Understanding the services
What are cloud engineering services?
Cloud engineering services are professional services in which specialist engineers design, build, automate, and improve an organization's cloud infrastructure and platforms. Typical work includes cloud architecture, landing zones, infrastructure as code, CI/CD pipelines, container and Kubernetes platforms, observability, reliability, security automation, and cost engineering.
The emphasis is on building. Where cloud consulting produces recommendations and managed services keep an environment running, cloud engineering services produce working systems: code in repositories, automated pipelines, reusable modules, and platforms that application teams use to ship software.
Cloud engineering services are hands-on technical services that design, implement, automate, and optimize cloud infrastructure, deployment pipelines, and internal platforms, typically delivered as infrastructure as code with documentation and a handover to the client's team.
Cloud engineering as a discipline sits between systems administration, networking, security, and software development. The cloud engineering section of our main guide explains how the role differs from software development and IT administration. This page focuses on buying that expertise as a service. It is one branch of the wider category of cloud tech services.
What cloud engineering services include
Most cloud engineering services fall into ten areas: architecture and landing zones, infrastructure as code, CI/CD and release engineering, containers and Kubernetes, platform engineering, reliability engineering and observability, security engineering, data and machine learning infrastructure, cost engineering, and application modernization.
| Area | What gets built | Common tools and standards |
|---|---|---|
| Architecture and landing zones | Account structure, networking, identity, guardrails | Provider landing zone guidance, Well-Architected frameworks |
| Infrastructure as code | Reusable modules for every cloud resource | Terraform, OpenTofu, CloudFormation, Bicep, Pulumi |
| CI/CD and release engineering | Build, test, and deployment pipelines | GitHub Actions, GitLab CI/CD, Jenkins, Argo CD |
| Containers and Kubernetes | Clusters, base images, deployment patterns | EKS, AKS, GKE, OpenShift, Helm |
| Platform engineering | Internal developer platforms and golden paths | Developer portals, templates, self-service workflows |
| Reliability and observability | SLOs, dashboards, alerting, incident processes | OpenTelemetry, Prometheus, Grafana, provider monitoring |
| Security engineering | Policy as code, secrets, supply chain controls | CIS Benchmarks, NIST SSDF, SLSA, policy engines |
| Data and ML infrastructure | Data pipelines, warehouses, model serving | Managed data and ML services |
| Cost engineering (FinOps) | Tagging, rightsizing automation, cost reporting | Provider cost tools, FinOps Framework |
| Application modernization | Containerized or refactored applications | Containers, managed runtimes, serverless |
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Cloud architecture and landing zones
Engineers design and build the foundation every workload will use: an account or subscription structure, network topology, identity and access management, centralized logging, security guardrails, and billing structure. This foundation is often called a landing zone. The work draws on published frameworks such as the AWS Well-Architected Framework, the Azure Well-Architected Framework, and the Google Cloud Architecture Framework, which cover reliability, security, cost, performance, and operational practices. When the goal is a first production-ready environment, this overlaps with cloud implementation services.
Infrastructure as code
Infrastructure as code (IaC) defines servers, networks, databases, and permissions in version-controlled files instead of manual console changes. Engineers write reusable modules, set up remote state storage and locking, add automated validation, and detect drift between code and reality. Good IaC makes environments reproducible, reviewable, and auditable, and it is the single most important thing a cloud engineering engagement should leave behind.
CI/CD and release engineering
Continuous integration and continuous delivery pipelines build, test, scan, and deploy both application code and infrastructure changes. Engineering work includes pipeline templates, test automation hooks, artifact storage, environment promotion, and safer release methods such as blue-green and canary deployments. GitOps, where tools such as Argo CD or Flux keep clusters in sync with a Git repository, is a common pattern for Kubernetes.
Containers and Kubernetes
Engineers containerize applications, build hardened base images, and design and operate Kubernetes clusters on managed services such as Amazon EKS, Azure Kubernetes Service, and Google Kubernetes Engine. The work covers cluster networking, ingress, autoscaling, secrets, upgrades, and multi-tenant access. Kubernetes is powerful but operationally heavy, so a good provider will also tell you when a simpler container service is enough.
Platform engineering
Platform engineering builds an internal developer platform: a curated set of self-service tools, templates, and "golden paths" that let application teams create services, environments, and pipelines without filing tickets. The Cloud Native Computing Foundation (CNCF) has published a platforms white paper and a platform engineering maturity model that describe the practice. Engineering services can build the first version of a platform and help an internal platform team take it over.
Reliability engineering and observability
Reliability work applies site reliability engineering (SRE) practices popularized by Google: defining service level objectives (SLOs), using error budgets to balance reliability against release speed, building dashboards and alerts that reflect user experience, and setting up incident response and post-incident reviews. Observability is increasingly built on OpenTelemetry, a vendor-neutral standard for traces, metrics, and logs.
