iteration based on user feedback and giving them a significant competitive advantage.

Enhanced Code Quality and System Stability

Velocity without stability is a recipe for failure. A core principle of DaaS is to improve software quality by embedding automated validation throughout the development process. A DaaS provider implements comprehensive, automated test suites (unit, integration, end-to-end) that run on every commit, catching bugs before they reach production.

This focus on proactive quality assurance leads to greater system stability. Practices like canary deployments and automated rollbacks minimize the impact of faulty releases. This significantly reduces the Mean Time to Recovery (MTTR), ensuring that when an issue does occur, its blast radius is contained and resolution is swift.

The objective is to engineer a resilient system where quality is not an afterthought but an intrinsic part of the delivery process. This creates a positive feedback loop: higher quality enables more frequent and confident deployments.

Reduced Operational Overhead

Building and maintaining an expert in-house DevOps team is a significant financial and operational investment. It involves high salaries for specialized engineers, ongoing training budgets, and the persistent challenge of recruitment in a competitive market. DaaS provides a more predictable and cost-effective alternative.

By partnering with a DaaS provider, you gain access to a team of specialists for a fixed operational cost, converting a large capital expenditure into a manageable operating expense. This frees your internal engineering team to focus on their primary objective: building your core product, not managing CI/CD pipelines or Kubernetes clusters. To understand the financial impact, explore our guide on the benefits of workflow automation.

Improved Security Posture

In an environment of escalating cyber threats, security must be integrated into every stage of the software lifecycle. DaaS providers implement DevSecOps practices, shifting security "to the left" and making it a shared responsibility across the engineering organization.

Automated security tools are embedded directly into the CI/CD pipeline to perform:

• Static Code Analysis: Identifying security vulnerabilities in your application source code.
• Dependency Scanning: Detecting known vulnerabilities (CVEs) in third-party libraries.
• Container Image Scanning: Ensuring your container images are free from known security flaws before deployment.

This continuous security validation closes vulnerabilities, simplifies compliance audits, and strengthens your application's defense against attacks from the outset.

Your Technical Roadmap for Implementing DaaS

Transitioning to a DevOps as a Service model is a structured engineering project, not an overnight switch. A clear technical roadmap is essential to guide your organization from its current state to a future of automated, efficient software delivery. This roadmap ensures alignment, mitigates risk, and establishes a framework for scalable success.

The implementation journey can be broken down into four distinct, actionable phases, each building upon the last to systematically transform how you build, test, and deploy software.

The diagram below illustrates this cyclical process, from initial assessment to continuous optimization.

Adopting DaaS is an iterative process of planning, execution, and refinement.

Phase 1: Audit and Goal Definition

Before building a new system, you must conduct a thorough analysis of the existing one. This initial phase is about deep technical discovery and establishing quantifiable goals. You cannot improve what you do not measure, and this step is crucial for establishing a baseline to demonstrate ROI.

Conduct a comprehensive audit of your current software delivery lifecycle. Map out the entire process, from code commit to production deployment, identifying every manual handoff, bottleneck, and source of friction.

Focus on collecting hard data for the four key DevOps metrics:

• Deployment Frequency: How often do you successfully release to production?
• Lead Time for Changes: What is the average time from code commit to production deployment?
• Change Failure Rate: What percentage of deployments result in a production failure requiring a rollback or hotfix?
• Mean Time to Recovery (MTTR): How long does it take to restore service after a production failure?

With this baseline data, you can set specific, measurable Key Performance Indicators (KPIs). Instead of a vague goal like "improve speed," aim for a concrete objective such as, "Reduce Lead Time for Changes from 14 days to less than 24 hours within Q3" or "Decrease Change Failure Rate by 50% in six months."

Phase 2: Provider Vetting and Onboarding

With clear objectives defined, the next step is to select the right technical partner. This phase requires rigorous due diligence to evaluate a DaaS provider's technical capabilities beyond their marketing materials.

The primary focus should be on toolchain and platform compatibility. The DaaS provider must integrate seamlessly with your existing technology stack.

