Quantiphi vs EPAM Systems: full comparison for 2026
Quick verdict
Quantiphi (4.4/5) edges ahead of EPAM Systems (3.8/5) overall. Quantiphi is the better choice for financial-services enterprises, cloud-native AI at scale. EPAM Systems is the stronger option for largest global enterprises, AI within a massive engineering partner. 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, Massachusetts, USA | Newtown, Pennsylvania, USA |
| Team size | 1,001–5,000 | 10,000+ |
| Rating | 4.4 / 5 | 3.8 / 5 |
| Primary differentiator | AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing | Largest headcount on this list (62,000+) with NYSE-listed financial transparency and a proprietary LLM orchestration platform (EPAM DIAL) |
| Pricing model | Fixed project and managed AI services | Managed services and fixed project |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, Google Cloud Vertex AI | Python, EPAM DIAL, Azure OpenAI |
| Industries served | Financial Services, Healthcare, Media, Technology/SaaS | Financial Services, Healthcare, Retail, Technology/SaaS, Government |
Quantiphi vs EPAM Systems: overview
Quantiphi
Quantiphi is an AI-first digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, headquartered in Marlborough, Massachusetts. Reported headcount is roughly 2,670–3,927 employees depending on source, making it one of the larger, more established AI-native firms on this list, with strong focus on financial services and cloud-native ML platform engineering.
EPAM Systems
EPAM Systems is a global digital engineering company founded in 1993 by Arkadiy Dobkin and Leo Lozner, listed on the NYSE since 2012, with approximately 62,850 employees as of end of 2025. The company has built a proprietary AI orchestration platform, EPAM DIAL, for managing large language models in production, but AI/ML delivery represents one part of an enormous, broadly diversified enterprise engineering portfolio.
Services and capabilities: Quantiphi vs EPAM Systems
| Capability | Quantiphi | EPAM Systems |
|---|---|---|
| Custom ML model development | ✓ | ✓ |
| Deep learning & computer vision | ✗ | ✗ |
| NLP & LLM / Generative AI | ✗ | ✓ |
| MLOps & production deployment | ✓ | ✓ |
| Data engineering | ✓ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: Quantiphi vs EPAM Systems
| Framework / platform | Quantiphi | EPAM Systems |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | ✓ |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: Quantiphi vs EPAM Systems
| Criterion | Quantiphi | EPAM Systems |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Managed services | Managed services, Fixed project, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / not published | Enterprise / not published |
Target audience comparison: Quantiphi vs EPAM Systems
| Dimension | Quantiphi | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Financial Services, Healthcare, Media | Financial Services, Healthcare, Retail |
| Best use cases | Enterprise financial-services AI programs requiring both scale and deep ML expertise, Cloud-native ML platform builds on GCP, AWS, or Azure at production scale | Global enterprises needing AI delivered at a scale only a 60,000+ employee firm can support, Programs that specifically want to leverage the EPAM DIAL LLM orchestration platform |
| Typical project type | Fixed project | Managed services |
Quantiphi vs EPAM Systems: pros and cons
| Quantiphi | |
|---|---|
| + | Founded as an AI-first company rather than a generalist IT firm that later added an AI practice |
| + | Enterprise-scale headcount (2,600+) supports large, multi-region programs |
| + | Strong cloud-native ML platform engineering, reducing gaps between model development and production deployment |
| + | 13 years of continuous focus on applied AI and analytics |
| - | Scale and enterprise sales process may be slower and less accessible for small pilot projects than boutique competitors |
| - | Recent employee counts show a reported year-over-year headcount decline (~4% per one source), worth asking about directly |
| - | Minimum engagement size and standard pricing are not publicly disclosed |
| EPAM Systems | |
|---|---|
| + | Largest, most globally distributed team on this list, supporting essentially unlimited program scale |
| + | NYSE listing (since 2012) provides the highest level of public financial transparency among firms reviewed here |
| + | Proprietary EPAM DIAL platform for LLM orchestration shows real internal AI infrastructure investment |
| + | 32 years of continuous operation across more than 55 countries |
| - | AI/ML is a specialization within an enormous generalist engineering portfolio, not the company's defining focus |
| - | Scale of the organization can translate into higher account-management overhead for smaller engagements |
| - | Buyers wanting a boutique, founder-accessible relationship will find that better served by smaller firms on this list |
Who should choose Quantiphi?
A typical fit: enterprise financial-services AI programs requiring both scale and deep ML expertise.
AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing. Minimum engagement starts at Not published. Works best with clients in Financial Services, Healthcare, Media, Technology/SaaS.
Who should choose EPAM Systems?
A typical fit: global enterprises needing AI delivered at a scale only a 60,000+ employee firm can support.
Largest headcount on this list (62,000+) with NYSE-listed financial transparency and a proprietary LLM orchestration platform (EPAM DIAL). Minimum engagement starts at Not published. Works best with clients in Financial Services, Healthcare, Retail, Technology/SaaS, Government.
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 | Check each company's engagement model |
| Your budget is at the lower end | Compare: Quantiphi (Not published) vs EPAM Systems (Not published) |
| You need specialist depth in a specific vertical | EPAM Systems |
| You need staff augmentation or team extension | EPAM Systems |
| You need consulting before committing to a build | Quantiphi |
Use case fit: Quantiphi vs EPAM Systems
| Use case | Quantiphi fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Enterprise financial-services AI programs requiring both scale and deep ML expertise | Strong | Strong | Both equally |
| Cloud-native ML platform builds on GCP, AWS, or Azure at production scale | Strong | Limited | Quantiphi |
| Global enterprises needing AI delivered at a scale only a 60,000+ employee firm can support | Limited | Strong | EPAM Systems |
| Programs that specifically want to leverage the EPAM DIAL LLM orchestration platform | 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. AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing.
EPAM Systems (3.8/5) is worth a look if you need programs that specifically want to leverage the EPAM DIAL LLM orchestration platform. If your situation matches that, 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's strongest advantage: founded as an AI-first company rather than a generalist IT firm that later added an AI practice. EPAM Systems's strongest advantage: Largest, most globally distributed team on this list, supporting essentially unlimited program scale.
How do Quantiphi and EPAM Systems differ in pricing?
Quantiphi uses fixed project and managed ai services pricing with a minimum engagement of Not published. EPAM Systems uses managed services and fixed project pricing with a minimum engagement of Not published. 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 agency before shortlisting.
What are the main differences between Quantiphi and EPAM Systems?
Quantiphi's primary differentiator is: AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing. EPAM Systems's primary differentiator is: largest headcount on this list (62,000+) with NYSE-listed financial transparency and a proprietary LLM orchestration platform (EPAM DIAL). They also differ in team size (1,001–5,000 vs 10,000+), minimum engagement (Not published vs Not published), and primary industries served (Financial Services, Healthcare vs Financial Services, Healthcare).