Application layer
Choose Cloud Run, GKE, Compute Engine or managed services around workload behavior.
GOOGLE CLOUD SOLUTIONS COMPANY
Google Cloud platforms built for modern applications, data and intelligent products.
AppsLoading designs, migrates and operates Google Cloud environments for digital products, analytics platforms and AI-enabled applications. From Cloud Run and GKE to BigQuery, Vertex AI, IAM, observability, DevOps and FinOps, we build cloud foundations that teams can scale and operate with confidence.
✓ Cloud architecture and migration✓ GKE, Cloud Run and serverless✓ BigQuery and data engineering✓ Vertex AI, security and FinOps
ApplicationCompute, containers and serverlessDataBigQuery pipelines and governanceOperationsSecurity observability and cost control
Cloud RunGKEBigQueryCloud SQLVertex AIPub/SubDataflowIAMCloud MonitoringTerraform
FROM CLOUD ACCOUNT TO PRODUCT PLATFORM
The cloud becomes valuable when infrastructure disappears into the product.
A Google Cloud project is not the architecture. Teams still need decisions around runtime, identity, networking, data ownership, deployment, observability, resilience and cost. We design those layers together so engineering can move faster without creating an operational mess.
RUNTIME
Data layer
Separate transactional, analytical and event data with clear ownership.
Security layer
Projects, IAM, secrets, service identities and network boundaries designed early.
Operations layer
CI/CD, metrics, logs, SLOs, alerts and cost visibility built into the platform.
GOOGLE CLOUD SERVICES
One engineering partner across architecture, migration, data, AI and operations.
Use AppsLoading for an end-to-end Google Cloud programme or a focused initiative such as a migration, GKE platform, Cloud Run workload, BigQuery foundation, AI application or FinOps review.
Google Cloud Architecture
Landing zones, projects, networking, identity, runtime, data and operating model designed around real product requirements.
FOUNDATIONCloud Migration & Modernisation
Assess, sequence and move workloads with clear dependency mapping, cutover planning and post-migration optimisation.
MIGRATECloud Run & Serverless
Containerised services, APIs, jobs and event-driven workloads without operating clusters.
SERVERLESSGoogle Kubernetes Engine
GKE platforms for teams that need Kubernetes control, multi-service workloads, policy and extensibility.
GKEBigQuery & Data Engineering
Ingestion, transformation, analytical models, governance, reporting and data products.
DATAVertex AI & GenAI Applications
Model access, evaluation, grounding, RAG, workflow integration and production AI product architecture.
AICloud Security & IAM
Identity, service accounts, secrets, network controls, logging, policy and access reviews.
SECURITYObservability, DevOps & FinOps
CI/CD, monitoring, logging, SLOs, incident readiness, billing visibility and continuous cost optimisation.
OPERATE
CLOUD RUN APPLICATION PLATFORM
Ship containerised services without owning the cluster.
Useful for APIs, web backends, workers, jobs and event-driven services where product teams want managed scaling and simpler operations.
CLOUD RUN & SERVERLESS
Make infrastructure smaller than the application problem.
Cloud Run is a fully managed application platform for code and containers. We use it where teams benefit from serverless operations, fast deployment and workload-based scaling without the platform overhead of Kubernetes.
Web services and APIs
Deploy stateless services behind secure, scalable endpoints.
Jobs and background processing
Run scheduled or asynchronous workloads with explicit execution behavior.
Event-driven architecture
Connect Pub/Sub, storage events and application events to managed services.
Developer velocity
Reduce infrastructure management so teams can focus on application logic.
GOOGLE KUBERNETES ENGINE
Use Kubernetes when the product genuinely needs platform-level control.
GKE is a managed Kubernetes platform. We design it for multi-service products, complex networking, custom scheduling, advanced policy, reusable internal platforms and teams that need Kubernetes capabilities beyond a serverless runtime.
Cluster model
Choose Autopilot or Standard around control, operations and workload requirements.
Workload boundaries
Namespaces, services, ingress, secrets and service identities planned deliberately.
Delivery automation
Image build, registry, deployment and release policy connected to CI/CD.
Operational readiness
Metrics, logs, alerts, autoscaling, upgrades and failure response built in.
LANDING ZONE & GOVERNANCE
Build the guardrails before projects and workloads multiply.
We structure organization, folders, projects, IAM, networks, environments and shared services so teams can grow without turning every new workload into a governance exception.
↓
Common services, secrets, CI/CD and operational controls.
Production, staging, development and data workloads with explicit ownership and boundaries.
Labels, budgets, reporting and accountability.
↓
GOOGLE CLOUD MIGRATION
Migration is a sequence of business and technical cutover decisions.
We map dependencies, data movement, downtime tolerance, security controls and target architecture before moving workloads. Not every application needs the same migration pattern.
