We Simplify Cloud Migration Assessment for Seamless Transition

What if a disciplined first step could cut uncertainty, speed timelines, and protect your business outcomes? We ask this because roughly one-third of initiatives fail outright, and another quarter miss deadlines or expectations.

We begin with a focused assessment that maps your applications, infrastructure, dependencies, and governance so teams see risks early. This clarity lets us design target architectures and a sequenced plan that executives can approve with confidence.

Our approach pairs automated analysis with stakeholder interviews to capture undocumented details, and we integrate security, compliance, and continuity from day one. The result is quantified risks with mitigation owners, right-sized resources, and KPIs that prove success after the move.

We work alongside your teams, transferring knowledge and handing off artifacts that are ready for action. In short, a structured assessment turns complexity into a clear path forward, reducing rework and keeping delivery on track.

Key Takeaways

• Structured first step: a focused review reduces uncertainty and accelerates timelines.
• Real market risk: one-third fail and 25% miss targets, so planning matters.
• Full visibility: we capture architecture, code, databases, and dependencies.
• Actionable outputs: target options, sequenced plans, and owned mitigations.
• Business-aligned: we balance technical depth with practical outcomes and clear KPIs.

Why a How-To cloud migration assessment matters right now

Acting with a clear process now saves time, reduces risk, and protects services. Without a proven strategy, teams misallocate resources, face cost overruns, and trigger operational outages when moving interdependent systems.

A thorough review outlines a safe path, identifies IaaS, PaaS, and SaaS fits, and guides provider selection. It also surfaces vendor lock-in and skills gaps so we can recommend targeted enablement before any change.

We translate business drivers—scalability, resilience, and cost control—into a practical plan with KPIs, rollback steps, and disaster recovery criteria. Early cost insight prevents overspending on oversized instances and wasted services while keeping required performance.

Our approach favors iterative pilots that prove value, then scale in waves. That method maps dependencies up front, reduces schedule slip, and limits service interruption. Cross-functional alignment with IT, security, finance, and product owners keeps the effort governed and measurable.

What a cloud migration assessment covers from end to end

We map technical dependencies and business priorities, so every activity links to clear objectives and measurable outcomes. This stage defines which applications and services drive value, acceptable downtime, and the constraints that shape our approach.

Outputs are concrete: target architecture patterns, a prioritized plan with migration waves, and a risk register that gives decision-makers full visibility.

Align objectives and scope

We translate executive goals into scope, grouping workloads by value and risk, and we set success criteria tied to business KPIs and technical SLAs.

Define tangible outputs

Deliverables include a validated target architecture, a sequenced plan and timeline, a complete inventory of workloads and supporting infrastructure, and a test track with proofs of concept to confirm choices and estimate TCO.

Choose platform scope

We evaluate on-premises modernization, multicloud trade-offs, or a Google Cloud landing zone, selecting tools for automated discovery and owner interviews to validate data.

• Inventory: apps, servers, IAM, licensing
• Sequencing: grouped moves and decoupling targets
• Validation: pilots, training, and a living risk register

Discovery and inventory: map your current environment and workloads

Discovery starts with tooling and owner interviews to turn scattered facts into a single source of truth. We run automated scans, then validate results with stakeholders so the inventory is accurate and actionable.

Automated discovery uses Google Cloud Migration Center and Azure Migrate to enumerate servers, VMs, databases, and applications across on-premises and multicloud environments. We expand discovery to include CI/CD, source repos, artifact stores, scheduled jobs, and physical network appliances.

We capture non-technical constraints—licensing, data residency, deployment methods, and IAM patterns—because these details shape sequencing and risk. We also record network restrictions, IP needs, and how each service is exposed.

Visuals and central repository

We generate dependency graphs with tools like Cloudockit and store diagrams, spreadsheets, and Visio artifacts in a shared wiki or DevOps repo so teams can iterate during the assessment phase.

