Cloud-Native & DevOpsCloud-Native & DevOps Engineering

Routine releases, reliable services and a clear cloud bill

We build your cloud platform as code on AWS, Azure or Google Cloud, with golden paths that make the secure way easy.

Start a project See the approach

Outcomes

Self-⁠service, safe releases, traceable spend

Self-⁠service within guardrails
Teams create environments and new services from reviewed templates, with access, security and tagging rules applied automatically, instead of waiting in an operations queue.
One tested path to production
Every change moves through the same automated build, tests, security checks and progressive rollout, so deploying stops being an event the whole team plans around.
Reliability you can measure
Service-⁠level objectives define what working means for each service. Alerts fire on symptoms users would feel, and error budgets show when reliability work should come first.
Spend tied to teams and services
Every resource maps to a team, service and environment. Finance and engineering work from the same figures, and savings decisions rest on real usage.

Capabilities

The platform, its pipelines, its guardrails

We build the platform in layers: a secure foundation first, then the runtimes and delivery paths your teams use daily, and finally the reliability and cost signals on top.

Landing zones and security baselines

Accounts, identity, networking, logging and guardrails are defined in code before workloads arrive, following each provider’s reference patterns, so every team starts from a secure baseline.

Accounts, subscriptions or projects per team and environment
Single sign-⁠on, least privilege and break-⁠glass access
Hub-⁠and-⁠spoke networking, private endpoints and DNS
Encryption, audit logs and CIS-⁠aligned baselines

Infrastructure and policy as code

Infrastructure changes follow the same path as application code: a pull request, an automated plan, policy checks and a reviewed apply. Console changes become rare, and drift gets flagged.

Reusable modules in Terraform, OpenTofu or Bicep
Plans and policy checks on every pull request
Cloud policy and Open Policy Agent guardrails
Drift detection against the declared state

Containers, Kubernetes and serverless

We choose the simplest runtime that meets the need: managed Kubernetes when your scale and team justify it, managed containers or functions when they don’t.

Managed clusters on EKS, AKS or GKE
GitOps delivery with Argo CD or Flux
Serverless on Lambda, Azure Functions or Cloud Run
Autoscaling, resource limits and workload isolation

CI/CD and platform engineering

Golden paths give teams a supported way to create, build and ship a service. Templates, pipelines and documentation sit together in an internal developer platform, run like a product.

Service templates with pipelines and guardrails built in
Security scans, SBOMs and signed images per build
Canary and blue-⁠green releases with automated rollback
Developer portals such as Backstage, when they help

SRE and observability

Metrics, logs and traces tie to service-⁠level objectives agreed with product owners, so alerts point to real user impact and on-⁠call stays sustainable.

OpenTelemetry instrumentation across services
SLOs, error budgets and burn-⁠rate alerts
On-⁠call rotations, runbooks and incident roles
Blameless reviews with tracked follow-⁠up actions

FinOps and cost visibility

Spend is broken down by team, service and environment, tracked as unit costs such as cost per order, and reviewed with the engineers who can change it.

Tagging enforced by policy from the start
Cost estimates on infrastructure pull requests
Budgets and anomaly alerts for each team
Rightsizing, scheduling and commitments from real usage

