EPAM Systems vs Kanerika: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of Kanerika (3.9/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. Kanerika is the stronger option for microsoft Fabric and Databricks shops needing AI-ready data. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs Kanerika: head-to-head summary
| Criterion | EPAM Systems | Kanerika |
|---|---|---|
| Founded | 1993 | 2015 |
| HQ | Newtown, Pennsylvania, USA | Austin, Texas, USA |
| Team size | 61,000+ | 250–500 |
| Rating | 4.6 / 5 | 3.9 / 5 |
| Primary differentiator | Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes | Platform specialists for Fabric, Databricks and Snowflake |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | Onshore, nearshore and offshore rates; time and materials; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Microsoft Fabric, Databricks, Snowflake |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Manufacturing, Financial services, Healthcare & life sciences, Logistics |
EPAM Systems vs Kanerika: 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.
Kanerika
Kanerika was founded in 2015 and is based in Austin, Texas, with offices in India, Argentina and Singapore. Directories list 250 to 500 employees. Its staff augmentation service supplies AI engineers, data engineers and platform specialists for Microsoft Fabric, Databricks and Snowflake, either as single specialists or extended teams. The company's own blog ranks it first for data and AI staff augmentation, which should be read as self-promotion.
Services and capabilities: EPAM Systems vs Kanerika
| Capability | EPAM Systems | Kanerika |
|---|---|---|
| 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 Kanerika
| Framework / platform | EPAM Systems | Kanerika |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | N/A |
| Azure | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: EPAM Systems vs Kanerika
| Criterion | EPAM Systems | Kanerika |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs Kanerika
| Dimension | EPAM Systems | Kanerika |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Manufacturing, Financial services, 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 Fabric engineer before an analytics copilot rollout, Migrating data to Databricks for ML workloads |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
EPAM Systems vs Kanerika: 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 |
| Kanerika | |
|---|---|
| + | Clear specialization in the data platforms most AI work depends on |
| + | Onshore, nearshore and offshore rate options |
| + | Can supply one specialist or a full team |
| - | Few independent client reviews |
| - | Its self-published rankings should not be treated as evidence |
| - | Less depth in model research than AI-only firms |
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 Kanerika?
A typical fit: adding a Fabric engineer before an analytics copilot rollout.
Platform specialists for Fabric, Databricks and Snowflake. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Financial services, Healthcare & life sciences, Logistics.
Decision matrix: EPAM Systems vs Kanerika
| 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 | EPAM Systems |
| 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 Kanerika (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 Kanerika
| Use case | EPAM Systems fit | Kanerika 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 Fabric engineer before an analytics copilot rollout | Strong | Strong | Both equally |
| Migrating data to Databricks for ML workloads | Limited | Strong | Kanerika |
Verdict: EPAM Systems vs Kanerika
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.
Kanerika (3.9/5) is worth a look if you need migrating data to Databricks for ML workloads. If your situation matches that, Kanerika is a competitive option.
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EPAM Systems vs Kanerika FAQ
Is EPAM Systems better than Kanerika?
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. Kanerika's strongest advantage: clear specialization in the data platforms most AI work depends on.
How do EPAM Systems and Kanerika differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. Kanerika uses onshore, nearshore and offshore rates; time and materials; 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 Kanerika?
Kanerika 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 Kanerika?
EPAM Systems's primary differentiator is: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. Kanerika's primary differentiator is: platform specialists for Fabric, Databricks and Snowflake. They also differ in team size (61,000+ vs 250–500), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Manufacturing, Financial services).
Verify all details directly with each company before making a decision.