Item	Scope	Key data captured	Validation
Compute & applications	Servers, VMs, containers	OS, runtime, workload owners	Owner interviews, tooling
Supporting services	CI/CD, repos, message brokers	Endpoints, auth, exposure	Configuration review
Infrastructure & network	Firewalls, appliances, storage	Connectivity, IPs, requirements	Network diagrams, tests

Baseline performance, configuration, and security for right-sizing

A measured baseline of resource use, security settings, and response times creates a factual foundation for target design.

We collect time-series metrics—CPU, memory, disk I/O (reads/writes, IOPS), network throughput, and peak concurrency—to size services and validate SLAs after cutover.

We also record configurations: VM sizes and specs, OS versions, storage types and capacity, GPUs, autoscaling rules, and licensing entitlements so decisions reflect real requirements.

Inventorying identity and security is equally essential. We list service and user accounts, API keys, encryption (at rest and in transit), firewall rules, and IAM roles to map access policies without creating gaps.

How results drive choices:

• Match observed workload patterns to target services—managed DBs, serverless, or orchestration platforms—to improve performance and lower cost.
• Flag compatibility risks across OS, middleware, and frameworks and produce a prioritized remediation backlog for pre- versus post-migration fixes.
• Establish monitoring baselines for key transactions and background jobs so teams can compare before/after performance.

Baseline category	Key metrics captured	Config & licensing	Actionable outcome
Workload behavior	CPU, memory, IOPS, throughput, peak concurrency	VM size, autoscaling rules	Right-size instances and SLA targets
Storage & network	Read/write IOPS, bandwidth, latency	Storage type, capacity, tier	Choose tiered storage and reduce cost
Security & identity	Service accounts, API keys, encryption methods	Firewall rules, IAM roles, license constraints	Design secure access groups and compliance mapping
Compatibility	OS versions, middleware, framework gaps	GPU needs, licensing entitlements	Remediation backlog and pre-move blockers

Deliverable: a concise report that links performance baselines, config inventories, and security findings to target architecture recommendations and quantified cost impact, including options for Google Cloud where appropriate.

Dependency mapping to de-risk migration waves

Mapping runtime connections reveals hidden call paths and prevents surprises during waves of change. We combine APM, network monitoring, and owner interviews to build a fact-based map of how applications, databases, and messaging systems interact under real workloads.

Internal dependencies:

Internal dependencies: services, databases, messaging, and shared resources

We observe live traffic to show which services and databases must move together or keep low-latency links. This helps us group tightly coupled workload pairs so phased moves do not break transactions.

External integrations: SaaS, partner APIs, data pipelines, SLAs

We catalog every external integration—SaaS, partner APIs, ETL pipelines, and auth systems—capturing SLAs, data contracts, and retry semantics that tests must preserve during a cutover.

Centralize dependency data to plan waves and prevent breakage

All findings are stored in a structured repository with diagrams, metadata, and ownership. We validate undocumented links with SMEs and record scheduled jobs that tools might miss.

• Prioritize waves that preserve call paths and queue semantics to reduce risk.
• Define interim network and security rules to support hybrid connectivity while groups are split.
• Flag dependencies that need refactoring, balancing short-term safety with long-term simplification.

Outcome: clear dependency maps tie directly to reduced operational risk, test plans, and rollback criteria so stakeholders can approve wave sequencing with confidence.

Compliance and operational requirements that shape the target architecture

Compliance, recovery objectives, and environment rules set the guardrails for a secure target design. We translate legal mandates and business goals into concrete technical controls so design choices reflect both risk tolerance and operational needs.

Regulatory frameworks—GDPR, HIPAA, FedRAMP, ISO 27001, and SOX—drive decisions about region selection, data protection, encryption, access controls, and audit trails. We catalog applicable rules and map them to platform controls and evidence requirements.

Reliability and recovery objectives

We define SLAs, RPOs, and RTOs for each workload and embed them in backup, replication, and failover designs. Those objectives determine replication topology, retention policies, and testing cadences.

