Quantiphi
AWS Premier Global Consulting Partner founded in 2013, headquartered in Marlborough, Massachusetts.
Founded 2013 | Marlborough, USA | 1,001–5,000 employees | Last updated: July 2026
custom-model-trainingmlops-pipelinemodel-deploymentml-infrastructure
What is Quantiphi?
Quantiphi is a digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, focused on applied artificial intelligence, machine learning, and data science for complex business problems. Headquartered in Marlborough, Massachusetts, the company operates across six global locations and reports between 1,000 and 5,000 employees. Quantiphi holds AWS Premier Global Consulting Partner status and was named the first Preferred Amazon Quick Global SI Partner by the AWS Generative AI Innovation Center, alongside being recognized as 2025 AWS Public Sector Global GenAI Consulting Partner of the Year.
Quantiphi was founded in 2013 and is headquartered in Marlborough, USA. The firm employs 1,001–5,000 people and works primarily with clients in Public sector, Healthcare, Financial services, Media sectors. Its primary differentiator is: Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status..
Quantiphi tech stack and services
AWS SageMakerAmazon BedrockAWSPythonKubernetes
| Service area | Details |
|---|---|
| Building and deploying ML models on AWS SageMaker at enterprise scale | Available for Public sector, Healthcare, Financial services, Media clients |
| Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support | Available for Public sector, Healthcare, Financial services, Media clients |
| Public sector agencies needing an AWS-vetted partner for AI/ML modernization | Available for Public sector, Healthcare, Financial services, Media clients |
| Migrating existing ML workloads into AWS-native MLOps pipelines | Available for Public sector, Healthcare, Financial services, Media clients |
Quantiphi use cases
Short answer: Quantiphi is best suited for enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison..
| Use case | Industries | Approach |
|---|---|---|
| Building and deploying ML models on AWS SageMaker at enterprise scale | Public sector, Healthcare | AWS SageMaker, Amazon Bedrock |
| Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support | Public sector, Healthcare | AWS SageMaker, Amazon Bedrock |
| Public sector agencies needing an AWS-vetted partner for AI/ML modernization | Public sector, Healthcare | AWS SageMaker, Amazon Bedrock |
| Migrating existing ML workloads into AWS-native MLOps pipelines | Public sector, Healthcare | AWS SageMaker, Amazon Bedrock |
Quantiphi pricing
Short answer: Quantiphi uses a not published; enterprise project engagements pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Enterprise project engagement | Variable; depends on team size | Large programmes or team augmentation |
| Managed AI services | Variable; depends on team size | Large programmes or team augmentation |
Quantiphi does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
Quantiphi pros and cons
| Advantages | Things to consider |
|---|---|
| +Strongest documented AWS partnership tier (Premier Global Consulting Partner) among companies in this comparison. | -Heavy AWS specialization may be less useful for clients standardized on Azure or GCP. |
| +2025 AWS Public Sector Global GenAI Consulting Partner of the Year recognition. | -No clearly located aggregate Clutch/G2 star rating in available public sources. |
| +Reported $630.2M in revenue signals substantial scale and financial stability. | -Employee count range (1,000–5,000) is wide, making exact delivery capacity hard to pin down. |
| +Multi-location global presence supports enterprise clients needing regional delivery. | -Pricing model and minimum engagement are not published. |
Quantiphi vs alternatives
How Quantiphi compares to the other top ML Model Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market fintech, supply chain, and SaaS companies that... | Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. | 4.8 | Full comparison |
| Neurons Lab | Financial services firms wanting a boutique, engineering-led partner... | End-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services. | 4.6 | Full comparison |
| DataRoot Labs | Startups and mid-market companies wanting a senior, AI-only... | Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University). | 4.6 | Full comparison |
| Miquido | Companies that need ML/computer-vision capability bundled with broader... | Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor. | 4.6 | Full comparison |
