Google Cloud Data & Analytics Partners

66degrees is best for data-driven retail, healthcare, financial-services, or media organizations with a $180K–$700K Google Cloud data-platform, BigQuery, machine-learning, migration, or application-modernization engagement.

Poor fit: 66degrees is the wrong fit for buyers that require meaningful AWS or Azure delivery, or for very large enterprises that need capacity beyond a 500+ person GCP specialist during a rapid growth phase.

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.

Cognizant Cloud Services is best for large application-migration and modernization programs that can use a hybrid onshore-offshore model and the capacity of 5,000+ AWS professionals to deliver at scale.

Poor fit: Cognizant Cloud Services is the wrong fit for buyers whose priority is a consistent senior team working in-region, because its profile warns that quality varies across delivery centers and offshore communication can be difficult.

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.

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.

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.

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.

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.

Searce is best for data-intensive BigQuery, data-pipeline, machine-learning, and cloud-native projects that can benefit from an agile offshore delivery model, from an India-based, GCP-only consultancy with more than 300 GCP engineers.

Poor fit: Searce is the wrong fit for AWS or Azure programs, very large enterprise transformations, or buyers that cannot accommodate offshore time-zone and cultural differences: its profile is GCP-only and its listed engagement band is $100K–$400K.

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.

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.