Landing zones and security baselines
Accounts, identity, networking, logging and guardrails are defined in code before workloads arrive, following each provider’s reference patterns, so every team starts from a secure baseline.
- Accounts, subscriptions or projects per team and environment
- Single sign-⁠on, least privilege and break-⁠glass access
- Hub-⁠and-⁠spoke networking, private endpoints and DNS
- Encryption, audit logs and CIS-⁠aligned baselines
Infrastructure and policy as code
Infrastructure changes follow the same path as application code: a pull request, an automated plan, policy checks and a reviewed apply. Console changes become rare, and drift gets flagged.
- Reusable modules in Terraform, OpenTofu or Bicep
- Plans and policy checks on every pull request
- Cloud policy and Open Policy Agent guardrails
- Drift detection against the declared state
Containers, Kubernetes and serverless
We choose the simplest runtime that meets the need: managed Kubernetes when your scale and team justify it, managed containers or functions when they don’t.
- Managed clusters on EKS, AKS or GKE
- GitOps delivery with Argo CD or Flux
- Serverless on Lambda, Azure Functions or Cloud Run
- Autoscaling, resource limits and workload isolation
CI/CD and platform engineering
Golden paths give teams a supported way to create, build and ship a service. Templates, pipelines and documentation sit together in an internal developer platform, run like a product.
- Service templates with pipelines and guardrails built in
- Security scans, SBOMs and signed images per build
- Canary and blue-⁠green releases with automated rollback
- Developer portals such as Backstage, when they help
SRE and observability
Metrics, logs and traces tie to service-⁠level objectives agreed with product owners, so alerts point to real user impact and on-⁠call stays sustainable.
- OpenTelemetry instrumentation across services
- SLOs, error budgets and burn-⁠rate alerts
- On-⁠call rotations, runbooks and incident roles
- Blameless reviews with tracked follow-⁠up actions
FinOps and cost visibility
Spend is broken down by team, service and environment, tracked as unit costs such as cost per order, and reviewed with the engineers who can change it.
- Tagging enforced by policy from the start
- Cost estimates on infrastructure pull requests
- Budgets and anomaly alerts for each team
- Rightsizing, scheduling and commitments from real usage

Approach

Start with one team’s path to production

See our delivery process

Step 1: Trace the path to production
We follow a change from commit to production and review accounts, access, incidents and the bill with your team, to find what slows releases, risks outages or wastes money.
Activities
- Walk the delivery pipeline with the engineers who use it
- Review security, reliability and cost posture
- Baseline DORA delivery metrics and incident history
You receive
- Prioritized findings and a target platform design
- A roadmap sequenced by risk and payoff
1
Step 1: Trace the path to production
We follow a change from commit to production and review accounts, access, incidents and the bill with your team, to find what slows releases, risks outages or wastes money.
Activities
- Walk the delivery pipeline with the engineers who use it
- Review security, reliability and cost posture
- Baseline DORA delivery metrics and incident history
You receive
- Prioritized findings and a target platform design
- A roadmap sequenced by risk and payoff
Step 2: Build the foundations as code
The landing zone, identity, networking, guardrails and tagging rules come first, all in version-⁠controlled code, so everything that follows starts from the same baseline.
Activities
- Account, network and access structure
- Policy guardrails, logging and secrets management
- Tagging rules and budgets from day one
You receive
- Landing zone and policies in your repositories
- A documented baseline for every new workload
2
Step 2: Build the foundations as code
The landing zone, identity, networking, guardrails and tagging rules come first, all in version-⁠controlled code, so everything that follows starts from the same baseline.
Activities
- Account, network and access structure
- Policy guardrails, logging and secrets management
- Tagging rules and budgets from day one
You receive
- Landing zone and policies in your repositories
- A documented baseline for every new workload
Step 3: Pave the golden path with a pilot team
Working with a pilot team, we build the service template, pipeline and runtime setup, then refine them until the supported way is easier than any workaround.
Activities
- Service template and CI/CD pipeline built with the pilot team
- Runtime chosen and configured: Kubernetes, containers or serverless
- Self-⁠service documentation and a support channel
You receive
- A golden path in production use
- A platform backlog shaped by developer feedback
3
Step 3: Pave the golden path with a pilot team
Working with a pilot team, we build the service template, pipeline and runtime setup, then refine them until the supported way is easier than any workaround.
Activities
- Service template and CI/CD pipeline built with the pilot team
- Runtime chosen and configured: Kubernetes, containers or serverless
- Self-⁠service documentation and a support channel
You receive
- A golden path in production use
- A platform backlog shaped by developer feedback
Step 4: Make reliability and cost visible
We instrument services, agree on SLOs with product owners, route alerts to the right people and put cost reports in front of the teams that create the spend.
Activities
- OpenTelemetry instrumentation and SLO dashboards
- Alert routing, runbooks and incident roles
- Cost allocation reports and anomaly alerts per team
You receive
- SLO and cost dashboards for each service
- On-⁠call runbooks and an incident process
4
Step 4: Make reliability and cost visible
We instrument services, agree on SLOs with product owners, route alerts to the right people and put cost reports in front of the teams that create the spend.
Activities
- OpenTelemetry instrumentation and SLO dashboards
- Alert routing, runbooks and incident roles
- Cost allocation reports and anomaly alerts per team
You receive
- SLO and cost dashboards for each service
- On-⁠call runbooks and an incident process
Step 5: Hand over and keep improving
Your engineers take ownership through pairing and documented runbooks, or we keep running the platform alongside you. Regular reviews of incidents, pipeline health and spend feed the backlog.
Activities
- Pairing and enablement for your platform team
- Incident, delivery and cost reviews on an agreed cadence
- Rightsizing and commitment planning from real usage
You receive
- Handover pack of code, diagrams and runbooks
- An improvement backlog your team owns
5
Step 5: Hand over and keep improving
Your engineers take ownership through pairing and documented runbooks, or we keep running the platform alongside you. Regular reviews of incidents, pipeline health and spend feed the backlog.
Activities
- Pairing and enablement for your platform team
- Incident, delivery and cost reviews on an agreed cadence
- Rightsizing and commitment planning from real usage
You receive
- Handover pack of code, diagrams and runbooks
- An improvement backlog your team owns

