Quantiphi vs EPAM Systems: full comparison for 2026
Quick verdict
Quantiphi (4.3/5) edges ahead of EPAM Systems (4.1/5) overall. Quantiphi is the better choice for large document-heavy programs on Google Cloud or AWS. EPAM Systems is the stronger option for enterprises with multi-year engineering budgets. 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, USA | Newtown, PA, USA |
| Team size | 3,500+ | 61,000+ |
| Rating | 4.3 / 5 | 4.1 / 5 |
| Primary differentiator | Partner-of-the-year history with both Google Cloud and AWS on AI work | Engineering capacity across Europe, India, and the Americas for long AI programs |
| Pricing model | Fixed-scope projects, time & materials, and dedicated teams; rates on request | Time & materials and dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Vertex AI, AWS Bedrock, Snowflake | Azure OpenAI, AWS Bedrock, Databricks |
| Industries served | Healthcare, Insurance, Financial services, Public sector, Media | Financial services, Healthcare, Retail & e-commerce, Media, Energy |
Quantiphi vs EPAM Systems: overview
Quantiphi
Quantiphi was founded in 2013, is headquartered in Marlborough, Massachusetts, and employs over 3,500 people, most of them in India. It reports 21 Google Cloud Partner of the Year awards over ten years and three AWS AI/ML Partner of the Year awards (per company materials; independently unverifiable). Document AI, contact-center AI, and healthcare and insurance workflows make up much of its integration work. Its size lets it staff large programs while still working only on AI and data.
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with over 61,200 employees at the end of 2025. Acquisitions shaped the recent mix: NEORIS (2024) for Latin America and Iberia, and First Derivative (completed December 2024) for financial-services data. Management targets more than $600 million of AI-native revenue in 2026, after reporting over $105 million in Q4 2025. Its AI/Run tooling and Agentic QA product support large engineering programs more than single-workflow pilots.
Services and capabilities: Quantiphi vs EPAM Systems
| Capability | Quantiphi | EPAM Systems |
|---|---|---|
| CRM / ERP integration | ✗ | ✓ |
| LLM API gateway & cost control | ✗ | ✓ |
| Document processing | ✓ | ✗ |
| Conversational AI | ✓ | ✗ |
| Agentic workflows | ✗ | ✓ |
| Fixed-price pilot | ✗ | ✗ |
| Managed services after launch | ✗ | ✓ |
Tech stack comparison: Quantiphi vs EPAM Systems
| Framework / platform | Quantiphi | EPAM Systems |
|---|---|---|
| Salesforce | N/A | N/A |
| SAP | N/A | ✓ |
| Microsoft Dynamics 365 | N/A | N/A |
| HubSpot | N/A | N/A |
| Snowflake | ✓ | ✓ |
| Databricks | N/A | ✓ |
| BigQuery | ✓ | N/A |
| Azure OpenAI | N/A | ✓ |
| AWS Bedrock | ✓ | ✓ |
| Zendesk | N/A | N/A |
Pricing comparison: Quantiphi vs EPAM Systems
| Criterion | Quantiphi | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed-scope project, Time & materials, Dedicated team | Time & materials, Dedicated team, Managed services |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Quantiphi vs EPAM Systems
| Dimension | Quantiphi | EPAM Systems |
|---|---|---|
| Best company size | Mid-market to enterprise | Enterprise |
| Best industries | Healthcare, Insurance, Financial services | Financial services, Healthcare, Retail & e-commerce |
| Best use cases | Claims and medical-record extraction for insurers, Contact-center assistants on Google Cloud | Large data-platform programs that end in AI features, Agent development across several business units |
| Typical project type | Fixed-scope project | Time & materials |
Quantiphi vs EPAM Systems: pros and cons
| Quantiphi | |
|---|---|
| + | Partner depth on two hyperscalers instead of one. |
| + | Document AI and contact-center AI are mature practice areas. |
| + | Enough staff to run several workstreams in parallel. |
| + | A multi-year Google Cloud partnership announced in 2026 covers joint industry solutions. |
| - | Most delivery is offshore, so time-zone overlap with U.S. or EU teams is partial |
| - | Fixed-price pilots aren't advertised as a standard entry point |
| - | Award counts come from the company itself |
| EPAM Systems | |
|---|---|
| + | Engineering depth to staff many workstreams at once. |
| + | Public reporting on AI-native revenue gives a measurable view of the practice. |
| + | First Derivative added capital-markets data skills. |
| + | Agentic QA product addresses testing of AI-generated code. |
| - | Acquisition-driven growth (NEORIS, First Derivative) means teams are still being integrated |
| - | Not built for a small fixed-price pilot |
| - | Management flagged slower organic growth in 2026 guidance |
Who should choose Quantiphi?
A typical fit: claims and medical-record extraction for insurers.
Partner-of-the-year history with both Google Cloud and AWS on AI work. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Insurance, Financial services, Public sector, Media.
Who should choose EPAM Systems?
A typical fit: large data-platform programs that end in AI features.
Engineering capacity across Europe, India, and the Americas for long AI programs. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media, Energy.
Decision matrix: Quantiphi vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You want a priced pilot before committing to a rollout | Neither advertises one; ask for a scoped pilot quote |
| You need someone to run and monitor the system after launch | EPAM Systems |
| Your budget is at the lower end | Compare: Quantiphi (Not disclosed) vs EPAM Systems (Not disclosed) |
| The AI has to read and write in your CRM or ERP | EPAM Systems |
| You need multi-step agents acting across systems | EPAM Systems |
| You need a large team for a multi-year program | EPAM Systems |
Use case fit: Quantiphi vs EPAM Systems
| Use case | Quantiphi fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Claims and medical-record extraction for insurers | Strong | Limited | Quantiphi |
| Contact-center assistants on Google Cloud | Strong | Limited | Quantiphi |
| Large data-platform programs that end in AI features | Limited | Strong | EPAM Systems |
| Agent development across several business units | Limited | Strong | EPAM Systems |
Verdict: Quantiphi vs EPAM Systems
Quantiphi (4.3/5) is the stronger overall choice for most AI Integration Services projects. Partner-of-the-year history with both Google Cloud and AWS on AI work.
EPAM Systems (4.1/5) is worth a look if you need agent development across several business units. 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 (4.3/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: partner depth on two hyperscalers instead of one. EPAM Systems's strongest advantage: engineering depth to staff many workstreams at once.
How do Quantiphi and EPAM Systems differ in pricing?
Quantiphi's pricing: fixed-scope projects, time & materials, and dedicated teams; rates on request. EPAM Systems's pricing: time & materials and dedicated teams; rates on request. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.
Which is better for enterprise: Quantiphi or EPAM Systems?
EPAM Systems is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each provider before shortlisting.
What are the main differences between Quantiphi and EPAM Systems?
Quantiphi's primary differentiator is: partner-of-the-year history with both Google Cloud and AWS on AI work. EPAM Systems's primary differentiator is: engineering capacity across Europe, India, and the Americas for long AI programs. They also differ in team size (3,500+ vs 61,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthcare, Insurance vs Financial services, Healthcare).
Verify all details directly with each provider before making a decision.