What if you could reduce risk, speed up value, and keep your teams focused while moving core systems to modern platforms?
We guide enterprises through a predictable program that links strategy to execution, turning business goals into measurable outcomes and clear timelines.
Our approach blends advisory and delivery, so your teams gain early wins and lasting momentum without a disruptive overhaul.
We pair proven patterns, partner ecosystems, and automation to protect availability, compliance, and cost control while accelerating transformation.
Key Takeaways
- We frame the journey as a low-risk, outcome-focused program that accelerates business value.
- Our plan turns strategy into pragmatic steps with measurable success metrics.
- Delivery integrates with your governance so the transition extends your team, not replaces it.
- We emphasize early wins, ongoing optimization, and financial guardrails.
- Our collaborative model ensures knowledge transfer and continued improvement after cut-over.
Why Businesses Choose Managed Cloud Migration Today
Organizations pick expert-led transitions when they want lower risk, clearer timelines, and faster business outcomes.
From disruption risk to faster time-to-value
We reduce risk by following structured assessment-to-execution playbooks that limit downtime and keep core systems available, so leadership sees value in less time.
We improve efficiency by replacing manual steps with automated pipelines and intelligent recommendations, which preserves scarce resources for strategic work.
- Clear strategy: business cases mapped to technical plans for predictable budgets and milestones.
- Operational protection: sequenced moves, dependency validation, and roll-back plans that prevent service gaps.
- Cost optimization: early license and platform reviews that inform financial guardrails and reduce unnecessary costs.
- Expert guidance: our experts bring platform knowledge to balance performance, security, and future innovation.
The result is a pragmatic story for executives: staged execution, measured wins, and a path that balances risk and reward while shortening time to measurable outcomes.
Cloud Migration Managed Services
We align business goals to technical workstreams so each phase delivers clear outcomes and repeatable improvements.
End-to-end strategy, execution, and ongoing optimization
Covering applications, data, and infrastructure
We deliver a comprehensive program that begins with discovery and strategy, continues through design and execution, and extends into post-cutover optimization for applications, data platforms, and cloud infrastructure.
Our teams handle governance, risk, and performance while preserving cost control and reporting for stakeholders. We bring architects, security experts, network engineers, DBAs, and DevOps into a single program to address dependencies and reduce surprises.
- Repeatable CI/CD pipelines and automated testing make the migration process faster and higher quality.
- We protect data integrity with schema validation, transformation tracking, and reconciliation for analytics and downstream systems.
- Operational handover includes monitoring, incident response, runbooks, and capacity planning so support is predictable on day one.
| Scope |
Outcome |
Typical Components |
| Advisory & Strategy |
Clear plan, TCO/ROI, risk register |
Workshops, catalogs, cost models |
| Design & Build |
Architected systems ready for cut-over |
CI/CD, testing, hybrid connectivity |
| Execution & Cut-over |
Validated workloads and apps in target |
Data migration, validation, rollback plans |
| Operations & Support |
Stable system with SLAs and runbooks |
Monitoring, logging, on-call, optimization |
Our Migration Strategy: From Assessment to a Clear Plan
We start every engagement with a focused assessment that turns application inventories and stakeholder goals into an actionable plan.
Holistic workload and application cataloging
We perform discovery that inventories applications, dependencies, interfaces, datasets, and cloud infrastructure requirements.
That work yields a prioritized backlog of workloads and a clear sequencing approach for the project.
TCO/ROI modeling and cost forecasts
Our experts analyze business drivers and constraints, and translate them into technical acceptance criteria and measurable outcomes.
We model TCO and ROI with realistic assumptions about consumption, licensing, and modernization, so stakeholders see payback windows and sensitivity scenarios.
- Tools and workshops validate requirements, simulate deployment options, and surface early blockers.
- We map workloads to the most suitable platforms, considering compliance, latency, and data gravity.
- Data migration strategies address volumes, throughput, downtime windows, and validation steps to keep analytics intact.
- Governance and financial guardrails—RACI, change control, and cost forecasts—ensure the project stays aligned with business goals.
