deepsense.ai vs EPAM Systems: full comparison for 2026
Quick verdict
deepsense.ai (4.5/5) edges ahead of EPAM Systems (4.1/5) overall. deepsense.ai is the better choice for teams that need measured accuracy before launch. EPAM Systems is the stronger option for enterprises adding AI across many products. The right choice depends on how far your pilot has got, your budget, and who will run the system after launch.
deepsense.ai vs EPAM Systems: head-to-head summary
| Criterion | deepsense.ai | EPAM Systems |
|---|---|---|
| Founded | 2014 | 1993 |
| HQ | Warsaw, Poland | Newtown, USA |
| Team size | 120+ | 61,000+ |
| Rating | 4.5 / 5 | 4.1 / 5 |
| Primary differentiator | Evaluation-first engineering that proves accuracy on your data before release | Engineering capacity to put AI into dozens of products under one program |
| Pricing model | Project-based; $100–$149/hr (Clutch band) | Time & materials and dedicated teams; rates not published |
| Min. engagement | $25,000+ (Clutch) | Not disclosed |
| Primary tech stack | Python, PyTorch, LangChain | AWS, Azure, Google Cloud |
| Industries served | Retail, Manufacturing, Financial services, Healthcare, Media | Financial services, Healthcare, Retail, Media, Manufacturing |
deepsense.ai vs EPAM Systems: overview
deepsense.ai
Founded in Warsaw in 2014, deepsense.ai has about 120 AI specialists and an office in Palo Alto. Its work covers language models, retrieval search, and computer vision, and its teams build the evaluation sets that tell you whether a system is accurate enough to release. That evaluation habit is the reason it ranks high here. Clutch lists $100–$149 an hour and a $25,000+ minimum project, which is expensive for a Polish firm.
EPAM Systems
EPAM Systems was founded in 1993, is headquartered in Newtown, Pennsylvania, and employs more than 61,000 people. It bought NEORIS and First Derivative in recent years to expand in Latin America and financial services. Its AI work sits inside a large software engineering business, so production AI usually arrives as part of a broader platform or product program. That suits enterprises with many products more than a company with one stalled pilot.
Services and capabilities: deepsense.ai vs EPAM Systems
| Capability | deepsense.ai | EPAM Systems |
|---|---|---|
| LLM gateway & model routing | ✗ | ✗ |
| Regression tests on prompt and model changes | ✓ | ✗ |
| Canary or staged rollout | ✗ | ✗ |
| Hallucination and drift monitoring | ✗ | ✗ |
| MLOps & model serving | ✓ | ✓ |
| Fixed-scope pilot | ✗ | ✗ |
| Managed run after launch | ✗ | ✓ |
Tech stack comparison: deepsense.ai vs EPAM Systems
| Framework / platform | deepsense.ai | EPAM Systems |
|---|---|---|
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
| MLflow | N/A | N/A |
| Databricks | N/A | ✓ |
| Amazon Bedrock | N/A | N/A |
| Vertex AI | N/A | N/A |
| LangChain | ✓ | N/A |
| Snowflake | N/A | ✓ |
Pricing comparison: deepsense.ai vs EPAM Systems
| Criterion | deepsense.ai | EPAM Systems |
|---|---|---|
| Minimum engagement | $25,000+ (Clutch) | Not disclosed |
| Engagement models | Fixed-scope project, Time & materials, Dedicated team | Time & materials, Dedicated team, Managed services |
| Rate transparency | Minimum disclosed | 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 | Enterprise |
| Best industries | Retail, Manufacturing, Financial services | Financial services, Healthcare, Retail |
| Best use cases | Retrieval search over internal documents with measured accuracy., Evaluating a vendor's AI before rolling it out. | AI across a portfolio of enterprise products., Production AI in banking platforms. |
| Typical project type | Fixed-scope project | Time & materials |
deepsense.ai vs EPAM Systems: pros and cons
| deepsense.ai | |
|---|---|
| + | Builds evaluation sets on real data before deciding a system is ready |
| + | Research-grade depth in language models and computer vision |
| + | Retrieval search projects come with measured answer accuracy |
| + | Senior engineers stay involved after the first release |
| - | Higher Clutch rate band than most Central European peers |
| - | Smaller team than the global engineering firms |
| - | No managed-run service listed after launch |
| EPAM Systems | |
|---|---|
| + | Very large engineering bench |
| + | Strong software delivery practice to build on |
| + | Financial-services depth added through First Derivative |
| + | Can run systems after launch |
| - | Recent acquisitions (NEORIS, First Derivative) are still being absorbed |
| - | Too large and slow for one small pilot |
| - | Rates not published |
Who should choose deepsense.ai?
A typical fit: retrieval search over internal documents with measured accuracy.
Evaluation-first engineering that proves accuracy on your data before release. Minimum engagement starts at $25,000+ (Clutch). Works best with clients in Retail, Manufacturing, Financial services, Healthcare, Media.
Who should choose EPAM Systems?
A typical fit: AI across a portfolio of enterprise products.
Engineering capacity to put AI into dozens of products under one program. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail, Media, Manufacturing.
Decision matrix: deepsense.ai vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| Your pilot works but nobody trusts it in production yet | Ask each for its release and rollback process |
| Model API spend is growing faster than usage | Neither lists a gateway; ask how cost per feature is tracked |
| You need someone to watch quality after launch | EPAM Systems |
| You run classic ML models that need serving and retraining | Both cover MLOps |
| You want a priced pilot before a full rollout | Neither advertises one; ask for a scoped quote |
| Your budget is at the lower end | Compare: deepsense.ai ($25,000+ (Clutch)) vs EPAM Systems (Not disclosed) |
| You need a large team for many products at once | EPAM Systems |
Use case fit: deepsense.ai vs EPAM Systems
| Use case | deepsense.ai fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Retrieval search over internal documents with measured accuracy. | Strong | Limited | deepsense.ai |
| Evaluating a vendor's AI before rolling it out. | Strong | Limited | deepsense.ai |
| AI across a portfolio of enterprise products. | Limited | Strong | EPAM Systems |
| Production AI in banking platforms. | Limited | Strong | EPAM Systems |
Verdict: deepsense.ai vs EPAM Systems
deepsense.ai (4.5/5) is the stronger overall choice for most AI Implementation projects. Evaluation-first engineering that proves accuracy on your data before release.
EPAM Systems (4.1/5) is worth a look if you need production AI in banking platforms. 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 (4.5/5) scores higher overall, but "better" depends on your use case. deepsense.ai's strongest advantage: builds evaluation sets on real data before deciding a system is ready. EPAM Systems's strongest advantage: very large engineering bench.
How do deepsense.ai and EPAM Systems differ in pricing?
deepsense.ai's pricing: project-based; $100–$149/hr (Clutch band) with a minimum engagement of $25,000+ (Clutch). EPAM Systems's pricing: time & materials and dedicated teams; rates not published. 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: deepsense.ai 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 deepsense.ai and EPAM Systems?
deepsense.ai's primary differentiator is: evaluation-first engineering that proves accuracy on your data before release. EPAM Systems's primary differentiator is: engineering capacity to put AI into dozens of products under one program. They also differ in team size (120+ vs 61,000+), minimum engagement ($25,000+ (Clutch) vs Not disclosed), and primary industries served (Retail, Manufacturing vs Financial services, Healthcare).
Verify all details directly with each provider before making a decision.