Quantiphi
AWS Premier and Google Cloud Partner of the Year specialising in AI-first digital engineering
Founded 2013 | Marlborough, MA | 1,000–5,000 employees | Last updated: July 2026
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What is Quantiphi?
Quantiphi is an AI-first digital engineering company founded in 2013 and headquartered in Marlborough, MA, with 1,001–5,000 employees. The firm holds AWS Premier Global Consulting Partner status and was named a Google Cloud Partner of the Year across four categories in 2026. Quantiphi's ML practice spans cloud-native model development, MLOps, computer vision, NLP, and generative AI, with a strong track record in healthcare, financial services, media, and retail.
Quantiphi was founded in 2013 and is headquartered in Marlborough, MA. The firm employs 1,000–5,000 people and works primarily with clients in Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce sectors. Its primary differentiator is: AWS Premier and four-time Google Cloud Partner of the Year — the highest independently verified cloud ML credentials in the market.
Quantiphi tech stack and services
TensorFlowPyTorchAWS SageMakerVertex AIApache SparkDatabricksBigQueryPython
| Service area | Details |
|---|---|
| Enterprise ML platform build on AWS SageMaker with MLOps pipeline and model governance | Available for Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce clients |
| Healthcare computer vision system for radiology and pathology AI on Google Cloud | Available for Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce clients |
| Large-scale NLP processing for media content classification and recommendation | Available for Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce clients |
| Retail demand forecasting and inventory optimisation with BigQuery ML | Available for Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce clients |
| Generative AI integration for financial services document review and compliance | Available for Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce clients |
Quantiphi use cases
Short answer: Quantiphi is best suited for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials.
| Use case | Industries | Approach |
|---|---|---|
| Enterprise ML platform build on AWS SageMaker with MLOps pipeline and model governance | Healthcare & Life Sciences, Financial Services | TensorFlow, PyTorch |
| Healthcare computer vision system for radiology and pathology AI on Google Cloud | Healthcare & Life Sciences, Financial Services | TensorFlow, PyTorch |
| Large-scale NLP processing for media content classification and recommendation | Healthcare & Life Sciences, Financial Services | TensorFlow, PyTorch |
| Retail demand forecasting and inventory optimisation with BigQuery ML | Healthcare & Life Sciences, Financial Services | TensorFlow, PyTorch |
| Generative AI integration for financial services document review and compliance | Healthcare & Life Sciences, Financial Services | TensorFlow, PyTorch |
Quantiphi pricing
Short answer: Quantiphi uses a fixed project, t&m, dedicated team pricing approach. Minimum engagement starts at $75K.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed project | From $75K | Well-defined scope |
| Time & materials | Variable; depends on team size | Large programmes or team augmentation |
| Dedicated team | 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 |
|---|---|
| +AWS Premier + Google Cloud four-time Partner of the Year — independently verified at the highest cloud tier | -$75K+ minimum engagement excludes SMB and startup budgets |
| +Named first Preferred Amazon Quick Global SI Partner by the AWS GenAI Innovation Center | -Large-firm delivery cadence can feel slower than agile boutiques for fast-moving projects |
| +Deep healthcare ML practice with imaging AI and clinical NLP deployments | -Strong AWS and GCP depth; less Azure-native capability compared to Microsoft-aligned firms |
| +Large team (1,000–5,000) supports enterprise-scale parallel programmes across multiple verticals | |
| +Covers both cloud-native SageMaker/Vertex AI and on-premise ML infrastructure |
Quantiphi vs alternatives
How Quantiphi compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market and enterprise teams needing specialist computer vision,... | Boutique ML depth combined with Anadea's 25-year enterprise delivery foundation — rare combination in the ML services market | 4.9 | Full comparison |
| LeewayHertz | Businesses that need generative AI or LLM integration... | Among the earliest boutique firms to build a structured GenAI delivery framework — deep LLM orchestration and RAG pipeline experience | 4.7 | Full comparison |