Proven Migration Approaches for Your Workloads
We pick pragmatic paths so each application follows the route that best balances speed, cost, and operational risk. That means choosing between rehost, replatform, or refactor based on business goals, time boxes, and operational readiness.
Start small, learn fast. We recommend a first step that validates tooling, landing zones, security controls, and cut-over procedures by moving a single, meaningful workload. This reduces risk and gives the project clear early wins.
How we decide the right path
- Assess application traits — statefulness, latency, dependency complexity, and data change rates — to decide whether to move apps as-is, optimize the platform, or modernize code.
- Factor licensing and modernization opportunities; containers, serverless, and open-source databases often reduce long-term cost and vendor lock-in.
- Plan migration waves that group compatible workloads, enabling controlled change windows and rollback plans tied to business calendars.
- Build validation gates for app and data checks into each wave, aligning go/no-go decisions with stakeholders to keep disruption low.
We turn lessons from the initial workload into playbooks and automation, so subsequent project phases accelerate with higher accuracy and predictable outcomes for the business.
Platforms, Technologies, and Partner Ecosystem
We combine vendor competencies and product tooling to give you predictable access to proven solution patterns.
Our team brings multi-vendor expertise across AWS, Microsoft Azure, Google Cloud, and VMware so we can select the right technology mix for regulatory, latency, and availability needs.
We leverage structured partner programs and automation to reduce cost and speed execution.
- We use AWS MAP and OLA to access assessments, funding, and tooling that help optimize licensing and lower execution friction.
- Automation and machine learning accelerators provide intelligent recommendations to right-size resources and improve consistency.
- Our approach covers Windows/.NET, SAP, SQL Server, VMware estates, and modern app patterns like containers and serverless.
Years of experience with partner competencies mean governance, security, and operational runbooks are in place from day one, avoiding gaps that slow adoption.
We include relevant case study references—such as enterprise mainframe and marketing platform transformations—to show how platform choice maps to measurable business outcomes and reduced risk.
Our Managed Migration Methodology
Our approach breaks the project into clear, testable steps that reduce operational risk and accelerate value delivery. We pair architecture, validation, and runbook-driven execution so teams see measurable progress at each checkpoint.
Architect, migrate, validate, and cut-over
We architect secure landing zones and connectivity, then migrate in controlled iterations that limit blast radius.
Each step includes functional and non-functional validation and a documented roll-back plan to protect availability and business continuity.
CI/CD, automated testing, and controlled go-live
We build CI/CD pipelines with automated tests and policy gates, using ScienceSoft design patterns and AWS quality controls to enforce parity and speed feedback.
Rackspace executes validation and cut-over procedures so go-live follows proven runbooks and compliance checks.
Post-migration operations and performance tuning
We establish operations foundations—observability, incident management, SRE practices, and capacity planning—so teams inherit a stable model on day one.
Performance engineering uses load testing and right-sizing to meet availability and user experience targets, and we provide support during and after go-live to stabilize workloads.
| Phase |
Focus |
Primary Deliverable |
| Architect |
Connectivity, security, sequencing |
Landing zone design, backlog |
| Build |
CI/CD, tests, automation |
Pipelines, automated test suites |
| Execute |
Validation, cut-over, rollback |
Go-live runbooks, verification reports |
| Operate |
Observability, tuning, support |
Dashboards, SRE playbooks, handover |
Tooling and Accelerators to Reduce Time and Costs
By combining intelligent automation and tailored workshops, we shrink timelines while protecting operations.
We deploy automation for discovery, dependency mapping, landing zone provisioning, and cut-over orchestration, which compresses timelines and reduces human error across environments.
AWS machine learning powers intelligent recommendations that find right-sizing opportunities and modernization candidates, lowering run-rate costs and improving performance.
We evaluate serverless and container-native options for applications and data pipelines to reduce operational overhead and increase elasticity. ScienceSoft patterns for serverless ETL cut consumption costs for DWH work.
Workshops and Playbooks
Rackspace-led hands-on workshops adapt playbooks to each application, capturing constraints and compliance needs that generic templates miss.
- Ready-to-use solutions for CI/CD, policy enforcement, and observability speed setup while keeping governance intact.