Assess the estate
Applications, databases, integrations, traffic, dependencies and operational risk.
Select the target pattern
Rehost, replatform, containerise, move to managed services or redesign where justified.
Move data deliberately
Replication, cutover, validation, rollback and data ownership planned together.
Stabilise after cutover
Observe errors, latency, costs and operational behavior before decommissioning old systems.
BIGQUERY & DATA PLATFORM
Turn operational data into a governed analytical product.
BigQuery is a fully managed, AI-ready data platform. We design ingestion, transformation, warehouse models, governance, quality and reporting so analytics teams can move from disconnected exports to a dependable decision layer.
Ingest
Batch, streaming and application events enter through controlled interfaces.
Transform
Standardise, clean and model data around business entities and metrics.
Govern
Access, metadata, lineage and quality controls improve trust.
Consume
Dashboards, data products, ML features and ad hoc analysis use the same foundation.
ANALYTICS FOUNDATION
One governed path from raw events to decision-ready metrics.
BigQuery · Dataflow · Pub/Sub · governance · BI · ML
VERTEX AI & GENERATIVE AI
Build AI as a product system, not a model demo.
We connect model access, application context, grounding, evaluation, safety, observability and cost into the product architecture. The work around the model often matters as much as model choice.
Vertex AI and model APIs
Select models around quality, latency, cost and modality.
RAG and enterprise context
Connect models to product data and controlled knowledge sources.
Quality before launch
Test prompts, outputs, edge cases and acceptance criteria.
Monitor the AI layer
Track latency, failures, usage, safety and spend.
SECURITY, IAM & TRUST
Security should be part of the platform shape, not a launch checklist.
We design identity and access around people, workloads and service accounts. Network boundaries, secrets, logging, backups and policy are mapped to how teams actually operate the platform.
Least-privilege access
Groups, roles, service identities and workload access.
Explicit boundaries
Connectivity, private access and exposed surfaces.
Credential management
Remove long-lived secrets from code and deployments.
Traceable operations
Logging, monitoring and change visibility for sensitive systems.
OBSERVABILITY & RELIABILITY
Operate from user impact, not just infrastructure graphs.
Cloud Monitoring and related observability services help teams understand application health and performance. We structure metrics, logs, alerts and service objectives around the behaviors that actually matter to customers and operators.
Availability
SLO
Latency
p95 / p99
Errors
rate
Saturation
capacity
Cost
unit cost
DEVOPS & DELIVERY
Make every release repeatable, observable and reversible.
Cloud delivery works best when build, test, artifact, deployment, verification and rollback form one clear operating path.
Commit
Branch policy, review and change context.
Build
Repeatable build with explicit dependencies.
Artifact
Versioned images and trusted releases.
Deploy
Environment-aware automated rollout.
Verify
Health checks, metrics and smoke tests.
Operate
Rollback, alerts and post-release learning.
FINOPS & COST OPTIMISATION
Cloud cost should become an engineering signal, not a monthly surprise.
We connect billing data to services, environments and business usage. Cost work includes visibility, ownership, architectural tradeoffs and continuous optimisation rather than one-off instance downsizing.
Allocate spend
Projects, labels, billing exports and ownership make cost attributable.
Find waste
Idle resources, overprovisioning and expensive architecture patterns.
Measure unit economics
Relate cloud spend to users, transactions, jobs or data volume.
Optimise continuously
Architecture and usage evolve, so cost controls need an operating rhythm.
GOOGLE CLOUD DIRECTIONS
Different workloads need different cloud shapes.
The right design depends on product traffic, engineering maturity, data intensity, integration requirements, reliability targets and operational ownership.
PLATFORM MODERNISATION
Move from fragile infrastructure to managed, observable product platforms.
Runtime, delivery, data, security and operations redesigned together.
AI PRODUCTS
Connect models to data, evaluation and production application workflows.
Vertex AI, RAG, model APIs and operational guardrails.
CLOUD-NATIVE SAAS
Use managed runtimes to ship features without owning unnecessary infrastructure.
Cloud Run, managed databases and event-driven services.
ENTERPRISE PLATFORMS
Governance, shared services and reliable operations across multiple teams.
Landing zones, GKE, IAM, observability and FinOps.
ENGINEERING ENABLEMENT
Give developers clear paved roads instead of cloud ticket queues.
Reusable patterns, automation and platform tooling.
INDUSTRIES & USE CASES
Google Cloud for businesses where software, data and scale are strategic.
Cloud platforms can support digital products, analytics, AI and operational systems across many industries, but the workload shape and governance model change by context.
SAAS & DIGITAL PRODUCTS
Application platforms that can scale product usage without scaling platform toil.
Cloud Run, GKE, data, observability and automated delivery.
AI & RESEARCH
Data and model workflows for intelligent products.
Vertex AI, analytics and controlled experimentation.
FINTECH
Secure application and data platforms with strong operational visibility.
