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
custom-mlmlopscomputer-visionnlpgenerative-aidata-engineering
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 |
|---|
| Custom ML Development |
| MLOps & Deployment |
| Computer Vision |
| NLP & LLMs |
| Generative AI |
| Data Engineering |
Quantiphi use cases
Short answer: Quantiphi is best suited for Enterprises, cloud-native ML, top-tier AWS/GCP credentials.
| Use case |
|---|
| Enterprise ML platform build on AWS SageMaker with MLOps pipeline and model governance |
| Healthcare computer vision system for radiology and pathology AI on Google Cloud |
| Large-scale NLP processing for media content classification and recommendation |
| Retail demand forecasting and inventory optimisation with BigQuery ML |
| Generative AI integration for financial services document review and compliance |
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 teams, specialist CV/time-series/LLM delivery. | Boutique ML depth, with access to the 25-year enterprise delivery experience of its parent company — a rare combination in the ML services market | 4.9 | Full comparison |
| LeewayHertz | Businesses, GenAI/LLM integration plus custom ML. | Among the earliest boutique firms to build a structured GenAI delivery framework — deep LLM orchestration and RAG pipeline experience | 4.1 | Full comparison |
| Scopic | Companies needing genuinely custom ML architectures. | 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 | Complex ML problems, deep data-science expertise. | 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, full ML pipeline ownership. | 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 finance/insurance firms, audit-ready ML governance. | 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, production-accountable ML partner. | 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 | EU/US enterprises, large dedicated teams, competitive rates. | 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, ML/GenAI embedded, fast time-to-demo. | 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, cloud data lakehouse... | 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, tightly integrated ML plus MLOps. | 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, AWS-native ML, validated CV/NLP/RAG results. | 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/finance orgs, HIPAA/PCI-DSS/SOC2-compliant 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 | Manufacturing/logistics enterprises, cloud-native ML plus IoT. | AWS Premier Partner specialising in connecting physical IoT sensor data to cloud-based ML models for predictive maintenance | 4.2 | Full comparison |
| Oxagile | Healthcare/media/retail enterprises, cost-effective Eastern European ML. | 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 for edge and embedded hardware. | Hardware-to-cloud ML engineering — a rare full-stack capability covering embedded device AI through cloud model serving | 4.1 | Full comparison |
| Aimprosoft | SMBs, AI consulting and custom ML at accessible... | 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, embedded senior... | Senior-only ML engineer staffing — embedded in your stack, working in your tools, without agency overhead | 4.1 | Full comparison |
| Ciklum | FinTech, Retail, Healthcare enterprises — AI product engineering... | 25+ AI products in production combined with 3,000+ global engineers — enterprise AI scale without the big-four overhead | 4.1 | Full comparison |
| Iflexion | ML newcomers, AI strategy and scoping first. | Consulting-first model ensures the ML problem is correctly defined before engineering investment begins | 4.0 | Full comparison |
| Itransition | EU-operating enterprises, GDPR/EU AI Act-compliant ML. | EU regulatory compliance depth for ML — GDPR-aligned data architecture and EU AI Act readiness built into delivery | 4.0 | Full comparison |
| DataToBiz | Startups taking an ML idea to market-ready delivery. | 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, fast teams, US time-zone aligned. | 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, large-scale ML, Fortune-500 references, EU footprint. | Named client references including Siemens, S&P Global, and Ryanair — enterprise ML track record at the highest scale | 4.0 | Full comparison |
| Intuz | SMBs, US-headquartered AI/ML at accessible rates. | 1,700+ delivered projects for SMBs — the broadest SMB ML delivery track record in this list | 3.9 | Full comparison |
| Tredence | Fortune 500 enterprises, AI analytics and supply-chain ML. | 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 orgs, end-to-end ML delivery. | 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, massive-scale MLOps, Hitachi-backed. | 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, complex AI products, governance at scale. | 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, 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, governed GenAI and agentic AI at... | 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, governed AutoML platform. | 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, cloud-native ML, top-tier AWS/GCP 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, with access to the 25-year enterprise delivery experience of its parent company — a 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