- We match tooling depth to workload criticality so mission-critical systems get enhanced automation and resiliency.
| Feature |
Benefit |
Example |
| Automation & Discovery |
Faster inventories, fewer errors |
AWS automated dependency mapping |
| Intelligent Recommendations |
Lower run-rate, better sizing |
AWS ML right-sizing reports |
| Serverless ETL |
Reduced consumption costs |
ScienceSoft serverless DWH models |
| Workshops & Playbooks |
Tailored cut-over plans |
Rackspace application workshops |
Security, Compliance, and Governance by Design
We design systems so access, encryption, and auditing are built in, not bolted on at the end.
Security and governance must be practical for operators and verifiable for auditors, so we combine technical controls with policy automation that reduces manual work.
Access management, encryption, backups, and monitoring
We design identity and access with least privilege, strong authentication, and just‑in‑time elevation to limit unauthorized entry.
- Encryption: in transit and at rest, centralized secrets, and standard key rotation embedded into pipelines.
- Backups & DR: tested restores, defined RPO/RTO, and cross-region options for business continuity.
- Monitoring: baseline logging, anomaly detection, alerting, and audit trails for rapid response.
Meeting HIPAA, PCI DSS, SOC 2, GDPR, CCPA, FedRAMP needs
We map controls to frameworks and automate evidence collection, so audits focus on outcomes, not paperwork.
ScienceSoft risk analyses flag threats like insecure interfaces, misconfiguration, and account hijacking, and we apply continuous checks to prevent regressions.
- Governance templates and policy packs that reduce misconfiguration and simplify change management.
- Periodic posture reviews and simulated drills that validate assumptions and update runbooks.
- Close coordination with your compliance and risk teams to make policies measurable and practical for the business.
Performance, Availability, and Cost Optimization
We combine profiling, telemetry, and policy to make sure systems meet latency targets while keeping spend in check.
Our approach couples performance engineering with financial governance so teams get reliable availability and leaders see predictable costs. We run capacity modeling and load testing to validate latency and throughput under realistic traffic.
Autoscaling, right-sizing, and managed PaaS databases
We configure autoscaling and right-sizing rules that use live telemetry to tune compute, storage, and database tiers.
Where appropriate, we prefer managed PaaS databases like Amazon RDS or Aurora to simplify operations and obtain best price-performance for SQL Server workloads.
License optimization for Windows and SQL Server
We assess licensing options, including AWS OLA recommendations, BYOL paths, and consolidation opportunities to lower fees and administrative overhead.
- Performance profiling, capacity modeling, and load testing to validate SLAs.
- Autoscaling and right-sizing policies driven by telemetry to improve availability and control costs.
- PaaS database selection (Amazon RDS/Aurora) for administration and price-performance gains.
- License reviews to evaluate BYOL, consolidation, or modernization alternatives.
| Focus |
Action |
Benefit |
| Performance engineering |
Profiling, load testing, capacity modeling |
Meets latency and throughput targets |
| Autoscaling & right-sizing |
Telemetry-driven policies, dynamic tiers |
Better availability, lower wasted compute |
| Database optimization |
PaaS selection (RDS / Aurora), scaling |
Simplified ops, improved price-performance |
| License optimization |
Assessment, consolidation, BYOL advice |
Reduced license expense and complexity |
Timelines and Pricing Expectations
We translate discovery findings into phased schedules and cost bands that match your operational cadence, so stakeholders see when value arrives and what it costs.
Typical durations: simple applications often complete in ~2–2.5 months, medium and complex apps commonly require 6+ months, and data warehouse moves range from ~2–8 months depending on scale and testing needs.
Budget guidance: indicative costs are $20,000–$250,000 for medium/large apps, $500,000+ for complex portfolios, and DWH projects often fall between $140,000–$700,000 based on organization size and platform choice.
- We classify workloads by complexity, dependencies, and non-functional needs to set realistic time and cost estimates.
- Scenario plans show how refactoring, extra environments, or feature work affect schedule and spend.
