Technology choices
Choose platforms through the responsibilities they create.
Compare workload fit together with skills, controls, data movement, reliability, supplier dependency, lifecycle, transition, and exit.
Technology context
Familiarity is useful; fit is the decision.
These names describe technologies the service model can accommodate. They do not assert a formal partnership, certification, recommendation, or endorsement.
Cloud and infrastructure
Foundations assessed through workload boundaries, resilience, identity, network, policy, cost, and service ownership.
AWS
Cloud services considered for workload foundations, data, integration, identity, resilience, and operations according to architecture fit.
Azure
Cloud and identity services used where Microsoft estate integration, governance, data, application, and operating context support the choice.
Google Cloud
Cloud platform capabilities evaluated for data-intensive, application, container, and analytical workloads with explicit operating consequences.
Cloudflare
Edge delivery and security capabilities considered for web performance, traffic policy, application protection, and network-boundary needs.
Data and analytics
Platforms considered through source contracts, meaning, quality, lineage, access, usage, and product ownership.
Snowflake
Cloud data-platform capabilities used for governed storage, transformation, sharing, and analytical workloads where its operating model fits.
Databricks
Data and AI platform capabilities considered for lakehouse, engineering, analytical, and model workflows with governance and ownership in scope.
dbt
Versioned SQL transformation, testing, lineage, and documentation practices used to make analytical data logic more reviewable and operable.
PostgreSQL
Open-source relational database capabilities used for transactional and analytical services that benefit from mature SQL and extension support.
AI and machine learning
Methods selected through decision fit, evaluation, oversight, data readiness, change, and operational evidence.
TensorFlow
Machine-learning framework considered for model development and serving where team capability, workload needs, and lifecycle responsibilities align.
PyTorch
Machine-learning framework used for experimentation and production workflows when its model ecosystem and engineering approach fit the use case.
LangChain
Application orchestration components considered for bounded language-model workflows with evaluation, observability, and provider abstraction requirements.
scikit-learn
Python machine-learning tools used for transparent baselines and predictive methods when simpler models suit the decision and available data.
Applications and integration
Runtimes and frameworks used within accessible journeys, domain boundaries, contracts, and maintainable delivery.
Next.js
React framework capabilities used for server-rendered and statically generated web experiences with routing, metadata, and performance needs.
React
Component-based interface capabilities used to build accessible application journeys with deliberate state, rendering, and interaction boundaries.
Node.js
JavaScript runtime used for web services, integration, and tooling where its concurrency model and shared language ecosystem are appropriate.
TypeScript
Static typing used to make application and integration contracts more explicit across JavaScript delivery and maintenance workflows.
Delivery and observability
Tooling that shortens useful feedback and connects change evidence with service behavior and ownership.
Kubernetes
Container orchestration capabilities considered when workload scale, isolation, platform ownership, and lifecycle needs justify the operating complexity.
Docker
Container packaging used to make application runtime dependencies more repeatable across build, test, release, and operating environments.
Terraform
Infrastructure-as-code workflows used to review, version, validate, and promote cloud and platform resource changes across environments.
GitHub Actions
Repository automation used for build, test, security, artifact, and release evidence when it fits the delivery and control environment.
Security and governance
Controls chosen around credible risk, identity, data, interfaces, detection, response, and reviewable exceptions.
HashiCorp Vault
Secrets and identity capabilities considered for controlled credential issuance, storage, rotation, audit, and workload access patterns.
Wazuh
Security monitoring capabilities considered for endpoint, integrity, configuration, log, and detection use cases with owned response workflows.
Kong Gateway
API gateway capabilities used for identity, traffic policy, routing, observability, and consumer-aware interface operations where appropriate.
Snyk
Developer security tooling considered for dependency, code, container, and infrastructure checks integrated with owned remediation workflows.
Evidence boundary
Platform familiarity is not alliance evidence.
A formal alliance, tier, certification, or marketplace claim belongs here only after verification and approved use of the relevant public asset.
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