Cloud Migration Assessment: Evaluate Your Readiness

With application architectures now built around microservices, containers, and AI/ML workloads, the complexity of managing infrastructure has multiplied. For startups and fast-scaling digital native enterprises, cloud-to-cloud migration from complex hyperscaler cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud to a simpler, cost-effective solution such as DigitalOcean can simplify operations while reducing overhead. Organizations transitioning to the cloud for the first time from on-premise infrastructure can take advantage of the efficiency and flexibility that cloud platforms provide, compared to the rigidity and high maintenance costs of on-premise systems.

Whether driven by the need for agility, cost efficiency, or scalability, migration requires more than just moving infrastructure; it demands careful evaluation of existing systems, performance patterns, and long-term growth strategies. Read on to explore how cloud migration assessment is approached, the strategies involved, and what it takes to ensure a smooth transition.

Key takeaways:

1. A cloud migration assessment provides a systematic evaluation of your IT environment, covering infrastructure, applications, and data, to define the right migration strategy and avoid risks such as downtime, cost overruns, or performance degradation.

2. By offering transparent pricing, managed services, and free migration consulting, DigitalOcean helps companies move to the cloud or away from complex providers like AWS or Azure, cutting costs while gaining operational agility and faster deployments.

3. Through workload discovery, dependency mapping, cost modeling, and AI-driven optimization, teams can select the most efficient migration strategy (e.g., rehost, replatform, refactor) tailored to their long-term growth needs.

What is a cloud migration assessment?

A cloud migration assessment is a systematic, data-driven evaluation of an organization’s existing IT environment, which includes infrastructure, applications, data, and interdependencies, to determine its readiness for migration to a cloud platform. This could involve moving from on-premise systems to the cloud or transitioning from one cloud environment to another. The process involves analyzing architectural complexity, performance baselines, security controls, compliance requirements, and operational dependencies.

The assessment identifies which workloads are suitable for cloud migration, maps them to appropriate cloud service models (IaaS, PaaS, SaaS), and informs the selection of the right deployment model (public, private, hybrid, or multi-cloud). It’s meant to capture technical constraints and resource requirements to support accurate migration planning and risk mitigation.

On-premise to cloud migration vs cloud-to-cloud migration

On-premise to cloud migration involves moving workloads, applications, and data from an in-house data center or local servers to a public or private cloud environment. In contrast, cloud-to-cloud migration shifts resources between cloud providers (e.g., from AWS to DigitalOcean) or between services within the same provider (e.g., migrating from a Droplet-based PostgreSQL setup to DigitalOcean’s fully managed PostgreSQL database service).

Benefits of cloud migration assessment

A cloud migration assessment helps you make informed, low-risk decisions by evaluating your current IT environment and identifying the most efficient path to the cloud. It shows if you’re technically ready and whether your business can handle the migration before you commit to a migration plan.

1. Strategic cost planning and budget control

Cloud migration assessments help you understand the financial impact before you make the move to avoid costly surprises later. Assessment tools like Infracost and Kubecost help analyze current infrastructure usage and forecast cloud costs so that you can avoid overspending and right-size your cloud environment from the start. Tools examine usage patterns, idle resources, and license allocations using tools like TCO calculators and rightsizing reports. This allows you to model reserved vs. on-demand instances, storage tiers, and autoscaling policies tailored to your workloads.

2. Improved security and compliance posture

You can spot security gaps and compliance requirements before they become expensive problems in the cloud. Evaluating workloads against compliance frameworks and cloud security best practices ensures sensitive data and critical systems remain protected during and after migration. Security assessments include vulnerability scanning, identity and access reviews, encryption policies, and cloud compliance audits against standards like HIPAA, GDPR, or SOC 2.

3. Reduced risk of downtime or failure

Pre-migration assessments identify potential compatibility issues, bottlenecks, or performance concerns, helping you mitigate risks early. Simulations and sandbox testing environments can be used to validate cloud-readiness, including OS version compatibility, database performance under load, and network latency impacts. Teams can also create rollback strategies and disaster recovery plans upfront.

4. Faster time to cloud ROI

With better workload planning and automation recommendations, assessments simplify execution, helping teams reach cloud benefits, like agility and scalability, faster. Tools like Cloud Custodian, Open Policy Agent (OPA), and Apache CloudStack provide code-level insights, migration blockers, and infrastructure recommendations. Combined with CI/CD automation and infrastructure as code (IaC), this speeds up deployment timelines.

Challenges of cloud migration assessment

While cloud migration assessments aim to de-risk and simplify cloud adoption, they come with their own set of risks and technical complexities. These challenges arise from the intricacies of legacy environments, limited visibility, and evolving cloud requirements.

1. Incomplete or outdated infrastructure inventory

Many organizations lack a current and accurate inventory of their on-premise infrastructure, existing cloud infrastructure, leading to gaps in assessment. Without comprehensive CMDBs or asset discovery tools, identifying workloads, dependencies, and resource utilization might become error-prone.