| Scopic | Companies that need genuinely custom ML architectures rather... | Engineers custom ML architectures from the ground up — not fine-tuned wrappers — with 20 years of production delivery discipline | 4.6 | Full comparison |
| InData Labs | Businesses with complex, highly specific ML problems requiring... | Boutique firm with a track record of solving atypical, high-complexity ML problems that generalist shops decline or under-deliver on | 4.6 | Full comparison |
| DATAFOREST | Mid-market companies that need a single vendor to... | Structured MLaaS delivery model — one team owns data engineering, model development, and post-deployment monitoring end-to-end | 4.5 | Full comparison |
| Forte Group | Regulated mid-market firms in financial services, insurance, or... | ML delivery built for regulated environments — model risk governance, audit trails, and compliance-aligned architecture are built in, not bolted on | 4.5 | Full comparison |
| RTS Labs | High-growth US companies that have done ML experiments... | Small by choice, senior by design — every project is staffed with senior practitioners accountable for post-launch performance, not just the plan | 4.5 | Full comparison |
| N-iX | European and US enterprises that need large dedicated... | Scale and depth in one package — 2,000+ engineers with a mature ML practice and competitive EU delivery rates | 4.4 | Full comparison |
| Miquido | Product companies that need ML or GenAI embedded... | GenAI and mobile ML integration in one team — a rare combination for companies building AI-native products for end users | 4.4 | Full comparison |
| Algoscale | Fortune 500 and growth-stage companies that need ML... | 100+ production ML deployments on AWS, Azure, and Snowflake — proven at enterprise scale with multiple cloud stacks | 4.3 | Full comparison |
| STX Next | Python-stack product companies that need ML tightly integrated... | Europe's largest Python shop — ML is embedded in full-stack Python systems with MLOps, not delivered as an isolated model | 4.3 | Full comparison |
| Intellias | Enterprises that need AWS-native ML with independently validated... | AWS AI Services Competency with verified production benchmarks — 10x TCO reduction in aerial imagery and sub-8-second NLP query latency | 4.3 | Full comparison |
| ScienceSoft | Healthcare and financial services organisations that need ML... | Over 35 years of regulated IT delivery — compliance-aligned ML architecture is a core competency, not an add-on | 4.2 | Full comparison |
| Simform | Enterprises that need cloud-native ML with IoT sensor... | AWS Premier Partner specialising in connecting physical IoT sensor data to cloud-based ML models for predictive maintenance | 4.2 | Full comparison |
| Oxagile | Enterprises in healthcare, media, or retail seeking cost-effective... | Strong connected-care and healthcare AI track record combined with 40–60% cost advantage versus US equivalents | 4.2 | Full comparison |
| Softeq | Companies building AI that must run on hardware... | Hardware-to-cloud ML engineering — a rare full-stack capability covering embedded device AI through cloud model serving | 4.1 | Full comparison |
| Aimprosoft | Small and mid-sized businesses that need AI consulting... | Full-cycle AI delivery from consulting through implementation, optimised for SMB budgets and timelines | 4.1 | Full comparison |
| Uvik Software | Teams with an existing ML codebase that need... | Senior-only ML engineer staffing — embedded in your stack, working in your tools, without agency overhead | 4.1 | Full comparison |
| Ciklum | Digital enterprises in FinTech, Retail, or Healthcare that... | 25+ AI products in production combined with 3,000+ global engineers — enterprise AI scale without the big-four overhead | 4.1 | Full comparison |
| Iflexion | Organisations new to ML that need AI strategy... | Consulting-first model ensures the ML problem is correctly defined before engineering investment begins | 4.0 | Full comparison |
| Itransition | European enterprises and US companies with EU operations... | EU regulatory compliance depth for ML — GDPR-aligned data architecture and EU AI Act readiness built into delivery | 4.0 | Full comparison |
| DataToBiz | Startups and growth-stage companies that need to take... | Product-oriented ML delivery — combines AI strategy with full-cycle engineering to produce launchable products, not just models | 4.0 | Full comparison |
| BairesDev | Enterprises and scale-ups that need large dedicated ML... | Latin American engineering delivery with US time-zone alignment — faster team ramp than Asian offshore with significant rate advantage versus US onshore | 4.0 | Full comparison |