Security engineering
Security engineering builds controls into the platform rather than checking for problems afterward. Typical deliverables include policy as code that blocks non-compliant resources, automated configuration checks against the CIS Benchmarks, secrets management, least-privilege roles, and software supply chain protections aligned with frameworks such as NIST's Secure Software Development Framework (SP 800-218) and SLSA. Ongoing monitoring is usually handled separately by managed cloud security services.
Data and machine learning infrastructure
Engineers build the infrastructure behind analytics and AI: data pipelines, warehouses and lakehouses, streaming systems, GPU capacity, and model training and serving environments. The engineering concerns are the same as elsewhere, including automation, access control, cost, and reliability, applied to data-heavy workloads.
Cost engineering
Cost engineering turns FinOps principles into automation: mandatory tagging, scheduled shutdown of non-production environments, rightsizing recommendations wired into pipelines, commitment planning, and cost dashboards per team or product. The FinOps Foundation's framework is the common reference.
Application modernization
Engineering teams containerize existing applications, move them to managed runtimes, or break them into services, often during or after a migration. For Java workloads specifically, see our guide to choosing a Java cloud service. Modernization that involves substantial new application code overlaps with cloud development services.
What you should receive from a cloud engineering engagement
At the end of a cloud engineering engagement you should own working code and the knowledge to run it: infrastructure as code and pipeline definitions in your repositories, architecture diagrams and decision records, runbooks, dashboards, and a completed handover to your team.
- Source code in your repositories: infrastructure modules, environment configurations, pipeline definitions, and policy code, with commit history.
- State and secrets under your control: IaC state files and secrets stored in your cloud accounts, not the provider's.
- Architecture documentation: current diagrams and architecture decision records (ADRs) explaining why key choices were made.
- Runbooks: step-by-step procedures for deployments, rollbacks, scaling, certificate renewal, and common incidents.
- Observability: dashboards, alerts, and SLO definitions that match your services.
- Test evidence: results from pipeline runs, failover or restore tests, and security scans.
- Handover: recorded walkthroughs, pairing sessions, and a period where your team makes changes with provider support.
"Build a landing zone" is hard to accept or reject. "A new production account with networking, logging, and guardrails can be created from the pipeline in under one hour by a member of our team, using only the documentation provided" is testable, and it forces the handover to be real.
Working with a provider
Engagement and pricing models for cloud engineering services
Cloud engineering services are usually delivered as a scoped project, a dedicated team (often called a pod or squad), individual engineers added to your team, or an ongoing platform partnership. Pricing follows the model: fixed fee for tightly scoped projects, time and materials for evolving work, and monthly fees for dedicated teams and retainers.
| Model | How it works | Typical pricing | Best for |
|---|---|---|---|
| Scoped project | Defined deliverables and end date | Fixed fee or capped time and materials | Landing zones, pipeline builds, specific migrations |
| Dedicated team | A small cross-functional team works through a backlog | Monthly fee per team | Platform builds and multi-month programs |
| Staff augmentation | Engineers join your team under your direction | Hourly or monthly rate per engineer | Capacity gaps when you already have leadership |
| Retainer | A block of expert hours each month | Monthly fee | Ongoing improvements, reviews, and escalation support |
| Co-managed platform | Provider builds and helps run the platform with your team | Monthly fee, sometimes tiered by scope | Organizations growing an internal platform team |
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Rates vary widely with seniority, location, and specialization, so this guide does not quote figures. The main cost drivers are the number of environments and accounts, compliance requirements, how many cloud platforms are involved, the maturity of existing automation, and how much knowledge transfer you need. Cloud usage costs are separate from service fees. For a comparison of pricing models and how they shift risk, see the pricing section of our cloud tech services guide.
How to measure the success of cloud engineering services
Measure cloud engineering services by outcomes, not activity: faster and safer software delivery, reliability against agreed targets, stronger security posture, lower cost per unit of business activity, and a team that can run what was built. Agree on baseline measurements before work starts.
Software delivery: DORA metrics
The DORA research program, now part of Google Cloud, defines widely used software delivery metrics. Its current guidance lists five:
| Metric | What it measures |
|---|---|
| Change lead time | Time from a change being committed to it running in production |
| Deployment frequency | How often changes are deployed to production |
| Failed deployment recovery time | Time to recover from a deployment that fails and needs immediate intervention |
| Change fail rate | Share of deployments that need immediate intervention, such as a rollback or hotfix |
| Deployment rework rate | Share of deployments that are unplanned and caused by a production incident |
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Other measures worth tracking
- Reliability: SLO attainment and error budget consumption for key services.
