Cloud engineering services across the AWS workload lifecycle
Eight service lines delivered by senior engineering teams. Engagements typically begin with an assessment or a fixed-scope proof of concept and progress to production build and managed operations.
Cloud Migration & Modernization
Structured migration of on-premises, VMware and hosted estates to AWS, with modernization where it delivers measurable value.
Rabbit Cloud delivers migrations in defined waves against a validated dependency map. Each workload receives a disposition under the AWS 7R framework, a target architecture and a cutover plan with rollback criteria. Landing zone, network and security foundations are established before the first workload moves.
Scope of service
- Portfolio discovery, application dependency mapping and 7R disposition
- Multi-account landing zone on AWS Control Tower with security guardrails
- Wave-based migration using AWS MGN, AWS DMS and native replication tooling
- VMware exit, Windows Server and SQL Server modernization, licence optimisation
- Containerisation to Amazon ECS or EKS, database migration to Amazon Aurora and RDS
- Cutover rehearsal, hypercare and post-migration optimisation
Deliverables
- A wave plan with accountable owners and scheduled cutovers
- A landing zone that satisfies internal security and audit requirements
- A validated run-rate model prior to migration
AWS services and tooling
DevOps & Platform Engineering
Internal developer platforms, infrastructure as code and delivery pipelines built to enterprise standards.
We design and implement the platform layer that engineering organisations depend on: reproducible environments defined in code, pipelines with automated quality and security gates, and self-service provisioning that removes infrastructure from the critical path of product delivery.
Scope of service
- Infrastructure as code with Terraform or AWS CDK, modular and peer-reviewed
- CI/CD on AWS CodePipeline, GitHub Actions or GitLab with progressive delivery
- Kubernetes on Amazon EKS with GitOps, autoscaling and cost-aware node strategy
- Environment provisioning, preview environments and ephemeral test stacks
- Secrets, configuration and artefact management to a defined standard
- DORA metrics baseline with agreed targets for lead time and change failure rate
Deliverables
- Deployment lead time measured in minutes
- A single governed path from development to production
- Infrastructure changes subject to the same review as application code
AWS services and tooling
Cloud Operations & SRE
Managed operations for production AWS workloads, governed by service level objectives.
Rabbit Cloud operates client environments against agreed SLOs with 24x7 monitoring and incident response. Observability is established first, alerting is tied to customer impact, and runbooks are maintained as controlled documents. Operational performance, spend and risk are reported monthly.
Scope of service
- Observability implementation: metrics, logs, traces and synthetic monitoring
- SLO definition, error budgets and alert rationalisation
- 24x7 monitoring, incident management and post-incident review
- Patch management, backup, disaster recovery design and scheduled restore testing
- Capacity planning and performance engineering
- Monthly operational review covering availability, spend and risk
Deliverables
- Alerting aligned to service impact
- Recovery objectives validated through scheduled testing
- A single operational report suitable for executive review
AWS services and tooling
Generative AI & Machine Learning
Production AI and ML systems on Amazon Bedrock and SageMaker, delivered with evaluation, governance and cost control.
AI initiatives most often fail on evaluation, governance and unit economics rather than model capability. Rabbit Cloud addresses these from the first week of an engagement: use cases are scored for value and feasibility, accuracy is measured against a client-specific evaluation set, and cost per transaction is modelled before build.
Scope of service
- Use-case assessment scored on business value, feasibility and data readiness
- Retrieval architecture with vector search, chunking strategy and evaluation harness
- Agentic workflows with tool use, human approval gates and deterministic fallbacks
- Model selection and cost-per-transaction modelling across Bedrock model families
- Guardrails, PII handling, prompt injection defence and audit logging
- Custom models: fine-tuning, distillation and inference optimisation on SageMaker
Deliverables
- A measured accuracy baseline prior to rollout
- Cost per document, call or query defined in advance
- A documented path from proof of concept to production
AWS services and tooling
Data Engineering & Analytics
Governed data platforms on AWS that serve analytics, reporting and AI workloads from a single source.
We build ingestion, modelling and governance layers on Amazon S3 and open table formats, so that business intelligence and machine learning consume the same governed datasets. Data quality, lineage and freshness are delivered as monitored controls rather than documentation.