Environment classification and guardrails

Production, test, and development environments receive distinct guardrails for access, cost, and change velocity. We enforce least-privilege access, separate networks, and policy-as-code to keep controls consistent across environments.

• Controls mapping: regulations → region, encryption posture, audit logs.
• Recovery: SLAs, RPOs, RTOs drive replication and DR patterns.
• Environment rules: separate networks, dev guardrails, production hardening.
• ISV validation: verify vendor support, licensing impacts, and upgrade needs early.
• Operational processes: change, incident response, and traceable approvals baked into the target architecture.

We align identity, key management, logging, and network segmentation to meet security and compliance needs without slowing delivery. For guidance on cross-region compliance and technical controls, see our cross-region compliance guide.

Choose migration strategies, platforms, and assessment tools

We match each workload to a fit-for-purpose strategy, so moves deliver value and limit disruption.

We evaluate six approaches—rehost (lift-and-shift), replatform, refactor, relocate, repurchase (SaaS), and retain—and pick the option that best aligns to cost, timeline, and business goals.

Tools guide and validate decisions. We use Azure Migrate to automate inventory and configuration capture. AppCAT, CloudPilot, and CAST Highlight analyze code, flag compatibility blockers, and produce modernization recommendations.

For Google Cloud adoption, we build a catalog, run proofs of concept, estimate TCO, train teams, and validate the plan and timeline so stakeholders can approve with confidence.

Database work requires an inventory of engines and versions, a map of inbound/outbound dependencies, and a decision on shared instances versus splits to enable parallel waves and safe sequencing.

Focus	Primary tool	Key output	When to use
Discovery & inventory	Azure Migrate	Full inventory, dependency map	Start of planning and risk reduction
Code analysis	AppCAT / CAST Highlight / CloudPilot	Compatibility report, refactor list	When modernizing applications or languages
Platform validation	Google Cloud PoC	TCO, performance validation	Before large-scale waves or costly changes

Outcome: a documented plan, clear entry/exit criteria, and prioritized change lists that balance speed, cost, and operational management.

Plan for success: TCO, skills enablement, pilots, and a living risk register

We quantify the true cost of the target environment so decisions rest on numbers, not guesses. Our approach ties resource plans to measured workload baselines and rightsizing assumptions. The result is an evidence-based model of total cost of ownership that leadership can trust.

We optimize spend by matching reserved or committed usage to steady-state workloads and by selecting managed services that reduce operational effort. This lowers ongoing cost and simplifies operational management.

Skills come next: we design role-based training paths using Google Cloud Skills Boost and hands-on proofs of concept. Teams run controlled PoCs to validate designs and build confidence before broader waves.

Pilot, measure, and manage risk

We craft pilots that cover stateful and stateless patterns, batch and interactive flows, and representative data volumes. Each pilot has KPIs for downtime, throughput, and user experience, with monitoring wired for early warning and post-cutover validation.

Risk management is continuous. We maintain a living risk register with mitigation owners, dates, and status so leadership sees progress and unresolved issues at a glance.

Area	Deliverable	Owner	Gate
Cost & rightsizing	Evidence-based TCO model	FinOps lead	Budget sign-off
Skills & enablement	Role-based training + PoC results	Learning lead	Training completion
Pilots & KPIs	Pilot reports, monitoring dashboards	Delivery lead	Performance targets met
Risk & recovery	Living risk register, rollback plans	Risk owner	Mitigations assigned

• Migration blueprint: timelines, roles, communications, escalation, and DR procedures packed into a single plan.
• Gated progress: we only scale waves after training and pilot success to protect services and revenue.
• Business alignment: every metric links to objectives so the plan demonstrates value early.

Conclusion

A data-driven plan turns inventory, tests, and stakeholder input into an executable playbook for moving workloads safely and predictably.

We deliver a validated plan that bundles inventory, categorized workloads, PoCs, training, and TCO estimates so leaders approve a realistic timeline. The plan includes a living risk register, KPIs, acceptance criteria, and rollback steps tied to architecture, databases, and network sequencing.