- Provisioning time: how long it takes to create a new environment or service from a request.
- Infrastructure as code coverage: the share of cloud resources managed by code, and how much drift exists.
- Security posture: open critical misconfigurations, time to patch, and policy violations blocked before deployment.
- Unit cost: cloud cost per customer, transaction, or other business measure, rather than total spend alone.
- Team independence: the share of changes your own engineers make without provider help by the end of the engagement.
What a cloud engineering engagement can look like
A typical foundation engagement moves from discovery to a working landing zone, automated pipelines, the first workloads in production, and a structured handover. The example below is illustrative; real timelines depend on scope, existing systems, and how quickly decisions are made.
- DiscoveryReview current environments, applications, team skills, compliance requirements, and goals. Agree on success metrics and baselines.
- DesignProduce the target architecture, account structure, network design, and tooling choices, with decision records for each major choice.
- Foundation buildImplement the landing zone as code: accounts, networking, identity, logging, guardrails, and cost allocation.
- Pipelines and platformBuild CI/CD for infrastructure and applications, base images, and self-service templates for common workloads.
- First workloadsDeploy or migrate one or two representative applications through the new platform to prove it works end to end.
- Operational readinessSet up observability, SLOs, alerting, backup and restore tests, and runbooks.
- HandoverPair with your engineers, transfer ownership of repositories and processes, and agree on any ongoing support.
Tooling choices and lock-in
The tools a provider chooses shape your costs and flexibility for years. Prefer widely used, well-documented tools your team can hire for, keep all code and state in accounts you control, and make sure licensing terms are understood before the work starts.
Infrastructure as code is a good example. Terraform has been the most widely used multi-cloud IaC tool, but HashiCorp moved it from an open-source license to the Business Source License in August 2023, and IBM completed its acquisition of HashiCorp in 2025. In response, the community created OpenTofu, an open-source fork now hosted under the Linux Foundation. Provider-native tools such as AWS CloudFormation and Azure Bicep avoid third-party licensing but tie you more closely to one cloud. Any of these can be the right choice; what matters is that the decision is explicit and documented.
The same applies to CI/CD platforms, observability tools, and developer portals. Ask the provider which tools require paid licenses, who holds those licenses, and what it would take to replace each tool later.
Decisions
When you need cloud engineering services
Organizations typically bring in cloud engineering services when their cloud environment has outgrown manual management, when developers are slowed by infrastructure requests, or when a deadline requires experienced engineers faster than they can be hired.
- Environments are created by hand in the console, and nobody is sure they match each other.
- Developers wait days for new environments, databases, or pipeline changes.
- Deployments are manual, infrequent, or regularly cause incidents.
- There is no reliable way to recreate production after a major failure.
- Cloud costs are rising and nobody can attribute them to teams or products.
- An audit or customer security review has exposed gaps in access control or logging.
- You are adopting Kubernetes or building a platform team for the first time.
If the need is continuous and central to your product, the long-term answer is usually an internal team. Outside engineers are most valuable for building foundations quickly, bringing experience your team lacks, and training your staff along the way.
How to choose a cloud engineering services provider
Choose a cloud engineering services provider by examining real work, not presentations: review sample code and designs, meet the engineers who will be assigned, check experience on your cloud platform and at your scale, and agree in writing on ownership, documentation, and handover.
Evaluation criteria
- Engineering qualityAsk to review anonymized infrastructure code, pipeline definitions, or architecture decision records from past work. Look for modular code, tests, clear naming, and documentation.
- Relevant experienceCheck work on your cloud platform, with your stack (for example, Kubernetes, serverless, or specific databases), and in your industry's compliance context.
- The assigned teamMeet the engineers, not only the sales lead. Ask about certifications, hands-on experience, and continuity if someone leaves.
- Security practicesConfirm how engineers access your accounts (individual identities, MFA, least privilege, logged sessions) and how secrets are handled.
- Working styleLook for pull-request-based changes, code review by your team, shared backlogs, and regular demos.
- Knowledge transferAsk for a specific handover plan, including pairing, documentation, and a period where your team leads with the provider supporting.
- Tool neutralityAsk whether the provider resells or receives incentives for any tools it recommends.
- Commercial termsCompare engagement models, how scope changes are handled, and who carries overrun risk.
Technical questions to ask
- How do you structure infrastructure code and modules, and how do you test them?
- Where will IaC state live, and how is it locked and backed up?
- How do you detect and handle drift between code and deployed resources?
- How do you manage secrets in pipelines and applications?
- How would you define SLOs for our main services?
- What would you recommend we not build, and why?
Red flags
- Repositories, cloud accounts, or state files created under the provider's ownership.