Scope of service
- Ingestion from SaaS, transactional and streaming sources with schema management
- Lakehouse on Amazon S3 with Apache Iceberg, AWS Glue Data Catalog and Lake Formation
- Warehouse and serving layer on Amazon Redshift Serverless or Amazon Athena
- Data quality, lineage and freshness monitoring as first-class deliverables
- Semantic layer and governed dashboards in Amazon QuickSight
- Feature stores and retrieval-ready datasets for downstream AI
Deliverables
- A single catalog with agreed metric definitions
- Freshness and quality visible to consumers
- Datasets that serve analytics and AI without duplication
AWS services and tooling
Cloud Security & Compliance
Well-Architected reviews, security architecture and compliance readiness for regulated workloads on AWS.
Rabbit Cloud assesses environments against the AWS Well-Architected Framework, prioritises findings by exploitability and business impact, and remediates alongside client teams. Evidence collection is automated to support SOC 2, HIPAA, ISO 27001 and PCI DSS programmes.
Scope of service
- Well-Architected Review across all six pillars with a scored findings register
- Identity architecture: AWS IAM Identity Center, least privilege and permission boundaries
- Threat detection and response: Amazon GuardDuty, AWS Security Hub and automated remediation
- Data protection: AWS KMS key strategy, Amazon Macie discovery, encryption in transit and at rest
- Network segmentation, AWS WAF, AWS Shield and egress control
- Control mapping and evidence collection for SOC 2, HIPAA, ISO 27001 and PCI DSS
Deliverables
- Findings remediated with supporting evidence
- Audit-ready control mapping
- Guardrails that remain effective as the organisation scales
AWS services and tooling
FinOps & Cost Optimization
Sustained reduction of AWS spend through allocation, rightsizing, commitment strategy and architectural change.
Cost optimisation begins with allocation: every dollar of spend is attributed to a product, team or customer. Rightsizing, commitment purchasing and architectural change follow in sequence, with savings tracked monthly against an agreed baseline and reviewed jointly with engineering and finance.
Scope of service
- Tagging standard and cost allocation model, including shared-cost treatment
- Rightsizing, AWS Graviton migration and storage tiering
- Savings Plans and Reserved Instance strategy with coverage targets
- Idle and orphaned resource reclamation, non-production scheduling
- Unit economics: cost per tenant, per transaction, per model invocation
- Monthly FinOps review with engineering and finance stakeholders
Deliverables
- Full attribution of spend to an accountable owner
- Savings tracked monthly against a baseline
- Unit cost that declines with scale
AWS services and tooling
AWS Programme Enablement
Structuring engagements to AWS partner programme requirements so that eligible clients reduce the cost of assessment, proof of concept and migration.
AWS operates partner programmes that can offset a portion of the cost of qualifying assessment, migration, modernization and AI engagements. Rabbit Cloud structures eligible engagements to programme requirements and manages the associated process with the AWS account team, so that the commercial benefit is realised without adding administrative burden to the client.
Scope of service
- Assessment of workload alignment with applicable AWS partner programmes
- Coordination with the client's AWS account team throughout the engagement
- Engagement scope and documentation structured to programme requirements
- Outcome reporting on completion, to programme standards
Deliverables
- Reduced client cost on qualifying engagements
- A single point of coordination with AWS
- No additional administrative effort for the client
AWS services and tooling
Engagement models
Engagements are staged so that scope, cost and outcomes are defined before each commitment. Clients are not asked to commit to a production build ahead of a completed assessment or proof of concept.
| Model | Duration | Deliverables | Commercials |
|---|---|---|---|
| Discovery workshop | 2 to 5 days | Dependency map, risk register, prioritised workload shortlist and initial cost view. | Fixed fee |
| Assessment | 2 to 4 weeks | Well-Architected review, target architecture, migration or AI readiness plan, business case. | Fixed fee; may qualify for AWS programme support |
| Proof of concept | 4 weeks | Working software against success criteria agreed before commencement, plus the production build plan. | Fixed scope and price |
| Build | 8 to 24 weeks | Production platform delivered by a dedicated team, with client engineers embedded where required. | Milestone or T&M |
| Operate | Ongoing | SLO-governed operations, 24x7 response, patching and disaster recovery, monthly FinOps and quarterly architecture review. | Monthly retainer |
Discuss your requirements with our team
A discovery workshop establishes scope, risk and a recommended approach before any commitment to build.