A provider's inability to support your primary cloud platform (AWS, GCP), programming languages, or container orchestration system is a non-negotiable deal-breaker. The provider must adapt their solution to your environment, not force you into a proprietary, rigid framework.

Develop a technical requirements document and conduct deep-dive architectural reviews. Discuss specifics: how will their CI/CD solution handle your monorepo or microservices architecture? How will their IaC modules integrate with your existing network topology and security policies? Scrutinize their support model, Service Level Agreements (SLAs), and incident response procedures.

Phase 3: Pilot Project Implementation

With a provider selected, it's time to transition from planning to execution. However, avoid a "big bang" migration. Instead, initiate a pilot project with a single, non-critical application to validate the DaaS model in a controlled environment.

Choose an application that is complex enough to be a meaningful test but not so critical that a failure would impact the core business. This pilot serves as a live-fire exercise to test the CI/CD pipeline, IaC configurations, and collaborative workflows.

The key technical objectives of the pilot are to:

1. Validate the Pipeline: Successfully build, test, and deploy the pilot application through the new automated pipeline, from code commit to a staging environment.
2. Test IaC Scripts: Use the provider's Terraform or CloudFormation modules to provision and de-provision the application's infrastructure. Verify that environments are identical and reproducible.
3. Confirm Observability Integration: Ensure that metrics, logs, and traces from the pilot application are being correctly ingested and visualized in the new observability platform.
4. Establish Collaboration Protocols: Fine-tune communication channels (e.g., shared Slack channels, JIRA boards) and workflows between your internal team and the provider's engineers.

Phase 4: Scaling and Continuous Optimization

A successful pilot project provides the validation needed to scale the DaaS model across your organization. This final phase involves methodically migrating the rest of your applications and services while embracing a culture of continuous improvement.

Create a prioritized backlog of applications to onboard and migrate them in logical cohorts. Each migration will refine the process, making subsequent rollouts faster and smoother. This iterative approach minimizes disruption and builds momentum.

Simultaneously, leverage the rich data from your new observability platform to drive continuous optimization. Analyze deployment metrics, performance data, and system health dashboards to identify new opportunities for improvement. This data-driven feedback loop is the essence of DevOps, enabling you to constantly refine your pipelines, optimize infrastructure costs, and enhance system resilience.

How to Choose the Right DaaS Provider

Selecting a DevOps as a Service (DaaS) provider is a critical technical decision. You are not just procuring a service; you are integrating an external engineering team into your core operations. A methodical evaluation process is essential to cut through marketing claims and identify a partner whose technical expertise and operational model align with your specific requirements.

The decision hinges on four key pillars: technology stack compatibility, verifiable security posture, a robust support model, and the ability to scale with your organization. A mismatch in any of these areas can lead to vendor lock-in, architectural friction, and operational inefficiencies that negate the benefits of the partnership.

Technology Stack and Tooling Flexibility

Technical compatibility is the foundational requirement. A DaaS provider's technology stack must integrate seamlessly with your existing environment. If their solution is incompatible with your team's established tools and platforms, the partnership will introduce friction rather than reduce it.

Drill down into these technical specifics:

• Cloud Provider Expertise: Do they possess deep, demonstrable experience with your primary cloud platform (AWS, GCP, Azure)? Request case studies or reference architectures of complex systems they have managed on that platform.
• Container Orchestration: In modern environments, this almost exclusively means Kubernetes. Verify their hands-on experience in deploying, managing, scaling, and securing Kubernetes clusters. Do they have expertise with managed services like EKS, GKE, and AKS, as well as self-hosted clusters?
• IaC and CI/CD Tooling: A provider should be tool-agnostic. If your team has standardized on Terraform for IaC and GitLab CI for CI/CD, a provider insisting on their proprietary tooling will create significant technical debt and retraining overhead.

An ideal DaaS partner brings best-practice patterns and applies them to your technology stack. They should adapt to your environment, not force you to conform to theirs.

