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Platform & Cloud · FOUNDATION + SKYWAY

Platform & Cloud.

Cloud architecture, DevOps and SRE, migrations, and data engineering, built by senior platform engineers who design for cost, security, and the on-call rotation. AWS, Azure, GCP, hybrid, and on-prem where sovereignty requires it.

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Sub-services
4
Department
FOUNDATION + SKYWAY

The problem

Most cloud estates are a lift-and-shift held together with cron jobs and Slack channels.

The shape of the failure is familiar: a cloud migration that promised modernization but produced an elaborate VM hosting bill; an IaC repo that drifts from production after the third quarter; an SRE team that inherited the worst parts of the application without the authority to refactor them; a data warehouse that nobody trusts, plus a parallel data lake that nobody can find. The cloud part isn't the hard part. The platform part is.

FOUNDATION (data) and SKYWAY (cloud), together with NEXUS, run platform and cloud engagements end-to-end. We design before we build, write the IaC that owns production, instrument observability that on-call can actually use, and treat cost as a first-class architectural concern. Cloud migrations land with documented rollback paths; SRE engagements ship with named SLOs; data engineering work produces lineage that catalogs survive on. The bench you see in the proposal is the bench in production.

What we deliver

4 capabilities, one bench.

Cloud architecture, DevOps, SRE, migrations, data engineering.

How we engage

4 phases, in writing.

The methodology shows up in the statement of work, not as slogans, but as deliverables, owners, and acceptance criteria.

  1. 01

    Architecture and cost ceiling

    Two-week design phase: account topology, network boundaries, identity model, IaC structure, observability stack, deployment flow, and cost ceiling per workload. Output is an Architecture Decision Record that becomes the contract for the build phase.

  2. 02

    IaC-first build

    Every resource lives in Terraform, Pulumi, or CDK from day one. No clickops, no untracked drift. Module library that subsequent workloads inherit. Promotion through dev → staging → prod via CI gates with policy-as-code (OPA / Conftest) checks at every promotion.

  3. 03

    Observability and SLOs

    Structured logging, metrics, distributed tracing, and error budgets shipped alongside the application. Named SLOs in the SOW. Runbooks per signal class. On-call rotation handoff with explicit pager-volume targets and quarterly SLO reviews.

  4. 04

    Cost and posture as recurring practice

    Monthly FinOps reviews against the cost ceiling. Quarterly security posture reviews against the framework that fits the workload (SOC 2, HIPAA, PCI-DSS, NERC CIP). The platform compounds value over quarters. It doesn't decay quietly between launches.

Capabilities

What’s in scope.

  • Cloud architecture: AWS, Azure, GCP, hybrid, and on-prem extensions
  • Multi-account / multi-subscription topology, network segmentation, transit
  • Infrastructure as code: Terraform, Pulumi, CDK, with module libraries
  • CI/CD pipelines with policy-as-code gates (OPA, Conftest, Checkov)
  • Observability: OpenTelemetry, Datadog, Honeycomb, Grafana, Prometheus
  • SRE and on-call: named SLOs, error budgets, runbooks, pager rotation
  • Cloud migrations: lift-shift, replatform, and rearchitect, documented
  • Data engineering: lakehouse, warehouse, ELT, data quality, lineage, BI

Stack

Tools we use in production.

Clouds
AWSAzureGCPCloudflareOCI
IaC & GitOps
TerraformPulumiAWS CDKArgo CDFlux
Containers & runtime
KubernetesEKSAKSGKEECSNomad
Observability
OpenTelemetryDatadogHoneycombPrometheusGrafana
Data platform
SnowflakeDatabricksBigQueryRedshiftdbtAirflow
Security & policy
OPAConftestCheckovVaultAWS GuardDuty

Selected work

Quantified outcomes, not adjectives.

All case studies

Common questions

Asked before the first call.

  1. 01

    Are you cloud-agnostic, or do you push a specific provider?

    Agnostic in principle, opinionated in practice. We design the platform so the architectural patterns survive a future cloud move, but we recommend the cloud (and services within it) that fits your workload, regulatory frame, and existing skills. AWS leads our portfolio by volume, Azure leads in regulated and Microsoft-shop environments, GCP leads where data + AI workloads dominate. We tell you why we're recommending what we recommend, in writing.

  2. 02

    Can you operate the platform after we ship it?

    Yes: through Managed Services. Named SLOs, on-call coverage, monthly FinOps and posture reviews, and quarterly architectural reviews against the eval set we build during delivery. Or we hand off to your SRE team with a 90-day shadowing period and complete operational documentation. Either path is in the SOW from kickoff, not bolted on.

  3. 03

    How do you handle cloud cost?

    Cost is an architectural concern, not a procurement one. We design with explicit cost ceilings per workload, Reserved Instance / Savings Plan strategy, autoscaling shapes calibrated to real load patterns, and continuous tagging. Monthly FinOps reviews surface drift. Most clients see 25–50% cost reduction within six months of our engagement starting, while improving reliability, without offshoring or staffing changes.

  4. 04

    Do you support on-prem or hybrid deployments?

    Yes: when sovereignty, regulatory, or latency requirements demand it. We've shipped hybrid topologies for healthcare, government, and financial services clients, including air-gapped deployments. Self-hosted Kubernetes, on-prem databases, and edge inference are all in the playbook. We will also tell you honestly when an on-prem requirement is solving for the wrong constraint.

  5. 05

    What's your approach to cloud migration?

    Strangler-fig is the default. We assess what to lift-and-shift, what to replatform, what to rearchitect, and what to retire: then build the migration tooling (dual-writes, traffic shadowing, replay) that lets us cut over incrementally. Most engagements migrate workloads in 5–15% increments with documented rollback at every stage. Big-bang cutovers are reserved for cases where the substrate is unsalvageable, and we'll tell you when that's the case.

  6. 06

    How do you do data engineering?

    Lakehouse-first for analytical workloads (Snowflake, Databricks, BigQuery), with explicit data contracts at the producer / consumer boundary. ELT (Fivetran / dbt) over hand-rolled pipelines where appropriate. Data quality monitored continuously, lineage tracked end-to-end, governance integrated with your identity layer. We don't ship a data warehouse without a documented refresh cadence and an owner.

  7. 07

    What's the typical Platform & Cloud engagement budget?

    Architecture and ADR: 4–6 weeks, $50K–$150K. Platform build (foundation + first workload): 4–9 months, $400K–$1.5M. Multi-account modernizations or full data-platform programs: $1M–$5M+. Managed Services for ongoing operations: $30K–$150K monthly retainer. Brackets published honestly so visitors self-qualify before the first call.

Related practices

Often paired with Platform & Cloud.

Talk to us

Bring a platform & cloud problem. We’ll bring a senior engineer.

A senior engineer plus the FOUNDATION + SKYWAY department lead joins the first call. No discovery gauntlet, no junior reps, no obligation.

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