2. Limited integration between assessment tools

Assessment involves multiple tools (e.g., for discovery, cost modeling, security checks), but these tools face limitations when integrating with the existing model. Each tool may use different data formats (e.g., JSON vs. CSV), APIs, or asset classification standards, making it difficult to correlate results. Some proprietary tools have restricted API access or require manual data export/import, slowing down workflows. Lack of interoperability can lead to duplicated asset scans, conflicting dependency maps, or mismatched security risk scores, which might reduce assessment accuracy and increase overhead. Data silos between tools can lead to inconsistent findings, duplicated effort, or missed insights during the analysis phase.

3. Lack of cloud readiness metrics

Legacy workloads may not align with cloud-native principles. Tools may struggle to evaluate application complexity, custom code, proprietary middleware, or deprecated libraries when source code access is limited.

4. Variability in performance benchmarking

Assessing whether existing performance baselines can be met in the cloud is complex due to differences in underlying infrastructure, VM types, storage latency, and network throughput. Simulating cloud-like conditions on-premises, or between clouds might result in misleading benchmarks.

Cloud migration strategies (6Rs)

A well-defined cloud migration strategy outlines the approach for moving workloads, data, and services from legacy, on-premise environments, or one cloud provider to another cloud with minimal disruption. The right strategy depends on your architecture, performance requirements, scalability goals, and cost constraints. A cloud migration assessment will help you determine which of the following strategies—or combination of strategies—is best for your business:

1. Rehosting (lift-and-shift)

Rehosting migrates workloads from on-prem or other cloud providers to a new environment with minimal or no changes to the underlying architecture. It is used for legacy applications or when the primary driver is speed or immediate cost reduction. Rehosting is ideal for early-stage startups or e-commerce platforms with existing VM-centric infrastructure looking to quickly reduce overhead or exit expensive bare-metal infrastructure.

NoBid, an ad-tech platform, migrated its infrastructure from AWS to DigitalOcean to gain cost predictability and improve developer efficiency. By rehosting their high-throughput, low-latency bidding systems, they were able to reduce their cloud spend and maintain performance.

2. Replatforming (lift-and-optimize)

Replatforming involves migrating workloads with minimal modifications to benefit from cloud-native capabilities, such as moving a self-managed database to a managed service or shifting from VMs to container services. Replatforming works great for SaaS providers and high-compute users who want to simplify operations by offloading infrastructure management.

Picap, a ride-hailing startup in Latin America, re-platformed its infrastructure to DigitalOcean to scale quickly while optimizing backend performance. It deployed managed services to minimize DevOps overhead and gain faster deployment cycles.

3. Refactoring (re-architecting)

Refactoring restructures or rewrites applications to take full advantage of cloud-native capabilities such as microservices, serverless functions, or container orchestration. This strategy offers the highest long-term cloud ROI but requires deeper engineering effort. Well-suited for companies with modern development teams using Kubernetes or containerized environments, those planning to improve resilience, scale, or CI/CD automation.

Content Ignite, a media-tech company handling high-bandwidth ad delivery, refactored its application stack and adopted Kubernetes on DigitalOcean. This allowed them to handle large-scale, programmatic content delivery with a globally distributed infrastructure and seamless autoscaling.

4. Repurchasing

The repurchasing strategy replaces an internally hosted application with a SaaS solution. For example, moving from a self-hosted analytics platform to a cloud-based one like Datadog or Segment. This strategy is relevant for teams looking to simplify operations, reduce licensing complexity, or decommission aging services in favor of third-party tools.

CTO.ai supports developer teams to repurchase their internal DevOps operations by automating CI/CD and cloud infrastructure workflows through its SaaS platform. Instead of managing complex deployment scripts and internal automation tools, teams use CTO.ai to simplify cloud migration and DevOps lifecycle.

5. Retaining (revisit later)

Some workloads may be retained on-prem or in their current environment due to regulatory, technical, or business constraints. These are mission-critical systems tightly coupled to legacy dependencies or hardware. Startups use a retaining strategy to regulate industries or with hybrid architectures needing gradual migration and pre-migration testing.

Aquazeel, a DigitalOcean migration partner, helps customers evaluate their cloud migration journey using cost-comparison tools and strategic planning. In multi-phase projects, certain workloads, those involving high compliance requirements or tightly integrated legacy systems, may be retained temporarily while simpler services are migrated.

6. Retiring

During assessment, some applications may be identified as obsolete or redundant. These can be deprecated, reducing licensing, storage, and maintenance costs. This is applicable during cloud readiness audits, where the goal is simplification—common in startups scaling down MVP components that are no longer used.

Persistent Systems, a DigitalOcean migration partner, supports cloud modernization by identifying legacy applications that are no longer needed. In many engagements, customers choose to retire old services during migration planning, components built for MVPs, or those no longer contributing to growth.

How to perform a cloud migration assessment

At each step of the assessment, you’ll use cloud migration tools, frameworks, and analysis techniques to ensure your infrastructure, applications, and business operations are fully ready for the move to the cloud.