Security Certifications and Compliance

Entrusting an external provider with access to your infrastructure demands a high degree of confidence in their security practices. This confidence must be earned through third-party audits and adherence to recognized industry standards.

Request evidence of relevant certifications, such as:

• SOC 2 Type II: This audit report verifies that the provider has effective controls for security, availability, processing integrity, confidentiality, and privacy over an extended period.
• ISO 27001: This international standard specifies the requirements for an information security management system (ISMS), demonstrating a formal, risk-based approach to security.
• HIPAA or PCI DSS: If your organization handles protected health information (PHI) or payment card data, compliance with these regulations is non-negotiable.

Beyond certifications, probe their internal DevSecOps practices. How do they manage secrets? What is their process for vulnerability management? How do they enforce the principle of least privilege for both their employees and your infrastructure? A mature provider will have well-documented policies and procedures for these critical areas.

Support Model and Escalation Procedures

When a production system fails, the quality of your DaaS provider's support model is put to the test. Their Service Level Agreements (SLAs) and incident response procedures are critical components of the partnership.

Demand absolute clarity on these points:

1. Response Times: What are the contractually guaranteed response times for different incident severity levels (e.g., P1, P2, P3)?
2. Engineering Expertise: Does a support request go to a first-level agent or directly to a qualified DevOps engineer who can begin technical troubleshooting immediately?
3. Escalation Path: What is the defined process for escalating a complex or unresolved issue to senior engineers or architects?
4. Proactive Support: Does the service include proactive guidance, such as performance tuning recommendations, cost optimization analysis, and architectural reviews? This level of engagement distinguishes a true partner from a simple vendor. A DevOps consulting service often embodies this strategic, proactive approach.

DaaS Provider Evaluation Checklist

Use a structured checklist to conduct a side-by-side comparison of potential providers against your specific technical and operational requirements. This data-driven approach helps you move beyond sales pitches and make an informed decision.

Evaluation Criteria	Provider A	Provider B	Your Requirements
Cloud Expertise (AWS, GCP, Azure)			e.g., Deep AWS experience
Kubernetes Management (EKS, GKE, etc.)			Must support Amazon EKS
IaC/CI/CD Tool Flexibility			Must support Terraform & GitLab
Security Certifications (SOC 2, ISO 27001)			SOC 2 Type II required
Industry Compliance (HIPAA, PCI DSS)			N/A
SLA for Critical Incidents (P1)			< 15-minute response
Access to Senior Engineers			Direct access on P1 issues
Proactive Optimization & Guidance			Yes, quarterly reviews
Onboarding Process & Timeline			4-week pilot implementation
Pricing Model & Scalability			Predictable, tiered pricing

By methodically completing this evaluation for each contender, you create an objective basis for selecting a partner that is not only technically proficient today but also capable of supporting your long-term growth.

The Future of DevOps: AIOps and Platform Engineering

DevOps as a Service is not a static endpoint but an evolving practice. As organizations master CI/CD and Infrastructure as Code, the focus shifts toward building more intelligent, resilient, and developer-centric systems. This evolution is driven by two transformative trends: AIOps and platform engineering.

The market is aggressively moving in this direction. The global DaaS market, valued at USD 13.16 billion in 2024, is projected to reach USD 81.14 billion by 2033, growing at a CAGR of 19.95%. This rapid growth, detailed in the IMARC Group's market analysis, reflects a deep industry investment in advanced operational capabilities.

The Rise of AIOps

AIOps (Artificial Intelligence for IT Operations) represents the next evolution of automation. It involves embedding machine learning (ML) algorithms directly into the DevOps toolchain to move from reactive problem-solving to predictive and preventative operations.

Instead of waiting for a threshold-based alert to signal a failure, AIOps models analyze vast streams of telemetry data (metrics, logs, traces) to detect anomalies and predict potential issues before they impact users.