| Andersen Lab | Enterprises needing large-scale ML delivery with named Fortune-500-level... | Named client references including Siemens, S&P Global, and Ryanair — enterprise ML track record at the highest scale | 4.0 | Full comparison |
| Intuz | Small and mid-size companies needing AI and ML... | 1,700+ delivered projects for SMBs — the broadest SMB ML delivery track record in this list | 3.9 | Full comparison |
| Tredence | Fortune 500 enterprises needing large-scale AI analytics, MLOps... | Large specialised analytics and AI firm — enterprise supply chain ML and CX analytics depth with Fortune 500 client delivery track record | 3.9 | Full comparison |
| Codiant | Budget-conscious organisations needing end-to-end ML delivery from discovery... | Cost-efficient end-to-end ML delivery covering all phases — discovery, build, integration, and optimisation — in a single engagement | 3.9 | Full comparison |
| GlobalLogic (Hitachi) | Global enterprises requiring MLOps at massive scale with... | Hitachi Group backing with 27,000 engineers — the scale and compliance posture of a major industrial conglomerate applied to enterprise ML | 3.9 | Full comparison |
| EPAM Systems | Global enterprises building complex, software-heavy AI products that... | AI-native engineering practice at 50,000-person scale — the broadest talent pool and delivery capacity of any firm on this list | 3.8 | Full comparison |
| Cognizant | Fortune 500 enterprises running multi-year AI transformation programmes... | One of the world's largest AI & Analytics practices — Fortune 500 industry vertical depth and compliance credentials at 350,000-person delivery scale | 3.8 | Full comparison |
| Accenture | Global enterprises with strict governance requirements scaling GenAI,... | Accenture's global AI practice applies consulting strategy, industry domain expertise, and engineering delivery at 700,000-person scale — designed exclusively for enterprise | 3.8 | Full comparison |
| DataRobot | Enterprise data science teams that want a governed... | Platform-driven ML — DataRobot's AutoML engine and MLOps governance layer enable internal data science teams to build and manage models at scale without per-project custom development | 3.8 | Full comparison |
Quantiphi FAQ
What is Quantiphi?
Quantiphi is an AI-first digital engineering company founded in 2013 and headquartered in Marlborough, MA, with 1,001–5,000 employees. The firm holds AWS Premier Global Consulting Partner status and was named a Google Cloud Partner of the Year across four categories in 2026. Quantiphi's ML practice spans cloud-native model development, MLOps, computer vision, NLP, and generative AI, with a strong track record in healthcare, financial services, media, and retail.
How much does Quantiphi charge?
Quantiphi uses fixed project, t&m, dedicated team pricing. Minimum engagement starts at $75K. A discovery call is required to get project-specific quotes.
What tech stack does Quantiphi use?
Quantiphi works with TensorFlow, PyTorch, AWS SageMaker, Vertex AI, Apache Spark, Databricks, BigQuery, Python. Primary industries served include Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce.
Is Quantiphi right for enterprise?
Enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials. 1,000–5,000 team size. Key consideration: $75K+ minimum engagement excludes SMB and startup budgets.
What are the best Quantiphi alternatives?
The best alternatives to Quantiphi depend on your use case. Top options are:
- Tensorway: boutique ml depth combined with anadea's 25-year enterprise delivery foundation — rare combination in the ml services market
- LeewayHertz: among the earliest boutique firms to build a structured genai delivery framework — deep llm orchestration and rag pipeline experience
- Scopic: engineers custom ml architectures from the ground up — not fine-tuned wrappers — with 20 years of production delivery discipline
Compare Quantiphi with other Machine Learning Development companies
Quantiphi vs Tensorway Quantiphi vs LeewayHertz Quantiphi vs Scopic Quantiphi vs InData Labs Quantiphi vs DATAFOREST Quantiphi vs Forte Group Quantiphi vs RTS Labs Quantiphi vs N-iX Quantiphi vs Miquido Quantiphi vs Algoscale Quantiphi vs STX Next Quantiphi vs Intellias Quantiphi vs ScienceSoft Quantiphi vs Simform Quantiphi vs Oxagile Quantiphi vs Softeq Quantiphi vs Aimprosoft Quantiphi vs Uvik Software Quantiphi vs Ciklum Quantiphi vs Iflexion Quantiphi vs Itransition Quantiphi vs DataToBiz Quantiphi vs BairesDev Quantiphi vs Andersen Lab Quantiphi vs Intuz Quantiphi vs Tredence Quantiphi vs Codiant Quantiphi vs GlobalLogic (Hitachi) Quantiphi vs EPAM Systems Quantiphi vs Cognizant Quantiphi vs Accenture Quantiphi vs DataRobot
Last reviewed: July 2026. Verify all details directly with Quantiphi before making a decision.