Tools and processes—discovery, code analysis, and testing—are integrated to keep velocity while protecting performance and compliance. Teams leave the assessment ready to act, with trained staff and clear resources. Proceed with confidence: we remain your collaborative partner as changes begin and outcomes are measured for success.

FAQ

What is the purpose of a cloud migration assessment and why does it matter now?

A migration assessment aligns business objectives with a technical plan, revealing target architecture, risks, and cost implications so organizations can move workloads with confidence; with rising regulatory pressure, tighter budgets, and rapid digital initiatives, this step prevents surprises and accelerates safe adoption of Google Cloud or multicloud platforms.

What end-to-end outcomes should we expect from an assessment?

The assessment delivers a prioritized inventory of applications and services, a target architecture design, a phased migration plan with wave sequencing, a risk register, and a cost model that estimates total cost of ownership and ongoing operational spend for the chosen platform.

How do you discover and inventory our environment and workloads?

We use automated discovery tools such as Google Cloud Migration Center and Azure Migrate alongside manual validation to catalog servers, applications, databases, network elements, and appliances, and we capture non-technical constraints like licensing, data residency, and deployment processes.

What performance and configuration data do you collect to right-size targets?

We gather CPU, memory, IOPS, throughput, and peak-load metrics, record VM sizes, OS versions, storage types, and autoscaling settings, and combine those with security and identity inventories so target services are sized for performance, resiliency, and cost efficiency.

How do you map dependencies to reduce migration risk?

We map internal service-to-service links, databases, messaging systems and shared resources, and catalog external integrations such as SaaS connectors and partner APIs; centralizing dependency data enables wave planning that avoids functional breakage during cutover.

Which compliance and operational requirements influence the target architecture?

Regulatory frameworks like HIPAA, GDPR, FedRAMP, and ISO 27001 drive data handling and controls, while reliability objectives such as SLAs, RPOs, and RTOs determine recovery patterns; environment classifications for production, test, and development set guardrails for permissions and change processes.

What migration strategies do you recommend for different workloads?

We apply fit-for-purpose strategies—rehost for lift-and-shift simplicity, replatform to gain managed services, refactor for cloud-native benefits, relocate for data gravity cases, repurchase where SaaS is better, and retain when modernization isn’t justified—selecting the approach that balances risk, cost, and business value.

Which assessment tools and tasks do you use for Google Cloud?

We use tools like Google Cloud Migration Center for discovery and cataloging, TCO calculators for cost estimates, and run experiments and validation tests to confirm performance; we also employ code-analysis and third-party tools where needed to assess application readiness.

How are databases evaluated and sequenced during the assessment?

We inventory database topologies, assess shared instances versus split architectures, analyze data size and I/O patterns, and define sequencing and cutover strategies to minimize downtime and preserve transactional integrity during moves.

How do you estimate total cost of ownership and optimize resource plans?

We combine current operational spend with forecasted cloud costs—including compute, storage, network egress, and managed services—apply right-sizing and committed-use options, and model multiple scenarios to produce a TCO that supports budgeting and executive decision-making.

What steps do you take to close skills gaps and ensure operational readiness?

We recommend role-based training, hands-on labs, and proofs of concept on Google Cloud, define runbooks and operational playbooks, and set up governance and monitoring to transfer skills to internal teams while reducing dependence on external support.

How do pilots, KPIs, and a living risk register contribute to success?

Pilot migrations validate assumptions under real conditions, KPIs measure performance, cost, and reliability against objectives, and a living risk register assigns owners and mitigations, ensuring issues are tracked and resolved throughout the program lifecycle.

Can you assess hybrid or multicloud environments as part of the plan?

Yes, we evaluate on-premises, Google Cloud, and other public cloud footprints, define landing zones and networking models, and recommend hybrid architectures or multicloud patterns where they better meet business, security, or compliance needs.