- We include infrastructure and platform assumptions and recommend contingency buffers for approvals, throughput, and change freezes.
| Workstream |
Typical Duration |
Indicative Cost Range |
| Simple app |
2–2.5 months |
$20,000–$50,000 |
| Complex app/portfolio |
6+ months |
$500,000+ |
| Data warehouse |
2–8 months |
$140,000–$700,000 |
How we help: we present phased project plans tied to deliverables and acceptance criteria, so procurement and governance can approve with confidence and compare our projections to industry benchmarks over years.
Selected Case Studies and Business Outcomes
Selected studies illustrate practical steps we used to improve application availability and shorten time-to-market for critical systems.
Mainframe and VMware modernization to managed services
We worked with Citizens Bank to move legacy mainframe workloads into AWS Mainframe Modernization, and rehost VMware estates for better agility, compliance, and continuity.
Outcome: improved resilience and reduced operational overhead while maintaining regulatory controls.
SQL Server and .NET migrations for agility and price-performance
We migrated SQL Server and .NET applications to Amazon RDS and Aurora, which simplified administration and improved price-performance for high‑traffic apps.
Outcome: lower licensing complexity, faster deployments, and higher availability for customer-facing systems.
Lower costs, faster time-to-market, and data platform wins
Examples include Salesforce moving Marketing Cloud to AWS and ScienceSoft shifting a mobile credit platform to a new region, cutting infrastructure expense and speeding releases.
ScienceSoft also moved analytics pipelines from Amazon RDS to Google Cloud and built a Hadoop/Hive/Spark system for real-time processing, enabling self-service BI and faster decisions.
- Regional moves reduced latency and optimized spend.
- Cross-functional teams followed clear governance to protect SLAs during cut-over.
- Business KPIs improved: faster time-to-market, cost reduction, and better customer experience.
| Case |
Work |
Primary Outcome |
Business KPI |
| Citizens Bank |
Mainframe & VMware re-platform |
Higher resilience, regulatory alignment |
Availability +15%, ops cost -18% |
| Salesforce |
Marketing Cloud move |
Consolidated platforms, faster releases |
Time-to-market -30% |
| ScienceSoft |
Mobile platform & analytics re-platform |
Lower infra cost, real-time analytics |
Infra spend -25%, processing latency -60% |
We extract repeatable patterns from each case study to shape your plan, aligning technology choices to applications, compliance needs, and team readiness so outcomes are measurable and replicable.
How We Engage: Consulting, Professional, and Managed Services
Our engagement starts with consulting that turns executive goals into a clear plan. We create prioritized waves, governance structures, and a road map your leadership can approve.
Assessment and advisory to define your migration route
We begin with focused assessments that identify risks, dependencies, and business priorities.
These findings become an executive-ready strategy and a sequenced project plan that guides every phase.
Design-build, cut-over, and ongoing managed operations
Our professional teams design landing zones, integrations, and automation under clear acceptance criteria.
During cut-over we follow runbooks and SLAs to protect availability and data integrity.
After go-live, we provide operations and support models ranging from co-managed to fully managed, tailored to your needs.
Credentials: engineers, certifications, and recognized leadership
We field a seasoned team of engineers and architects, with 1,700+ cloud engineers and 11,000+ certifications across AWS, Microsoft, Google, and VMware.
Our partners include AWS Premier Consulting Partner, Google Cloud Partner of the Year, Azure Expert MSP, and VMware Cloud Verified.
ScienceSoft brings ISO 9001 and ISO 27001 compliance and long-term client partnerships that underscore quality and security.
- Consulting and advisory that crystallize strategy and governance.
- Professional services to build landing zones and automation for cut-over.
- Ongoing management, optimization, and support with KPI reporting and structured reviews.
| Engagement Type |
Primary Offer |
Value to You |
| Consulting & Advisory |
Roadmaps, prioritized waves, governance |
Executive clarity, reduced risk, faster buy-in |
| Professional Services |
Design, build, automation, cut-over |
Repeatable runbooks, tested acceptance criteria |
| Operations & Support |
Co-managed or full support, KPIs, optimization |
Stable operations, continuous improvement, cost control |
Conclusion
We offer a clear, executable path that turns assessment findings into prioritized workstreams and measurable milestones. This approach uses proven patterns, AWS MAP and OLA programs, and multi-vendor credentials from Azure and Google to limit risk and speed results.