Deliverables and fit

A platform in code, dashboards and runbooks

What you receive

8 deliverables

Platform assessment with prioritized risks and a target design
Landing zone, identity and network baseline defined in code
Reusable infrastructure modules and policy-⁠as-⁠code guardrails
Golden-⁠path service templates and CI/CD pipelines
Kubernetes, container or serverless runtime configuration
Observability stack with SLO dashboards, alert routing and runbooks
Cost allocation model, budgets and per-⁠team spend reports
Architecture diagrams, decision records and handover sessions

What you receive

Platform assessment with prioritized risks and a target design
Landing zone, identity and network baseline defined in code
Reusable infrastructure modules and policy-⁠as-⁠code guardrails
Golden-⁠path service templates and CI/CD pipelines
Kubernetes, container or serverless runtime configuration
Observability stack with SLO dashboards, alert routing and runbooks
Cost allocation model, budgets and per-⁠team spend reports
Architecture diagrams, decision records and handover sessions

Seen from behind, two developers work at large monitors showing code in a bright office. — Accounts, networks and guardrails, all defined in code

A good fit if

Deployments depend on manual steps and the people who remember them
Staging and production have drifted apart over time
Every new service waits in a queue for infrastructure and pipelines
Diagnosing an incident means searching logs, metrics and traces in separate tools
Your cloud bill keeps growing without a clear link to the services behind it
You’re weighing Kubernetes and want an honest view of whether you need it

Engagement models

A platform build, missing skills or a standing team

The models that usually suit this service. You can switch as the work changes.

Compare engagement models

Project delivery
A landing zone, platform or golden path built to agreed sign-off criteria.
Team extension
Kubernetes, infrastructure-as-code or SRE skills your platform team is missing.
Dedicated team
A platform team that keeps paving the golden path after launch.

Technology

Platform and pipeline tools

Listed so you can check the fit with your stack. None of them implies a partnership or certification.

Tools and platforms

AWS
Microsoft Azure
Google Cloud
Terraform
Kubernetes
GitHub Actions
GitLab CI/CD
Azure DevOps
Open Policy Agent
Backstage
Argo CD
OpenTelemetry
Prometheus
Grafana

Illustrative scenarioGetting a retailer’s cloud platform ready for peak seasonRead the scenarioHide the scenario

Illustrative scenario

Getting a retailer’s cloud platform ready for peak season

An online retailer that moved its storefront, order services and back-⁠office tools to the cloud unchanged, onto virtual machines that each team set up differently.

Challenge: Extra capacity for sales events is added by hand and removed late, deployments follow a checklist only a few people can run, and when checkout slows down nobody can tell which service is at fault. Finance sees one growing bill with no owners.

