AWS Managed Services Partners

2nd Watch is best for enterprises evaluating an AWS lift-and-shift migration followed by optimization, financial-services or retail workloads, and managed cloud operations — now delivered under Ollion, which absorbed the 2nd Watch business in 2023.

Poor fit: 2nd Watch is the wrong fit for buyers who need a standalone vendor with a stable, independently verifiable team, because it is no longer a standalone brand: since 2023 the practice operates inside Ollion, so current team composition, SLAs, and pricing should be confirmed under Ollion.

Accenture Cloud is best for Fortune 500 buyers planning a multi-year transformation across AWS, Azure, and GCP that requires global delivery capacity, regulated-industry experience, and a bench of 10,000+ cloud specialists.

Poor fit: Accenture Cloud is the wrong fit for a smaller, AWS-only modernization where its $250K–$1M+ pricing band and process-heavy delivery model would add more overhead than the scope warrants.

Capgemini Cloud Services is best for enterprises that need application modernization, migration, DevOps, and managed services from a multi-cloud integrator with 6,000+ AWS-certified staff and flexible delivery models.

Poor fit: Capgemini Cloud Services is the wrong fit for a buyer that needs uniform delivery quality across regions and a documentation-heavy handoff to its internal team, because both regional consistency and knowledge transfer are documented concerns.

ClearScale is best for an SMB or mid-market company committed to AWS that needs a difficult migration, cloud-native modernization, or ML/data build from an engineering-led partner; its Acoustic case documents 6,300 servers migrated from 12 data centers in 10 months.

Poor fit: ClearScale is the wrong fit for organizations evaluating Azure or GCP, or for a very large program that cannot tolerate competition for senior staff, because the firm is AWS-exclusive and its roughly 100–175-person team has limited concurrent capacity.

Cloudticity is best for healthcare and life-sciences organizations that need an AWS migration or managed environment with HIPAA and HITRUST controls built into operations; its MiHIN case reports an 80% reduction in manual compliance oversight.

Poor fit: Cloudticity is the wrong fit for buyers outside healthcare or for programs that need broad multi-cloud delivery, because its practice is healthcare-specialized, its listed platform is AWS, and its team of 120+ healthcare cloud specialists may lack capacity for the largest concurrent transformations.

Coalfire is best for SaaS vendors pursuing FedRAMP, organizations combining frameworks such as PCI, HITRUST, ISO 27001, and SOC 2, and DoD contractors that need CMMC or IL4–IL6 assessment capability, delivered by a roughly 1,000-person firm supporting more than 100 frameworks.

Poor fit: Coalfire is the wrong fit for a buyer that expects one team to advise on and assess the same FedRAMP package: impartiality rules require those roles to be split, and its $50K–$500K 3PAO pricing can also be excessive for buyers seeking a boutique or automation-led assessment.

DoiT International is best for technology, media, gaming, and SaaS companies that need FinOps, cloud-cost optimization, migration, or managed cloud operations with data and machine-learning support, from a GCP- and AWS-focused MSP with more than 400 cloud engineers.

Poor fit: DoiT International is the wrong fit for Azure-led programs or traditional enterprises that do not want an MSP relationship: its platform coverage is limited to GCP and AWS, its culture is oriented toward technology and startups, and its listed engagement band starts at $150K plus managed services.

DXC Technology is best for a large enterprise with a mainframe, SAP, or complex legacy estate that needs one integrator to migrate and operate workloads across AWS, Azure, GCP, or hybrid cloud; its insurance case moved more than 1 million annuity contracts from COBOL and Db2 systems to AWS.

Poor fit: DXC Technology is the wrong fit for an SMB, startup, or cloud-native buyer seeking a fast, transparent, specialist engagement, because its model is built around formal procurement and multi-year enterprise contracts while delivery quality can vary for non-strategic accounts.

Effectual is best for a U.S. public-sector, nonprofit, or regulated organization moving VMware or legacy infrastructure into AWS or AWS GovCloud; one committed case documents 120 virtual machines and 30 TB moved to an AWS-native disaster-recovery environment in three weeks.

Poor fit: Effectual is the wrong fit for a multi-cloud program or a very large transformation that needs a global delivery bench, because it is AWS-only, has roughly 200–300 employees, and its profile flags unknown concurrent capacity and account-continuity risk.

