Cloud Migration Checklist for B2B Teams in 2026

Gartner has tracked cloud migration failure rates for years. The number that keeps surfacing: roughly half of all migrations either run significantly over budget, miss their timeline by more than three months, or require partial rollback within the first year. The pattern in post-mortems is consistent — teams underestimated dependency complexity or made provider selection decisions based on compute pricing alone, ignoring egress costs that later doubled their monthly bill.

This checklist is structured around the decision points where B2B migrations actually go wrong. It’s sequenced to be used as a working document, not a reference artifact that sits in Confluence.

Phase 1 — Discovery and Workload Classification

This phase cannot be rushed. Every hour saved here is typically recovered tenfold in re-architecture costs during execution.

Inventory Every Workload and Its Dependencies

Start with a service dependency map, not a server list. A server list tells you what exists. A dependency map tells you what breaks if you move something in the wrong order.

For each workload, record:

• Runtime dependencies — databases, message queues, internal APIs, third-party services
• Network dependencies — which services communicate with each other, at what volume, and across which network boundaries
• Data residency requirements — does this workload process data subject to GDPR, HIPAA, SOC 2, or industry-specific regulation that constrains where it can run?
• Latency sensitivity — is this workload user-facing with sub-100ms requirements, or is it a background batch process that tolerates seconds?

Tools like AWS Migration Hub, Azure Migrate, and open-source options like CloudSploit or Netdata can accelerate discovery, but they don’t replace human review of what the dependency graph actually means for migration sequencing.

Classify Workloads into Migration Patterns

The standard classification — Rehost, Replatform, Refactor, Retire, Retain — still holds in 2026, but AI workloads add a sixth category worth naming explicitly: Rehost with GPU uplift. This applies to workloads that currently run on CPU-only infrastructure but will be migrated to a cloud platform with GPU AI managed services to unlock inference acceleration or model training capabilities that weren’t available on-premises.

Misclassifying a workload as a simple rehost when it’s actually a refactor is one of the most common root causes of migration overruns. Be precise.

Phase 2 — Provider Selection Criteria That B2B Teams Often Miss

Egress Pricing Is Not a Line Item — It’s a Structural Cost

The default instinct is to compare compute and storage pricing. Those numbers are easy to find and easy to model. Egress pricing is harder to model because it depends on your architecture — but it’s often where the financial case for a migration quietly collapses.

For a B2B SaaS platform processing 50TB/month of data with multi-region replication, egress fees on the major hyperscalers can run $4,500–$7,000/month at standard rates. Multiply that by 12 and it’s a meaningful portion of your annual infrastructure budget that wasn’t in the business case.

Evaluating a no egress fee cloud provider as either a primary or secondary cloud is a legitimate financial decision, not a compromise. For workloads with high inter-region data movement — analytics pipelines, database replication, large-scale media processing — the architectural flexibility that comes from not paying per-gigabyte to move your own data across regions has real compounding value.

GPU Availability and AI Managed Services

For B2B teams with active AI product roadmaps, GPU access is now a provider selection criterion alongside SLA and compliance certifications. The relevant questions to ask providers:

• What GPU instance types are available on-demand versus reserved only?
• Do you offer managed inference endpoints, or is GPU infrastructure self-managed?
• What’s the cold-start time for GPU instances under your auto-scaling policies?
• Is there a cloud platform with a GPU offering that abstracts cluster management, so ML engineers can focus on model deployment rather than infrastructure?

The distinction between raw GPU compute and managed AI services matters for team sizing. A small ML team deploying inference at scale needs managed abstractions. A large team building custom training pipelines may prefer raw GPU access with full control.

Hybrid Cloud Deployment Architecture

Not every workload belongs in public cloud. For B2B teams in regulated industries — financial services, healthcare, defense contracting — hybrid cloud deployment is not a transitional state. It’s the permanent target architecture.

A well-designed hybrid cloud solution keeps sensitive data processing and regulated workloads on-premises or in a private cloud segment, while running compute-intensive, latency-tolerant, or burstable workloads on public cloud infrastructure. The interconnect between these environments needs to be a dedicated private link (AWS Direct Connect, Azure ExpressRoute, or equivalent), not a VPN tunnel — VPN latency and throughput ceilings will become a production constraint within months of go-live.

When evaluating providers for hybrid architectures, ask specifically about:

• Native support for hybrid identity federation (not just SAML integration, but full RBAC parity between cloud and on-premises)
• Consistent control plane tooling across environments (Kubernetes, Terraform provider support, observability agents)
• SLA applicability — do SLAs cover the interconnect, or only the cloud-side components?

Phase 3 — Security and Compliance Architecture

Map Your Compliance Requirements Before Choosing a Region

SOC 2 Type II, ISO 27001, HIPAA, PCI DSS — each imposes different requirements on where data can be stored, who can access it, and what audit logging must be retained. These requirements should drive region and availability zone selection, not be retrofitted after architecture decisions are made.