1. Inventory and discovery

The initial step is to identify all assets across your infrastructure, like compute resources (VMs, bare metal servers), storage volumes, databases, networking components, and applications. Open-source tools like Snipe-IT (asset tracking), phpIPAM (IP address management), and openMAINT (infrastructure inventory) can support lightweight discovery needs, suitable in smaller environments, during early-stage planning, or when budget and vendor neutrality are priorities. However, these tools may fall short in large-scale migrations that require real-time traffic analysis, automated dependency mapping, code-level assessment, or integration with TCO and cost modeling tools. For such cases, some teams may also explore proprietary solutions offered by AWS, Azure, and GCP.

Configuration management databases (CMDBs) and IT asset management (ITAM) tools help map system interdependencies and detect shadow IT components. Discovery should capture OS types, installed software, patch levels, resource utilization patterns, and inter-service dependencies.

2. Workload classification

Each workload is classified based on technical complexity, business criticality, and migration readiness. Teams categorize applications as monolithic, microservices-based, or containerized, and assess the technology stack (e.g., Java, Node.js, .NET, Python, Docker). Workloads are then grouped according to the most suitable migration strategy for each…

3. Architecture analysis

This step focuses on identifying how systems interact, including data flow, service-to-service communication, and integration points with third-party APIs or SaaS platforms. You can deploy open-source tools like Jaeger for distributed tracing, and Prometheus and/or Grafana for visualizing service relationships and metrics. These tools help model application architectures and highlight dependencies across services and infrastructure layers.

For containerized applications, your team can analyze cloud orchestration platforms such as Kubernetes, Rancher, with network policies to track ingress/egress traffic rules. This step ensures tightly coupled components are migrated together, minimizing the risk of breaking interdependent services during the transition.

4. Performance analysis

Accurately capturing usage metrics ensures right-sizing in the cloud. Use cloud monitoring tools like Prometheus, Grafana, Datadog, or New Relic to analyze CPU, memory, disk I/O, bandwidth, and request throughput. For database workloads, measure query latency, index utilization, and replication lag. This step also includes determining cloud service models, based on performance patterns, scaling needs, and burst behavior (the ability of a workload to handle sudden, short-term spikes in resource demand beyond its baseline capacity). Bandwidth-heavy applications (e.g., content delivery or scraping workloads) require special attention to egress cost estimation and latency benchmarking.

5. Security and risk assessment

A strong security foundation is formed by evaluating IAM policies, firewall rules, encryption mechanisms, and endpoint security. Review compliance requirements such as SOC 2, GDPR, HIPAA, or industry-specific standards.

Use cloud security posture management (CSPM) tools to assess vulnerabilities and misconfigurations. Identify risks in shared responsibility models when moving to public, hybrid, or multi-cloud architectures.

6. Cost estimation

Estimate current and future costs using tools like DigitalOcean pricing calculator and cost comparison tools like Infracost. Factor in compute, storage, bandwidth, licensing, and operational overhead.

For startups and scale-ups, compute-intensive and bandwidth-heavy workloads should be evaluated using price-to-performance benchmarks and resource right-sizing reports. Include assumptions for migration tools, training, managed service costs, and potential downtime.

AI in cloud migration assessment

AI tools now help organizations get better results from their cloud migration assessments. By using predictive analytics, automation, and anomaly detection, AI tools like Cast AI can help increase the accuracy, speed, and depth of migration planning.

• In the assessment phase, machine learning driven optimizers continuously predict and lower run costs by picking optimal GPU/TPU instance types (e.g., NVIDIA H100, A100, AMD MI300X), bin‑packing containers, and reacting to real usage patterns. AI tools can scan IAM policies, configs, and logs to flag misconfigurations or policy violations early in the assessment, strengthening posture before any cutover.

• During the planning stage, by combining code complexity, dependency graphs, and cloud maturity signals, AI systems can recommend whether to rehost, replatform, or refactor each app. AI-based benchmarking tools analyze latency, throughput, and cost-performance trade-offs, which helps teams design a migration strategy that balances compute efficiency and scalability.

• Post-migration, AI can support continually analyzing utilization and SLOs to auto-tune scaling, storage classes, and node types.

Open-source AI-driven solutions like PyOD (for anomaly detection and time-series forecasting) can be integrated into custom workflows to support activities such as anomaly detection, cost forecasting, and performance monitoring. However, these open-source tools require manual setup and do not provide end-to-end AI-driven migration assessments like commercial offerings.

Cloud migration assessment FAQs

What tools are used in a cloud migration assessment? A cloud migration assessment involves solutions for discovery, cost modeling, performance tracking, and security analysis. AI-powered platforms are also used to provide intelligent recommendations and optimize resources. Open-source tools like Infracost, Prometheus, Grafana, and OPA, and proprietary solutions like Datadog and Cast AI support discovery, cost modeling, performance analysis, and security evaluation.

How long does a cloud assessment take? The timeline varies by environment size. Small workloads can be assessed in 1–2 weeks, medium setups may take 3–6 weeks, and large or complex enterprises might require 2–3 months or more.

What is the purpose of a cloud assessment? Its purpose is to evaluate cloud readiness, define the right migration strategy, estimate costs, ensure security and compliance, and create a roadmap that aligns with business goals.

References

• Configuration management databases (CMDBs)

• Jaeger

• Infracost

• Kubecost

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