deepsense.ai vs EPAM Systems: full comparison for 2026
Quick verdict
deepsense.ai (4.6/5) edges ahead of EPAM Systems (3.9/5) overall. deepsense.ai is the better choice for research-heavy ML problems, senior data scientists. EPAM Systems is the stronger option for global enterprises with large, compliance-heavy AI programs. The right choice depends on your project size, budget, and required tech stack.
deepsense.ai vs EPAM Systems: head-to-head summary
| Criterion | deepsense.ai | EPAM Systems |
|---|---|---|
| Founded | 2014 | 1993 |
| HQ | Warsaw, Poland | Newtown, Pennsylvania, USA |
| Team size | 100–200 | 62,850 |
| Rating | 4.6 / 5 | 3.9 / 5 |
| Primary differentiator | Research-grade data scientists available as embedded team members | Scale, compliance maturity and vendor certifications |
| Pricing model | Time and materials; dedicated team; rates on request | Enterprise time and materials; dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | PyTorch, TensorFlow, Hugging Face | Claude, OpenAI, Gemini |
| Industries served | Retail and e-commerce, Manufacturing, Healthcare, Fintech | Fintech, Healthcare, Retail, Manufacturing, Travel |
deepsense.ai vs EPAM Systems: overview
deepsense.ai
deepsense.ai was founded in 2014 in Warsaw, Poland, and keeps a second office in Palo Alto. It is an AI-first company whose work spans generative AI, LLMs, retrieval-augmented generation, MLOps, computer vision and edge AI. Its team-augmentation offer draws on a staff of more than 100 data scientists, data engineers and software engineers, a group that includes Kaggle competition winners and PhD holders (per company website; independently unverifiable). Clutch reviewers describe engineers who integrate with in-house teams and add capacity on strategic projects.
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with about 62,850 employees as of June 30, 2026, of whom roughly 56,650 work in delivery. It reports more than 5,700 Claude-certified engineers and set a target of 10,000, along with thousands of OpenAI- and Gemini-certified specialists. The company is targeting $600 million in AI-native services revenue for 2026. Its model is enterprise delivery, so individual staff augmentation usually sits inside a larger program.
Services and capabilities: deepsense.ai vs EPAM Systems
| Capability | deepsense.ai | EPAM Systems |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✓ |
| MLOps & deployment | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| Data engineering | ✗ | ✓ |
| AI agent development | ✗ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: deepsense.ai vs EPAM Systems
| Framework / platform | deepsense.ai | EPAM Systems |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | N/A | ✓ |
| AWS SageMaker | ✓ | ✓ |
| Azure ML | N/A | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: deepsense.ai vs EPAM Systems
| Criterion | deepsense.ai | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Full-time dedicated engineers, Managed delivery | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: deepsense.ai vs EPAM Systems
| Dimension | deepsense.ai | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail and e-commerce, Manufacturing, Healthcare | Fintech, Healthcare, Retail |
| Best use cases | Embedding a senior data scientist in a product team with a hard modeling problem, Adding computer-vision engineers for an edge-device deployment | Staffing a multi-team GenAI program at a global bank, Adding certified Claude engineers to an enterprise AI platform |
| Typical project type | Dedicated team | Dedicated team |
deepsense.ai vs EPAM Systems: pros and cons
| deepsense.ai | |
|---|---|
| + | Every engineer comes from a company that has done nothing but applied AI since 2014 |
| + | Unusually deep bench for computer vision and edge deployment |
| + | Can supply data engineers alongside data scientists, so the people building features also get clean data |
| + | Polish base gives EU data-protection familiarity and a few hours of overlap with the U.S. East Coast |
| - | Bench of roughly 100 people limits how many concurrent placements it can take |
| - | Senior research talent is priced accordingly; rates are not published |
| - | Better suited to multi-month engagements than one-off fractional help |
| EPAM Systems | |
|---|---|
| + | Largest bench on this list, with security and compliance processes to match |
| + | Thousands of engineers certified on major model platforms |
| + | Can staff any role an AI program needs |
| - | Built for enterprise programs; a single-engineer request is a poor fit |
| - | Highest overhead and slowest procurement on this list |
| - | Rates not published |
Who should choose deepsense.ai?
A typical fit: embedding a senior data scientist in a product team with a hard modeling problem.
Research-grade data scientists available as embedded team members. Minimum engagement is not publicly disclosed. Works best with clients in Retail and e-commerce, Manufacturing, Healthcare, Fintech.
Who should choose EPAM Systems?
A typical fit: staffing a multi-team GenAI program at a global bank.
Scale, compliance maturity and vendor certifications. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Retail, Manufacturing, Travel.
Decision matrix: deepsense.ai vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | deepsense.ai |
| Your budget is at the lower end | Compare: deepsense.ai (Not disclosed) vs EPAM Systems (Not disclosed) |
| You need specialist depth in a specific vertical | EPAM Systems |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: deepsense.ai vs EPAM Systems
| Use case | deepsense.ai fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Embedding a senior data scientist in a product team with a hard modeling problem | Strong | Limited | deepsense.ai |
| Adding computer-vision engineers for an edge-device deployment | Strong | Strong | Both equally |
| Staffing a multi-team GenAI program at a global bank | Limited | Strong | EPAM Systems |
| Adding certified Claude engineers to an enterprise AI platform | Strong | Strong | Both equally |
Verdict: deepsense.ai vs EPAM Systems
deepsense.ai (4.6/5) is the stronger overall choice for most AI Staffing projects. Research-grade data scientists available as embedded team members.
EPAM Systems (3.9/5) is worth a look if you need adding certified Claude engineers to an enterprise AI platform. If your situation matches that, EPAM Systems is a competitive option.
Related comparisons
deepsense.ai vs EPAM Systems FAQ
Is deepsense.ai better than EPAM Systems?
deepsense.ai (4.6/5) scores higher overall, but "better" depends on your use case. deepsense.ai's strongest advantage: every engineer comes from a company that has done nothing but applied AI since 2014. EPAM Systems's strongest advantage: largest bench on this list, with security and compliance processes to match.
How do deepsense.ai and EPAM Systems differ in pricing?
deepsense.ai uses time and materials; dedicated team; rates on request pricing. EPAM Systems uses enterprise time and materials; dedicated teams; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: deepsense.ai or EPAM Systems?
deepsense.ai 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 deepsense.ai and EPAM Systems?
deepsense.ai's primary differentiator is: research-grade data scientists available as embedded team members. EPAM Systems's primary differentiator is: Scale, compliance maturity and vendor certifications. They also differ in team size (100–200 vs 62,850), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Retail and e-commerce, Manufacturing vs Fintech, Healthcare).
Verify all details directly with each agency before making a decision.