Start with a pilot wave that validates tooling, controls, and cut-over runbooks, then scale across apps and portfolios with consistent governance and acceptance gates.
We commit to ongoing support so your teams retain knowledge, reduce operational cost, and keep improving performance over time.
Our solutions combine practical technology choices and partner ecosystems to balance innovation with stability and compliance. Engage our architects to refine strategy, finalize the plan, and schedule an initial discovery sprint.
Ready to move forward? Contact us to define your first wave and validate the roadmap with a hands-on workshop.
FAQ
What are the primary benefits of using cloud migration managed services for our applications and data?
We reduce operational burden and accelerate time-to-value by offering a clear plan, experienced teams, and repeatable tools that cover applications, data, and infrastructure. Our approach minimizes disruption, improves performance and availability through autoscaling and right-sizing, and delivers cost transparency with TCO/ROI modeling so you can measure business outcomes quickly.
How do you decide between rehost, replatform, and refactor for different workloads?
We catalog workloads and assess technical debt, dependencies, and business value, then match each application to the least disruptive option that meets performance and cost goals. For low-risk, lift-and-shift moves we rehost; for quick cloud advantages we replatform; and for long-term agility and cost reduction we refactor, often starting with a single meaningful workload to de-risk the project.
What is included in your migration strategy from assessment to cut-over?
Our end-to-end strategy includes holistic workload discovery, security and compliance mapping, TCO/ROI forecasting, migration planning, CI/CD pipelines, automated testing, controlled go-live, and post-migration tuning. We combine workshops, migration playbooks, and automated accelerators to shorten timelines and maintain operational continuity.
Which platforms and partner certifications do you support?
We work across AWS, Microsoft Azure, Google Cloud, and VMware environments, leveraging partner programs such as AWS MAP and recognized competency partners. Our engineers hold industry certifications and follow best practices to ensure compatibility, performance, and licensing optimization for enterprise systems like Windows and SQL Server.
How do you ensure security, compliance, and governance during the move?
Security is built in from day one with access management, encryption, backups, monitoring, and role-based controls. We map controls to regulatory frameworks such as HIPAA, PCI DSS, SOC 2, GDPR, CCPA, and FedRAMP, and we validate configurations through automated scans and manual reviews before cut-over.
What tooling and accelerators do you use to reduce time and cost?
We use automation for discovery, intelligent recommendations for right-sizing, serverless options where appropriate, and prebuilt playbooks for common application patterns. These accelerators speed assessments, lower manual effort, and reduce risk during execution, helping meet both performance and budget goals.
How long does a typical migration project take and what affects the timeline?
Timelines vary: simple applications often take about 2–2.5 months, complex estates can exceed 6 months, and data warehouse moves may span 2–8 months. Factors include application complexity, data volume, integration dependencies, compliance requirements, and whether refactoring is required.
How do you manage costs and provide budgeting guidance for migrations?
We provide TCO and ROI modeling up front, recommend license optimization strategies, and identify cost-saving options like managed PaaS databases and autoscaling. During the project we track spend against forecasts and propose optimizations to deliver measurable savings post-move.
What support do you provide after cut-over to ensure performance and stability?
Post-migration we offer monitoring, incident response, performance tuning, and ongoing operations support. Our teams run validation suites, implement CI/CD for continuous improvements, and provide managed operations to maintain availability and meet agreed SLAs.
Can you provide examples of past outcomes and industry use cases?
We have modernized mainframe and VMware estates, migrated SQL Server and .NET applications for improved agility and price-performance, and reduced total costs while accelerating time-to-market. Case studies demonstrate measurable gains in efficiency, availability, and operational simplicity.
How do you engage with clients—consulting, project delivery, or ongoing management?
We offer flexible engagement models: advisory assessments to define strategy, design-build projects for execution and cut-over, and managed operations for ongoing support. Our credentialed engineers and certified partners ensure continuity from planning through steady-state operations.