Key capabilities of AIOps include:

• Predictive Analytics: Identifying subtle deviations from normal system behavior that are precursors to failure.
• Intelligent Root Cause Analysis: Correlating alerts across multiple systems to pinpoint the single underlying cause of an outage, reducing Mean Time to Resolution (MTTR).
• Automated Remediation: Triggering self-healing actions—such as scaling a service, restarting a pod, or rolling back a deployment—without human intervention.

AIOps transforms observability from a passive monitoring tool into an active, intelligent system. For DaaS providers, it enables the management of highly complex, distributed systems with unprecedented accuracy and efficiency.

Platform Engineering and Developer Self-Service

The other significant trend is the emergence of platform engineering. This discipline focuses on building and maintaining an Internal Developer Platform (IDP), which provides a paved road for development teams.

An IDP is a curated set of tools, services, and automated workflows that abstracts away the complexity of the underlying infrastructure. It allows developers to self-service their operational needs through a simple, standardized interface.

With an IDP, a developer can provision a new microservice, spin up a testing environment, or access a database with a single API call or CLI command, without needing to file a ticket or understand the intricacies of Kubernetes or Terraform. This model aligns perfectly with DaaS, where the provider builds and operates the underlying platform. It empowers developers with autonomy and speed while enforcing organizational standards for security, compliance, and cost management.

Got Questions About DaaS? Let's Clear a Few Things Up.

Evaluating DevOps as a Service inevitably raises critical questions from both engineering and business stakeholders. Addressing these concerns with technical clarity is essential for determining if this model is the right fit for your organization. Here are direct answers to the most common questions from technical leaders considering a DaaS partnership.

What's the Real Difference Between DevOps and DaaS?

The distinction lies in philosophy versus implementation.

DevOps is a cultural philosophy and a set of practices adopted internally. It involves breaking down organizational silos between development and operations, fostering shared ownership, and building a DIY toolchain to automate the software delivery lifecycle. This requires a significant, long-term investment in hiring, training, and tool management.

DevOps as a Service (DaaS), in contrast, is a managed operational model. It is the implementation of DevOps principles delivered as a service. Instead of building the capability from the ground up, you partner with a provider that supplies the entire operational framework—the integrated toolchain, the proven automation workflows, and the specialized engineers—as a turnkey solution. DaaS is a way to achieve the outcomes of DevOps without the extensive upfront investment and learning curve.

Can DaaS Even Work in a Hybrid Environment?

Yes, absolutely. A competent DaaS provider is architected for heterogeneity and has deep expertise in managing complex infrastructure topologies. This includes hybrid-cloud (a mix of on-premises data centers and public cloud) and multi-cloud (leveraging services from multiple providers like AWS, Azure, and GCP) environments.

The provider's role is to create a unified control plane—a single CI/CD pipeline and observability stack—that abstracts away the underlying infrastructure's location.

Through the consistent application of Infrastructure as Code (IaC) and container orchestration with Kubernetes, a DaaS provider can enforce uniform deployment and management patterns regardless of where the application workload is running. This creates a single, coherent operational view across a distributed and diverse infrastructure landscape.

How Does a DaaS Provider Handle Security?

Security is not a separate service; it is an integral, non-negotiable component of a mature DaaS offering. The core principle is DevSecOps, which involves embedding automated security controls throughout the entire software delivery lifecycle. This proactive, "shift-left" approach is far more effective than traditional, late-stage security gates.

This is implemented through multiple automated layers:

• Pipeline Security: Automated Static Application Security Testing (SAST) and Software Composition Analysis (SCA) tools are integrated directly into the CI/CD pipeline. These tools scan code and dependencies for vulnerabilities on every commit, providing immediate feedback to developers.
• Infrastructure Hardening: The provider uses IaC to codify and enforce security best practices, such as network security group rules, IAM policies, and encrypted data storage. This ensures secure, consistent configurations across all environments and prevents configuration drift.
• Continuous Monitoring: An advanced monitoring and alerting system is configured to detect and respond to security threats and compliance deviations in real time, such as unauthorized access attempts or suspicious API calls.

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