Approach: 1Bring the existing infrastructure under Terraform, then rebuild staging and production from shared modules
2Make the order services stateless and move them to a managed container platform, with autoscaling tested against modeled sale-⁠day traffic
3Ship every service through one pipeline with canary releases and automatic rollback
4Trace checkout end to end with OpenTelemetry, and set SLOs for it with the ecommerce team
5Enforce cost tags through policy as code and report cost per order to each team
Outcome: Capacity follows demand during sales instead of being provisioned by hand, a faulty release rolls back without a scramble, and a slow checkout can be traced to the service responsible. Each team sees its cost per order rather than a slice of one bill.

Services involved

Server-Side & APIs

Illustrative scenario

Getting a retailer’s cloud platform ready for peak season

An online retailer that moved its storefront, order services and back-⁠office tools to the cloud unchanged, onto virtual machines that each team set up differently.

Challenge: Extra capacity for sales events is added by hand and removed late, deployments follow a checklist only a few people can run, and when checkout slows down nobody can tell which service is at fault. Finance sees one growing bill with no owners.

Approach: 1Bring the existing infrastructure under Terraform, then rebuild staging and production from shared modules
2Make the order services stateless and move them to a managed container platform, with autoscaling tested against modeled sale-⁠day traffic
3Ship every service through one pipeline with canary releases and automatic rollback
4Trace checkout end to end with OpenTelemetry, and set SLOs for it with the ecommerce team
5Enforce cost tags through policy as code and report cost per order to each team
Outcome: Capacity follows demand during sales instead of being provisioned by hand, a faulty release rolls back without a scramble, and a slow checkout can be traced to the service responsible. Each team sees its cost per order rather than a slice of one bill.

Services involved

Server-Side & APIs

Ask about a project like this

FAQ

Questions before you build a platform

Ask a question

Can you work with the cloud setup we already have?

Yes, and that is the usual starting point. We bring existing resources under infrastructure as code step by step, starting with what changes most often, rather than rebuilding everything at once. If workloads still run on-⁠premises, our cloud migration service plans and runs that move, and this work builds the platform they land on.

Do we actually need Kubernetes?

Not always. Kubernetes pays off when you run many services and have engineers to operate it. For a smaller set of services, managed container platforms such as Amazon ECS, Azure Container Apps or Cloud Run, or serverless functions, are often cheaper to run and easier to staff. We recommend the simplest platform that meets your requirements and write down what each option would cost you in effort and flexibility.

Do we need an internal developer platform?

You need a supported path to production, which is not the same as needing a portal. Most of the value comes from one well-⁠maintained golden path for your most common kind of service: a template, a pipeline and a runtime with guardrails built in. We start there, watch how teams use it and add a portal such as Backstage only when the number of services makes finding things a real problem.

How do you build security and compliance into the platform?

Controls are built into the platform rather than left to a review at the end: single sign-⁠on with least-⁠privilege roles, encryption by default, central audit logs, managed secrets and policy as code that blocks non-⁠compliant resources before they are created. We work within your compliance requirements, for example ISO 27001, SOC 2 or PCI DSS, and align configuration baselines with CIS benchmarks. Evidence comes from code, policy results and logs rather than screenshots, and certification decisions stay with your auditors.

How do you keep cloud spend under control?

Visibility comes first: tagging enforced by policy, so every cost has an owner, and reports that the owning teams actually review. Then come the usual levers: rightsizing from observed usage, switching off idle non-⁠production environments, storage lifecycle rules and commitment discounts such as AWS Savings Plans, Azure reservations or Google Cloud committed use discounts once usage is steady. Cost estimates on infrastructure pull requests show the effect of a change before it merges.

Next step

Which team’s path to production comes first?

Describe the system, process or decision in front of you. Expect questions back, a sensible first step and a fitting engagement model.

First step: A conversation about the problem, your systems and constraints
You leave with: A view on whether we can help, and a sensible first step
Before any work: A written proposal covering scope, team, approach and terms
Commitment: None until you approve that proposal

Start a project See how we work

Or call +1 302-464-5943 · Eastern Time (ET)

Self-⁠service, safe releases, traceable spend

Self-⁠service within guardrails

One tested path to production

Reliability you can measure

Spend tied to teams and services