| Provectus | Mid-market companies that need cloud data infrastructure and... | Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves. | 4.5 | Full comparison |
| Neoteric | Organizations wanting a structured feasibility/strategy phase before committing... | Two-decade operating history combined with a formal upfront feasibility-assessment stage before any model-building work begins. | 4.5 | Full comparison |
| Addepto | Cost-conscious teams that specifically need MLOps consulting or... | Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option. | 4.4 | Full comparison |
| N-iX | Enterprise buyers wanting a large, heavily certified engineering... | Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice. | 4.4 | Full comparison |
| InData Labs | Companies needing a focused predictive-analytics or computer-vision model... | Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. | 4.3 | Full comparison |
| MobiDev | Small and mid-sized companies wanting a dedicated ML/data-science... | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Companies needing a research-oriented boutique for NLP, digital... | R&D-first culture with named specializations in digital signal processing and NLP that are less commonly offered as distinct practice areas by peers. | 4.2 | Full comparison |
| Sigmoid | Enterprises whose primary bottleneck is data infrastructure and... | Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse. | 4.2 | Full comparison |
| Tredence | Enterprises needing vertical-specific analytics and ML applied to... | Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting. | 4.2 | Full comparison |
| Sigma Software Group | Companies wanting a large, diversified engineering group with... | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting an enterprise-name client roster and a... | Unusually strong roster of large, publicly named enterprise clients (EY, Qualcomm, London Stock Exchange) for a company of its relatively modest team size. | 4.1 | Full comparison |
| ELEKS | Enterprises wanting a long-established European software engineering partner... | One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. | 4.1 | Full comparison |
| Fractal Analytics | Large enterprises wanting a scaled analytics and AI... | Maintains a dedicated internal foundational AI research team alongside client delivery work, and is now a publicly listed company (NSE/BSE) rather than privately held like most peers of similar size. | 4.1 | Full comparison |
| Xebia | Enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has... | Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone. | 4.0 | Full comparison |
| Grid Dynamics | Fortune 1000 companies wanting the financial transparency and... | The only publicly traded company (NASDAQ: GDYN) in this comparison among the mid-to-large tier, giving buyers audited financial transparency unavailable from private peers. | 4.0 | Full comparison |
| Iterate.ai | Data-sensitive enterprises (e.g., regulated industries) that require AI... | Purpose-built for on-premise/private-infrastructure AI deployment, so client data and proprietary code never leave the client's own environment. | 4.0 | Full comparison |
| Modus Create | Distributed organizations wanting a remote-first partner that pairs... | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 4.0 | Full comparison |
| Aptus Data Labs | Companies wanting a boutique, India-based data engineering and... | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises needing edge computer vision or asset-monitoring ML... | Only company in this list simultaneously holding AWS Premier, Google Cloud AI/ML Specialization, and NVIDIA Elite Consulting Partner status, reflecting particular strength in edge and GPU-accelerated computer vision. | 4.0 | Full comparison |
| DataRobot | Enterprises that want to standardize on a single... | The only platform-first vendor in this comparison, meaning model development work happens on and around DataRobot's own automated ML software rather than being platform-agnostic. | 3.9 | Full comparison |
| Persistent Systems | Mid-market and enterprise buyers wanting a publicly traded,... | Purpose-built DxH accelerator suite for MLOps and bias detection, plus a specific Everest Group Leader ranking in the mid-market Data & AI segment rather than only the largest enterprise tier. | 3.9 | Full comparison |
| EPAM Systems | Very large enterprises wanting a publicly traded, AWS... | Proprietary EPAM DIAL platform for enterprise AI orchestration, combined with the 2025 AWS Global Innovation Partner of the Year distinction, an award-level differentiator not held by most peers. | 3.9 | Full comparison |
