Quantiphi vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
Quantiphi (4.4/5) edges ahead of EPAM Systems (3.8/5) overall. Quantiphi is the better choice for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials. EPAM Systems is the stronger option for global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs EPAM Systems: head-to-head summary
| Criterion | Quantiphi | EPAM Systems |
|---|---|---|
| Founded | 2013 | 1993 |
| HQ | Marlborough, MA | Newtown, PA |
| Team size | 1,000–5,000 | 50,000+ |
| Rating | 4.4 / 5 | 3.8 / 5 |
| Best for | Enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials | Global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation |
| Pricing model | Fixed project, T&M, dedicated team | Dedicated team, T&M |
| Min. engagement | $75K | ~$200K+ |
| Primary tech stack | TensorFlow, PyTorch, AWS SageMaker | Python, TensorFlow, PyTorch |
| Industries served | Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce |
Quantiphi vs EPAM Systems: overview
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.
EPAM Systems
EPAM Systems is a global software engineering and IT services company founded in 1993 and headquartered in Newtown, PA, with 50,000+ professionals. The firm offers AI-native engineering services with a focus on scaling ML, generative AI, and agentic systems across large enterprises with strict governance requirements. EPAM is a powerhouse for building complex, software-heavy AI products from scratch, though it comes at a premium price point.
Services and capabilities: Quantiphi vs EPAM Systems
| Capability | Quantiphi | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP & LLMs | ✓ | ✗ |
| MLOps & deployment | ✓ | ✓ |
| Generative AI | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: Quantiphi vs EPAM Systems
| Framework / platform | Quantiphi | EPAM Systems |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | N/A | N/A |
| Vertex AI | ✓ | N/A |
| Scikit-learn | N/A | N/A |
| Hugging Face | N/A | N/A |
| Apache Spark | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
| MLflow | N/A | N/A |
Pricing comparison: Quantiphi vs EPAM Systems
| Criterion | Quantiphi | EPAM Systems |
|---|---|---|
| Minimum engagement | $75K | ~$200K+ |
| Engagement models | Fixed project, Time & materials, Dedicated team | Dedicated team, Time & materials |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Enterprise |
Target audience comparison: Quantiphi vs EPAM Systems
| Dimension | Quantiphi | EPAM Systems |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Healthcare & Life Sciences, Financial Services, Media & Entertainment | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial |
| Best use cases | 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 | Global AI transformation programme for Fortune 100 enterprise with multi-year delivery scope, Enterprise GenAI platform with strict governance and compliance for regulated financial institution |
| Typical project type | Fixed project | Dedicated team |
Quantiphi vs EPAM Systems: pros and cons
| Quantiphi | |
|---|---|
| + | AWS Premier + Google Cloud four-time Partner of the Year — independently verified at the highest cloud tier |
| + | Named first Preferred Amazon Quick Global SI Partner by the AWS GenAI Innovation Center |
| + | Deep healthcare ML practice with imaging AI and clinical NLP deployments |
| + | 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 |
| - | $75K+ minimum engagement excludes SMB and startup budgets |
| - | Large-firm delivery cadence can feel slower than agile boutiques for fast-moving projects |
| - | Strong AWS and GCP depth; less Azure-native capability compared to Microsoft-aligned firms |
| EPAM Systems | |
|---|---|
| + | 50,000+ professionals — unmatched delivery scale for global multi-stream AI programmes |
| + | AI-native engineering practice purpose-built for scaling ML, GenAI, and agentic systems |
| + | Strict governance and compliance frameworks for regulated enterprise AI delivery |
| + | Full-stack capability from hardware infrastructure through ML models to frontend AI products |
| + | Strong US and Eastern European delivery mix for cost-performance balance at enterprise scale |
| - | ~$200K+ minimum makes EPAM inaccessible for all but the largest enterprise budgets |
| - | Large-firm overhead — procurement, contracting, and ramp-up timelines are significantly longer than boutiques |
| - | Generalist breadth means less niche ML depth than boutiques in specific domains like healthcare imaging or time-series |
Who should choose Quantiphi?
Quantiphi is the right choice for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials.
AWS Premier and four-time Google Cloud Partner of the Year — the highest independently verified cloud ML credentials in the market. Minimum engagement starts at $75K. Works best with clients in Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce.
Who should choose EPAM Systems?
EPAM Systems is the right choice for global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation.
AI-native engineering practice at 50,000-person scale — the broadest talent pool and delivery capacity of any firm on this list. Minimum engagement starts at ~$200K+. Works best with clients in Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce.
Decision matrix: Quantiphi vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Quantiphi |
| You need a large dedicated team for an ongoing programme | Quantiphi |
| Your budget is at the lower end | Quantiphi |
| You need specialist depth in a specific vertical | Quantiphi |
| You need staff augmentation or team extension | EPAM Systems |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Quantiphi vs EPAM Systems
| Use case | Quantiphi fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Enterprise ML platform build on AWS SageMaker with MLOps pipeline and model governance | Strong | Strong | Both equally |
| Healthcare computer vision system for radiology and pathology AI on Google Cloud | Strong | Limited | Quantiphi |
| Global AI transformation programme for Fortune 100 enterprise with multi-year delivery scope | Limited | Strong | EPAM Systems |
| Enterprise GenAI platform with strict governance and compliance for regulated financial institution | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Quantiphi vs EPAM Systems
Quantiphi (4.4/5) is the stronger overall choice for most Machine Learning Development projects. AWS Premier and four-time Google Cloud Partner of the Year — the highest independently verified cloud ML credentials in the market. It is best for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials.
EPAM Systems (3.8/5) is the better choice when global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation. If your situation matches those criteria, EPAM Systems is a competitive option.
Related comparisons
Quantiphi vs EPAM Systems FAQ
Is Quantiphi better than EPAM Systems?
Quantiphi (4.4/5) scores higher overall, but "better" depends on your use case. Quantiphi is better for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials. EPAM Systems is better for global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation.
How do Quantiphi and EPAM Systems differ in pricing?
Quantiphi uses fixed project, t&m, dedicated team pricing with a minimum engagement of $75K. EPAM Systems uses dedicated team, t&m pricing with a minimum engagement of ~$200K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Quantiphi or EPAM Systems?
Quantiphi is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between Quantiphi and EPAM Systems?
Quantiphi's primary differentiator is: aws premier and four-time google cloud partner of the year — the highest independently verified cloud ml credentials in the market. EPAM Systems's primary differentiator is: ai-native engineering practice at 50,000-person scale — the broadest talent pool and delivery capacity of any firm on this list. They also differ in team size (1,000–5,000 vs 50,000+), minimum engagement ($75K vs ~$200K+), and primary industries served (Healthcare & Life Sciences, Financial Services vs Financial Services, Healthcare & Life Sciences).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.