EPAM Systems is best for a large enterprise that needs software-engineering depth across cloud modernization and production AI on AWS, Azure, or GCP; its Louis Dreyfus engagement moved 1,500+ virtual machines into three regional Azure landing zones.

Poor fit: EPAM Systems is the wrong fit for a lean, price-led single-cloud project that would be better served by a focused boutique, or for a buyer unwilling to contractually pin down delivery geography and seniority, because its premium global model carries team-variation and location risk.

HCLTech is best for a large enterprise that wants one long-term integrator for a complex AWS, Azure, and GCP transformation, SAP migration, or managed-cloud program, backed by a listed pool of 40,000+ certified cloud professionals.

Poor fit: HCLTech is the wrong fit for an SMB, a boutique cloud-native build, or a highly specialized single-platform workload where named expert access matters more than global scale, because its enterprise model uses opaque multi-year contracts and delivery quality varies by account and geography.

IBM Consulting is best for a large enterprise modernizing a hybrid or multi-cloud estate where Red Hat OpenShift portability, SAP depth, and integration across AWS, Azure, GCP, and private infrastructure matter; its profile lists 19,000+ SAP-trained consultants.

Poor fit: IBM Consulting is the wrong fit for an SMB, a sub-$1M project, or a pure single-hyperscaler build that needs fast mobilization and focused platform depth, because its model carries enterprise contracting overhead, opaque premium pricing, and large-team quality variation.

Infosys is best for a global enterprise coordinating a multi-country transformation across AWS, Azure, and GCP, especially when SAP, Salesforce, Oracle, or regulated-industry integration must run through one systems integrator with delivery coverage in 59 countries.

Poor fit: Infosys is the wrong fit for an SMB, a cloud-native company, or a narrow single-platform build that needs specialist depth and a stable small team, because its 320,000+ employee generalist model relies heavily on offshore delivery and opaque enterprise contracts.

Kyndryl is best for large regulated enterprises moving complex legacy, mainframe, or hybrid estates into long-term managed operations across AWS, Azure, and GCP, backed by approximately 73,000 employees and unusual IBM Z depth.

Poor fit: Kyndryl is the wrong fit for a greenfield cloud-native product build or application-development-only engagement, because its operating model is infrastructure-led and optimized for large, negotiated managed-services programs rather than boutique engineering delivery.

Mission Cloud is best for startup and mid-market media, gaming, or SaaS teams with a $100K+ AWS migration, managed-services, or FinOps engagement that needs an AWS-focused partner to extend the internal team.

Poor fit: Mission Cloud is the wrong fit for organizations that need Azure or GCP delivery or a massive legacy-enterprise transformation, because its practice is AWS-only and its profile notes less experience with that scale of migration than global integrators.

NTT DATA Cloud is best for enterprises that need hybrid-cloud migration, complex network integration, or ongoing managed infrastructure across AWS, Azure, and GCP, particularly where delivery strength in Asia matters, backed by more than 4,000 cloud-certified staff.

Poor fit: NTT DATA Cloud is the wrong fit for a buyer seeking a small, highly agile team for cloud-native application development: its profile is infrastructure- and network-led, its approach is more traditional than a cloud-native boutique, and engagements are listed at $200K–$700K.

Optiv is best for regulated enterprises consolidating cloud-security tooling, compliance, and managed security across AWS, Azure, and GCP with a pure-play integrator that has 600+ security practitioners.

Poor fit: Optiv is the wrong fit for a general cloud migration or application-modernization program where security is not the primary mandate, or for a buyer unwilling to name and vet the delivery consultants before signing the SOW.

phData is best for enterprises with a $200K–$1M+ Snowflake migration, data-platform build, or managed DataOps/MLOps program that needs deep Snowflake, dbt, AWS, and Azure engineering rather than general cloud consulting.

Poor fit: phData is the wrong fit for buyers seeking broad infrastructure, networking, cybersecurity, or multi-cloud operations from one provider, because the firm's specialization is data engineering and managed data operations.

Presidio is best for mid-market and enterprise buyers with a $100K–$500K cloud project who want AWS migration, security, MDR, procurement, and ongoing managed services under one integrator.

Poor fit: Presidio is the wrong fit for a Fortune 100 buyer seeking a pure-play security partner with deep threat-intelligence research, offensive-security depth, or bespoke detection engineering, because security is one horizontal practice within its broader systems-integration business.

