N-iX vs Xenoss: full comparison for 2026
Quick verdict
N-iX (4.3/5) edges ahead of Xenoss (4.3/5) overall. N-iX is the better choice for enterprises scaling data and ML teams in Europe. Xenoss is the stronger option for ad-tech and high-volume data teams. The right choice depends on your project size, budget, and required tech stack.
N-iX vs Xenoss: head-to-head summary
| Criterion | N-iX | Xenoss |
|---|---|---|
| Founded | 2002 | 2013 |
| HQ | Lviv, Ukraine (offices across Europe and the Americas) | New York, USA |
| Team size | 2,000–2,500 | 50–249 |
| Rating | 4.3 / 5 | 4.3 / 5 |
| Primary differentiator | Formal staff-augmentation model backed by a 2,400-person bench | Data engineers with ad-tech throughput experience |
| Pricing model | Time and materials; dedicated team; rates on request | Time and materials; staff augmentation; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Databricks, Apache Spark | Python, Apache Spark, Kafka |
| Industries served | Fintech, Manufacturing, Logistics, Healthcare, Telecom | Ad tech, Media, Fintech, Retail and e-commerce |
N-iX vs Xenoss: overview
N-iX
N-iX started in Lviv, Ukraine, in 2002 and now reports about 2,400 professionals across more than 25 countries in Europe and the Americas. Staff augmentation sits alongside managed teams and full-solution delivery as one of its three cooperation models, and its AI and machine-learning practice is supported by data-engineering and cloud groups. Clutch reviewers describe it as quick to scale teams and good at integrating developers into existing groups. It serves more than 80 active enterprise clients according to a 2026 company overview.
Xenoss
Xenoss was founded in 2013 by ad-tech veterans led by CEO Dmitry Sverdlik and is based in New York, with offices in London and Kyiv. It describes itself as a specialized AI and data-engineering company, and Clutch places it in the 50–249 employee band. Client reviews describe staff augmentation in practice: one London ad-tech client hired Xenoss after failing to find engineers locally, and Xenoss sourced candidates from Ukraine and integrated them into the in-house team. Its background in high-throughput ad-tech systems shows in its data-engineering work.
Services and capabilities: N-iX vs Xenoss
| Capability | N-iX | Xenoss |
|---|---|---|
| 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: N-iX vs Xenoss
| Framework / platform | N-iX | Xenoss |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: N-iX vs Xenoss
| Criterion | N-iX | Xenoss |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| 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: N-iX vs Xenoss
| Dimension | N-iX | Xenoss |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Manufacturing, Logistics | Ad tech, Media, Fintech |
| Best use cases | Adding data engineers to an enterprise lakehouse program, Staffing an MLOps engineer to productionize existing models | Adding streaming-data engineers ahead of an ML launch, Placing ML engineers in a bidding or attribution product |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
N-iX vs Xenoss: pros and cons
| N-iX | |
|---|---|
| + | Large enough to staff data, ML and platform roles from one vendor |
| + | Staff augmentation is a defined product with its own process |
| + | Delivery hubs in several EU countries help with data-residency questions |
| + | Long enterprise client history |
| - | AI is one practice inside a broad software company |
| - | Enterprise sales process can be slow for a single-seat request |
| - | No public rates |
| Xenoss | |
|---|---|
| + | Strong on real-time data infrastructure that ML features depend on |
| + | Has placed engineers into UK teams that struggled to hire locally |
| + | Senior leadership comes from the industry it serves most |
| + | Covers both data engineering and model work |
| - | Ad-tech focus is narrower than general AI staffing |
| - | Mid-sized bench |
| - | Rates are not public |
Who should choose N-iX?
A typical fit: adding data engineers to an enterprise lakehouse program.
Formal staff-augmentation model backed by a 2,400-person bench. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Manufacturing, Logistics, Healthcare, Telecom.
Who should choose Xenoss?
A typical fit: adding streaming-data engineers ahead of an ML launch.
Data engineers with ad-tech throughput experience. Minimum engagement is not publicly disclosed. Works best with clients in Ad tech, Media, Fintech, Retail and e-commerce.
Decision matrix: N-iX vs Xenoss
| 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 | N-iX |
| Your budget is at the lower end | Compare: N-iX (Not disclosed) vs Xenoss (Not disclosed) |
| You need specialist depth in a specific vertical | N-iX |
| 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: N-iX vs Xenoss
| Use case | N-iX fit | Xenoss fit | Winner |
|---|---|---|---|
| Adding data engineers to an enterprise lakehouse program | Strong | Strong | Both equally |
| Staffing an MLOps engineer to productionize existing models | Strong | Limited | N-iX |
| Adding streaming-data engineers ahead of an ML launch | Strong | Strong | Both equally |
| Placing ML engineers in a bidding or attribution product | Limited | Strong | Xenoss |
Verdict: N-iX vs Xenoss
N-iX (4.3/5) is the stronger overall choice for most AI Staffing projects. Formal staff-augmentation model backed by a 2,400-person bench.
Xenoss (4.3/5) is worth a look if you need placing ML engineers in a bidding or attribution product. If your situation matches that, Xenoss is a competitive option.
Related comparisons
N-iX vs Xenoss FAQ
Is N-iX better than Xenoss?
N-iX (4.3/5) scores higher overall, but "better" depends on your use case. N-iX's strongest advantage: large enough to staff data, ML and platform roles from one vendor. Xenoss's strongest advantage: strong on real-time data infrastructure that ML features depend on.
How do N-iX and Xenoss differ in pricing?
N-iX uses time and materials; dedicated team; rates on request pricing. Xenoss uses time and materials; staff augmentation; 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: N-iX or Xenoss?
N-iX 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 N-iX and Xenoss?
N-iX's primary differentiator is: formal staff-augmentation model backed by a 2,400-person bench. Xenoss's primary differentiator is: data engineers with ad-tech throughput experience. They also differ in team size (2,000–2,500 vs 50–249), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Fintech, Manufacturing vs Ad tech, Media).
Verify all details directly with each agency before making a decision.