Identity, auditability, reliability and data controls.
COMMERCE & MARKETPLACES
Elastic application backends, analytics and event-driven operations.
Traffic bursts, integrations, data and customer journeys.
GLOBAL TEAMS
Cloud foundations that make distributed engineering easier to operate.
Standards, automation, observability and clear ownership.
GOOGLE CLOUD DELIVERY PROCESS
From assessment to operationally ready cloud platform.
The process keeps architecture, migration, security, delivery, observability and cost decisions visible from the beginning.
Assess
Review workloads, dependencies, data, users, security, traffic, cost and operational maturity.
DISCOVERYArchitect
Define landing zone, runtime, data, networking, IAM, resilience and operating model.
DESIGNBuild foundation
Implement infrastructure as code, shared services, observability and deployment paths.
PLATFORMMigrate or modernise
Move workloads in controlled waves and validate behavior against the target design.
DELIVERYValidate production
Test availability, security, data, performance, recovery, alerts and cost behavior.
QUALITYOperate and optimise
Improve reliability, developer experience, spend and architecture as usage changes.
OPERATIONSWHAT YOU RECEIVE
A Google Cloud foundation your teams can build on, operate and explain.
01 · PLATFORM BLUEPRINT
Documented target architecture
Projects, runtime, networking, IAM, data, integrations, observability and operational ownership mapped clearly.
Infrastructure as code
Reusable, versioned infrastructure patterns for repeatable environments.
Migration runbook
Dependencies, waves, cutover, validation and rollback expectations.
CI/CD and release model
Build, artifact, deployment, verification and recovery paths.
Security and IAM model
People, service identities, secrets, access and audit controls.
Observability and FinOps
Metrics, alerts, logs, SLOs, billing visibility and optimisation routines.
WHY APPSLOADING
Cloud engineering that connects product delivery with operational reality.
We treat cloud architecture as a product system. The design has to work for developers, operations, security, finance and the end users depending on the platform.
Workload-led architecture
Services are selected around behavior, not because they are fashionable.
Managed-service bias
Reduce operational surface area where Google Cloud can own it safely.
Platform and data together
Application, data and AI decisions are designed as one operating system.
Production before launch
Observability, IAM, cost and recovery are planned before the first incident.
What Google Cloud services do you work with?
We work across application runtimes, containers, serverless, databases, storage, data engineering, AI, networking, security, observability, DevOps and cost management depending on the workload.
Can you design a Google Cloud landing zone?
Yes. We can define organization, folders, projects, IAM, networks, shared services, logging, billing structure and environment boundaries before workload migration or new product development.
Can you migrate existing systems to Google Cloud?
Yes. We assess applications, databases, integrations and operating constraints, then define migration waves, target patterns, cutover, validation and rollback requirements.
When should we use Cloud Run?
Cloud Run is often a strong fit for web services, APIs, jobs and event-driven containers when you want a fully managed application platform without operating Kubernetes clusters.
When should we use GKE instead?
GKE is useful when you need Kubernetes-level control, multi-service platform patterns, custom networking or scheduling, policy, or an internal container platform across multiple teams.
Can you help choose between Cloud Run, GKE and Compute Engine?
Yes. We compare workload type, operational ownership, networking, portability, performance, scaling, security and team maturity before selecting a runtime.
Can you build a BigQuery data platform?
Yes. We can design ingestion, transformation, warehouse models, access, data quality, governance, analytics and reporting foundations around BigQuery and related Google Cloud services.
Can you build AI applications with Vertex AI?
Yes. We can integrate model APIs, grounding, RAG, evaluation, workflow logic, application context, monitoring and cost controls into production AI experiences.
Can application data and analytics share one platform?
They can share governance and integration patterns, but transactional systems and analytical workloads usually benefit from explicit responsibilities and fit-for-purpose storage.
Can you improve Google Cloud security and IAM?
Yes. Work can include IAM structure, service accounts, secret handling, network exposure, logging, project boundaries, access review, backup and operational controls.
Do you set up monitoring and alerting?
Yes. We can structure metrics, logs, dashboards, alerting, uptime checks and service objectives around application and infrastructure behavior.
Can you help with disaster recovery and reliability?
Yes. We can review recovery objectives, redundancy, backups, failure modes, dependencies, runbooks and recovery testing based on workload criticality.
Can you optimise Google Cloud cost?
Yes. We review billing visibility, resource usage, architecture choices, environments, storage, networking and unit economics, then prioritize changes that preserve required service levels.
Do you provide ongoing Google Cloud support?
Yes. Support can include monitoring, incident response, security, cost optimisation, upgrades, data operations, platform maintenance and planned improvements.
Who owns the cloud configuration and documentation?
Your team retains ownership of the project code, infrastructure configuration and documentation created for the engagement, subject to the agreed project terms.
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