Pythian is best for complex database migrations, data-platform architecture, and 24/7 managed data services across GCP, AWS, and Azure, delivered by more than 400 data specialists.

Poor fit: Pythian is the wrong fit for broad, non-data-centric cloud transformations or very large programs that need abundant generalist capacity: its specialization carries a $150K–$550K engagement band, less breadth outside data, and potential capacity constraints at the largest scale.

Quantiphi is best for healthcare, financial-services, public-sector, or media buyers building production AI and machine-learning systems on Google Cloud or AWS with an AI-first specialist of approximately 3,100 employees.

Poor fit: Quantiphi is the wrong fit for an Azure-first program or an engagement requiring a predominantly US-based onsite team, because Azure is a secondary practice and roughly 87% of its workforce is based in India.

Rackspace Technology is best for organizations with a $150K+ need to operate and support an existing AWS, Azure, or GCP estate through managed services, 24/7/365 coverage, and flexible access to engineering capacity.

Poor fit: Rackspace Technology is the wrong fit for a greenfield, cloud-native product build where application innovation matters more than infrastructure operations, because its hosting heritage and managed-run focus can overshadow cloud-native engineering.

SADA is best for enterprise technology, healthcare, media, or financial-services teams making Google Cloud the center of a data, analytics, machine-learning, or application-modernization program, backed by a listed bench of 800+ cloud engineers.

Poor fit: SADA is the wrong fit for a buyer seeking an AWS-first partner, a low-cost provider, or a very large legacy migration, because its profile identifies Google Cloud as the primary practice, premium pricing, limited capacity at the largest scale, and complex legacy migration as a weak fit.

Slalom is best for enterprise buyers running a multi-year AWS, Azure, or GCP transformation that needs organizational change management and close collaboration from a local-market team within a 10,000+ employee consultancy.

Poor fit: Slalom is the wrong fit for price-sensitive buyers or programs that require identical delivery quality across locations, because engagements are typically $250K+ and team quality can vary by local office.

SoftwareONE is best for organizations that need Azure migration, license optimization, FinOps, or managed infrastructure across complex Microsoft estates, backed by more than 3,000 Azure specialists.

Poor fit: SoftwareONE is the wrong fit for buyers prioritizing application modernization or cutting-edge cloud-native product engineering: its strengths are licensing, cost management, and infrastructure, while its listed engagement band is $180K–$650K and delivery quality can vary by region.

TCS Cloud is best for large, long-term migration, managed-services, or SAP-on-AWS programs that benefit from industrialized delivery and offshore scale across AWS, Azure, and GCP, backed by more than 12,000 cloud specialists.

Poor fit: TCS Cloud is the wrong fit for buyers seeking a small, highly adaptive cloud-native team: its offshore-heavy, process-oriented model can be slow to change, account teams can feel impersonal, and its listed engagement band starts at $150K.

Thoughtworks is best for engineering-led enterprises investing $500K–$5M+ to modernize a complex mainframe, monolith, platform, or data estate while transferring cloud-native engineering capability into the internal team.

Poor fit: Thoughtworks is the wrong fit for a commodity lift-and-shift migration or day-to-day managed cloud operations, because its value is architectural modernization and engineering craft rather than migration headcount, patching, ticketing, or infrastructure management.

Wipro Cloud is best for large infrastructure migrations and ongoing managed-services programs that value offshore pricing, industry accelerators, and delivery across AWS, Azure, and GCP, backed by more than 8,000 AWS-certified staff.

Poor fit: Wipro Cloud is the wrong fit for buyers that need a consistent small team for fast cloud-native or modern DevOps delivery: its profile flags slower innovation, quality variation across locations, and communication risk, with engagements listed from $160K.

Xebia is best for mid-market and enterprise buyers that need one engineering partner to modernize applications, data, AI, and cloud platforms across AWS, Azure, or Google Cloud, especially when platform engineering, FinOps, managed operations, and team upskilling must be combined in the same program.

Poor fit: Xebia is the wrong fit for a buyer seeking a small commodity cloud task with public fixed pricing, or one unwilling to validate the proposed regional delivery team, because Xebia sells custom, globally staffed programs and does not publish a firmwide rate card or minimum engagement.