For multinational B2B operations, data sovereignty is increasingly a board-level concern. The EU’s Schrems II implications, combined with evolving national data localization requirements in markets like India, Brazil, and Saudi Arabia, mean that “we’ll deploy in a nearby region” is no longer sufficient due diligence.

Zero Trust Network Architecture Is the Baseline, Not the Advanced Option

Migrating to cloud and maintaining perimeter-based network security is operationally worse than either staying on-premises or going full zero trust. Cloud environments are perimeter-less by design. Treating them as if they have a defensible edge leads to over-broad security group rules, lateral movement risk, and audit findings.

Zero trust implementation for cloud migrations means: per-workload identity (service accounts with minimum viable permissions), network microsegmentation, mutual TLS between services, and short-lived credentials managed through a secrets manager rather than static API keys embedded in configuration.

This is also the phase where you establish your SIEM integration, cloud-native threat detection (AWS GuardDuty, Azure Defender, or equivalent), and incident response runbooks specific to the new cloud environment. Do not migrate into an environment without these in place.

Phase 4 — Data Migration Sequencing

Migrate Data Before Migrating Compute

The most reliable migration pattern moves data first, validates it, and only then switches compute to point at the new data layer. The reverse pattern — migrating compute while data is still on-premises — creates a hard dependency on the interconnect during cutover and amplifies the blast radius of any migration issue.

For large datasets, use provider-native data transfer services (AWS DataSync, Azure Data Box, GCS Transfer Service) rather than network-based transfers. For databases, establish replication from the source to the target before any cutover, run both in parallel long enough to validate consistency, then perform a controlled failover during a low-traffic window.

Define Your Rollback Trigger Criteria Before Cutover

Every cutover should have documented rollback criteria written before the migration window opens — not during the event when engineers are under pressure. Define: what error rate triggers a rollback, what latency threshold triggers a rollback, and who has the authority to make that call. This is a governance decision, not a technical one, and it needs to be made in advance.

Phase 5 — Post-Migration Observability and Cost Governance

You Cannot Optimize What You Cannot See

Deploy distributed tracing, structured logging, and metric collection from day one of production operation — not after the first performance complaint. OpenTelemetry is now the practical standard for instrumentation that works across cloud providers and avoids vendor lock-in at the observability layer.

Set up cloud cost anomaly detection before you need it. A misconfigured auto-scaling policy or a runaway data transfer job can generate unexpected costs within hours. Most providers offer native cost anomaly alerting — configure it with thresholds that will actually catch problems before they appear on the monthly invoice.

Establish a FinOps Review Cycle

Cloud cost management is an ongoing operational discipline, not a one-time optimization exercise. B2B teams that treat the initial architecture as the final architecture typically see 20–35% cost drift within 18 months as workloads grow and usage patterns change.

A monthly FinOps review should cover: reserved vs on-demand utilization rate, egress cost trend by region pair, GPU utilization efficiency for AI workloads, and idle resource identification. If your provider offers a commitment-based discount program, model the break-even point against your actual usage before committing — not against projected usage that may not materialize.

Migration Checklist: Actionable Takeaways

Discovery Phase

• Complete service dependency map (not just server inventory)
• Classify every workload by migration pattern including GPU uplift candidates
• Document data residency and compliance constraints per workload

Provider Selection

• Model egress costs based on actual data flow volume, evaluate a no egress fee cloud provider for high-transfer workloads
• Validate GPU instance availability and managed AI service options for inference workloads
• Confirm hybrid cloud deployment interconnect options and SLA scope

Security and Compliance

• Map compliance requirements to region selection before architecture decisions
• Design zero trust network architecture — microsegmentation, per-workload identity, mTLS
• Configure SIEM integration and cloud-native threat detection before go-live

Data Migration

• Move data before compute; validate consistency before cutover
• Establish and test rollback criteria before migration windows open

Post-Migration

• Deploy OpenTelemetry instrumentation from day one
• Configure cost anomaly alerts with actionable thresholds
• Schedule monthly FinOps reviews covering egress, GPU utilization, and idle resources

The Architecture Decision That Migration Checklists Can’t Make for You

A checklist gets your team to the starting line with the right questions answered. What it cannot do is make the judgment calls that depend on your specific workload mix, compliance posture, and AI product roadmap.

The provider selection decision in particular has compounded consequences — your choice of network architecture, egress pricing model, and GPU access model creates constraints that take 12–18 months to reverse if you get it wrong. That’s a conversation worth having with an infrastructure partner who can model the financial and operational trade-offs specific to your environment.

MarQi Cloud works with B2B teams navigating exactly this decision — from hybrid cloud architecture design through to GPU-accelerated workload deployment and ongoing cost governance. If you’re in the planning phase of a migration and want an architecture review before committing to a provider or infrastructure model, that’s the right time to have it.