EPAM Systems vs deepsense.ai: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of deepsense.ai (4.3/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. deepsense.ai is the stronger option for research-heavy ML problems, computer vision, edge AI. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs deepsense.ai: head-to-head summary
| Criterion | EPAM Systems | deepsense.ai |
|---|---|---|
| Founded | 1993 | 2014 |
| HQ | Newtown, Pennsylvania, USA | Warsaw, Poland |
| Team size | 61,000+ | 100–200 |
| Rating | 4.6 / 5 | 4.3 / 5 |
| Primary differentiator | Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes | A pure applied-AI firm whose augmented engineers come from a research-grade data-science bench |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | Time and materials for augmented engineers; project contracts; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Manufacturing, Retail & e-commerce, Healthcare & life sciences, Financial services, Software & SaaS |
EPAM Systems vs deepsense.ai: overview
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with roughly 61,000 employees across delivery centers in Europe, the Americas and Asia. It is a public company listed on the New York Stock Exchange. On its Q2 2026 earnings call, management said EPAM had more than 5,700 Anthropic-certified engineers and was among the five largest certified partners worldwide, with AI-native work making up about 11% of revenue. EPAM employs its engineers directly and sells them as augmented capacity, dedicated teams or managed programs, though most large accounts end up in the managed model.
deepsense.ai
deepsense.ai was founded in 2014, grew out of the AI division of CodiLime, and is headquartered in Warsaw with an office in Palo Alto. Third-party directories put its headcount between roughly 100 and 200 people, and the company says it employs more than 120 AI experts, including Kaggle competition winners and PhD holders. Besides project work in generative AI, MLOps, computer vision and edge AI, it runs a dedicated AI staff augmentation service in which its own engineers extend a client's team.
Services and capabilities: EPAM Systems vs deepsense.ai
| Capability | EPAM Systems | deepsense.ai |
|---|---|---|
| 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: EPAM Systems vs deepsense.ai
| Framework / platform | EPAM Systems | deepsense.ai |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | ✓ |
| Hugging Face | N/A | ✓ |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Databricks | ✓ | N/A |
| MLflow | N/A | ✓ |
| Kubernetes | ✓ | ✓ |
Pricing comparison: EPAM Systems vs deepsense.ai
| Criterion | EPAM Systems | deepsense.ai |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs deepsense.ai
| Dimension | EPAM Systems | deepsense.ai |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Manufacturing, Retail & e-commerce, Healthcare & life sciences |
| Best use cases | Adding a 15-person GenAI squad to a bank's existing platform team, Rolling out agentic workflows across several business units under one master agreement | Adding a computer-vision specialist to a manufacturing quality team, Bringing research depth into a stalled model-accuracy effort |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
EPAM Systems vs deepsense.ai: pros and cons
| EPAM Systems | |
|---|---|
| + | A Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list |
| + | Public-company reporting, audited financials and mature security reviews make vendor onboarding easier at banks and insurers |
| + | Can staff ten or more AI engineers in parallel across several time zones without running out of senior people |
| + | Deep data-platform practice means LLM work and the data engineering under it can come from one supplier |
| - | Rates are among the highest on this list and are only shared after scoping |
| - | Small requests for one or two engineers rarely get the same attention as large programs |
| - | Engagements tend to drift toward managed delivery, which moves decisions away from your own team |
| deepsense.ai | |
|---|---|
| + | Every engineer it places comes from an AI-only company |
| + | Strong record in computer vision and edge deployment |
| + | Clutch reviewers describe team-augmentation work with strong engineering skills |
| - | A bench of roughly 120 AI staff limits how many people can start at once |
| - | Polish rates are higher than Ukrainian or Latin American alternatives |
| - | Better suited to hard modeling work than to routine LLM integration |
Who should choose EPAM Systems?
A typical fit: adding a 15-person GenAI squad to a bank's existing platform team.
Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing.
Who should choose deepsense.ai?
A typical fit: adding a computer-vision specialist to a manufacturing quality team.
A pure applied-AI firm whose augmented engineers come from a research-grade data-science bench. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Retail & e-commerce, Healthcare & life sciences, Financial services, Software & SaaS.
Decision matrix: EPAM Systems vs deepsense.ai
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; EPAM Systems rates higher overall |
| You want the supplier to own delivery as well as staffing | Both; EPAM Systems rates higher overall |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: EPAM Systems (Not published) vs deepsense.ai (Not published) |
| You need overlap with U.S. working hours | Neither is nearshore; agree overlap hours up front |
| You need specialist depth in a specific vertical | EPAM Systems |
Use case fit: EPAM Systems vs deepsense.ai
| Use case | EPAM Systems fit | deepsense.ai fit | Winner |
|---|---|---|---|
| Adding a 15-person GenAI squad to a bank's existing platform team | Strong | Strong | Both equally |
| Rolling out agentic workflows across several business units under one master agreement | Strong | Limited | EPAM Systems |
| Adding a computer-vision specialist to a manufacturing quality team | Strong | Strong | Both equally |
| Bringing research depth into a stalled model-accuracy effort | Limited | Strong | deepsense.ai |
Verdict: EPAM Systems vs deepsense.ai
EPAM Systems (4.6/5) is the stronger overall choice for most AI Staff Augmentation projects. Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes.
deepsense.ai (4.3/5) is worth a look if you need bringing research depth into a stalled model-accuracy effort. If your situation matches that, deepsense.ai is a competitive option.
Related comparisons
EPAM Systems vs deepsense.ai FAQ
Is EPAM Systems better than deepsense.ai?
EPAM Systems (4.6/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: a Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list. deepsense.ai's strongest advantage: every engineer it places comes from an AI-only company.
How do EPAM Systems and deepsense.ai differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. deepsense.ai uses time and materials for augmented engineers; project contracts; 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: EPAM Systems or deepsense.ai?
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 company before shortlisting.
What are the main differences between EPAM Systems and deepsense.ai?
EPAM Systems's primary differentiator is: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. deepsense.ai's primary differentiator is: a pure applied-AI firm whose augmented engineers come from a research-grade data-science bench. They also differ in team size (61,000+ vs 100–200), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Manufacturing, Retail & e-commerce).
Verify all details directly with each company before making a decision.