| Globant | Large enterprises wanting industry-specific pre-packaged AI solutions ("AI... | Only company in this list organized around a formal "studio + AI Pods" delivery model, and the only one with an IDC MarketScape Worldwide Leader in AI Services designation. | 3.9 | Full comparison |
| LTIMindtree | Large enterprises, particularly in BFSI and technology/media sectors,... | Explicit ModelOps templates and model-governance/responsible-AI tooling as named, productized capabilities rather than only bespoke consulting delivery, backed by an IBM watsonx Center of Excellence. | 3.9 | Full comparison |
| Cognizant | Large enterprises, especially in healthcare, wanting a very... | Dedicated, named MLOps platform specifically built for healthcare, combined with one of the largest disclosed data/AI consultant headcounts (23,000+) in this comparison. | 3.9 | Full comparison |
| HCLTech | Very large enterprises wanting a full-stack AI vendor... | Unusually broad "chip-to-cloud" AI stack claim backed by two named proprietary platforms (Graviton for ML development, AION for AI lifecycle management), a combination not matched by most peers in this list. | 3.9 | Full comparison |
| Infosys | Very large global enterprises wanting a substantial library... | Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds. | 3.9 | Full comparison |
| Accenture | The largest global enterprises needing AI model development... | By far the largest scale of any company in this comparison (approximately 779,000 employees, $69.67B FY2025 revenue), trading breadth and compliance maturity for less niche, hands-on model-engineering depth than boutique specialists. | 3.9 | Full comparison |
| Devbridge (a Cognizant company) | Clients who want Devbridge's original product-engineering delivery model... | The clearest ownership-change disclosure in this comparison: a formerly independent boutique now operating explicitly as a Cognizant subsidiary, combining boutique delivery heritage with large-parent-company backing. | 3.8 | Full comparison |
Quantiphi FAQ
What is Quantiphi?
Quantiphi is a digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, focused on applied artificial intelligence, machine learning, and data science for complex business problems. Headquartered in Marlborough, Massachusetts, the company operates across six global locations and reports between 1,000 and 5,000 employees. Quantiphi holds AWS Premier Global Consulting Partner status and was named the first Preferred Amazon Quick Global SI Partner by the AWS Generative AI Innovation Center, alongside being recognized as 2025 AWS Public Sector Global GenAI Consulting Partner of the Year.
How much does Quantiphi charge?
Quantiphi uses not published; enterprise project engagements pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does Quantiphi use?
Quantiphi works with AWS SageMaker, Amazon Bedrock, AWS, Python, Kubernetes. Primary industries served include Public sector, Healthcare, Financial services, Media.
Is Quantiphi right for enterprise?
Enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison.. 1,001–5,000 team size. Key consideration: Heavy AWS specialization may be less useful for clients standardized on Azure or GCP..
What are the best Quantiphi alternatives?
The best alternatives to Quantiphi depend on your use case. Top options are:
- Tensorway: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
- Neurons Lab: end-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services.
- DataRoot Labs: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).
Compare Quantiphi with other ML Model Development companies
Quantiphi vs Tensorway Quantiphi vs Neurons Lab Quantiphi vs DataRoot Labs Quantiphi vs Miquido Quantiphi vs Provectus Quantiphi vs Neoteric Quantiphi vs Addepto Quantiphi vs N-iX Quantiphi vs InData Labs Quantiphi vs MobiDev Quantiphi vs Sciforce Quantiphi vs Sigmoid Quantiphi vs Tredence Quantiphi vs Sigma Software Group Quantiphi vs Intellectsoft Quantiphi vs ELEKS Quantiphi vs Fractal Analytics Quantiphi vs Xebia Quantiphi vs Grid Dynamics Quantiphi vs Iterate.ai Quantiphi vs Modus Create Quantiphi vs Aptus Data Labs Quantiphi vs SoftServe Quantiphi vs DataRobot Quantiphi vs Persistent Systems Quantiphi vs EPAM Systems Quantiphi vs Globant Quantiphi vs LTIMindtree Quantiphi vs Cognizant Quantiphi vs HCLTech Quantiphi vs Infosys Quantiphi vs Accenture Quantiphi vs Devbridge (a Cognizant company)
Last reviewed: July 2026. Verify all details directly with Quantiphi before making a decision.