- Heavy manual console work instead of code.
- Proprietary frameworks or tooling that only the provider understands.
- No plan for documentation or handover, or a handover squeezed into the final week.
- Recommending Kubernetes or a complex platform for a small, simple workload without discussing alternatives.
For contracts, SLAs, and exit terms that apply to any provider, see the contracts section of our cloud tech services guide.
Cloud engineering services by business size
Smaller organizations usually need a solid, simple foundation built once and handed over. Growing companies need automation and a platform that keeps up with more teams. Large enterprises need platform engineering, governance at scale, and engineering capacity for multi-year programs.
Startups and small businesses
The priority is a secure, cost-aware foundation that a small team can run: a basic landing zone, infrastructure as code, a straightforward deployment pipeline, backups, and monitoring. Favor managed services and simple container platforms over self-managed Kubernetes unless there is a clear reason.
Growing companies
As engineering teams multiply, the needs shift to consistent environments, reusable modules, self-service provisioning, SLOs for critical services, and cost allocation by team. This is often when organizations form their first platform team, sometimes seeded by outside engineers.
Enterprises
Large organizations typically run many accounts across more than one cloud, alongside existing data centers. Cloud engineering services support internal developer platforms, policy as code across hundreds of teams, regulated workloads, and large migration and modernization programs, usually working alongside internal platform, security, and operations teams.
Reference
Cloud engineering services FAQs
What do cloud engineering services include?
Cloud engineering services typically include cloud architecture and landing zones, infrastructure as code, CI/CD pipelines, containers and Kubernetes, platform engineering, reliability engineering and observability, security automation, data and ML infrastructure, cost engineering, and application modernization.
What is the difference between cloud engineering and cloud consulting?
Cloud consulting focuses on strategy and recommendations, such as what to move, which provider to use, and how to govern cloud use. Cloud engineering builds and automates the actual infrastructure, pipelines, and platforms. Many providers offer both, and projects often start with consulting and continue with engineering.
Is cloud engineering the same as DevOps?
They overlap heavily. DevOps is a set of practices and a culture for delivering software quickly and reliably. Cloud engineering is the technical discipline of building and automating cloud infrastructure, and it relies on DevOps practices such as automation, CI/CD, and shared ownership. Platform engineering and site reliability engineering are closely related specialties.
How much do cloud engineering services cost?
Costs depend on scope, number of environments, compliance requirements, the cloud platforms involved, and engineer seniority and location. Pricing is usually a fixed fee for scoped projects, time and materials, or a monthly fee for dedicated teams and retainers. Cloud usage is billed separately by the cloud provider.
Should we hire cloud engineers or use a services provider?
Hire when the work is continuous and central to your business. Use a provider to build foundations quickly, fill skill gaps, or handle a time-bound program, ideally with a plan to transfer knowledge to internal staff. Many organizations combine both.
What should we own at the end of an engagement?
You should own all infrastructure code, pipeline definitions, IaC state, documentation, runbooks, dashboards, and access to every cloud account, with your team able to make changes without the provider.
Which infrastructure as code tool should we use?
Terraform and its open-source fork OpenTofu are common for multi-cloud work, while AWS CloudFormation, Azure Bicep, and Pulumi are also widely used. Choose based on your cloud platforms, team skills, licensing preferences, and existing code, and document the reasoning.
How long does a cloud engineering project take?
Focused work such as a pipeline or a small landing zone can take a few weeks. A full foundation with pipelines, a platform, and first workloads often takes a few months. Platform programs and large modernization efforts can run for a year or more, usually in phases.
Sources and further reading
Frameworks and definitions in this guide come from the following primary sources.
- DORA, DORA's software delivery performance metrics
- Google, Site Reliability Engineering: Service Level Objectives
- CNCF TAG App Delivery, CNCF Platforms White Paper
- CNCF TAG App Delivery, Platform Engineering Maturity Model
- AWS, The pillars of the AWS Well-Architected Framework
- Microsoft, Azure Well-Architected Framework pillars
- Microsoft, What is an Azure landing zone?
- Google Cloud, Google Cloud Architecture Framework
- NIST, SP 800-218, Secure Software Development Framework
- OpenSSF, SLSA: Supply-chain Levels for Software Artifacts
- CIS, CIS Benchmarks
- OpenTelemetry, Documentation
- OpenTofu, OpenTofu project
- FinOps Foundation, FinOps Framework
About this guide
This guide is published by Crecso as an independent educational resource and is part of our cloud technology guide. It names tools and providers as examples only, is not affiliated with any cloud or tooling vendor, and does not describe services offered by Crecso.
Last reviewed on . If you spot something that has changed, please let us know through our contact page.