Xenoss vs Netguru: full comparison for 2026
Quick verdict
Xenoss (4.3/5) edges ahead of Netguru (4.0/5) overall. Xenoss is the better choice for ad-tech and high-volume data teams. Netguru is the stronger option for consumer brands adding AI to digital products. The right choice depends on your project size, budget, and required tech stack.
Xenoss vs Netguru: head-to-head summary
| Criterion | Xenoss | Netguru |
|---|---|---|
| Founded | 2013 | 2008 |
| HQ | New York, USA | Poznań, Poland |
| Team size | 50–249 | 500–999 |
| Rating | 4.3 / 5 | 4.0 / 5 |
| Primary differentiator | Data engineers with ad-tech throughput experience | Design and product talent alongside AI engineers |
| Pricing model | Time and materials; staff augmentation; rates on request | Time and materials; team augmentation; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Apache Spark, Kafka | Python, OpenAI, LangChain |
| Industries served | Ad tech, Media, Fintech, Retail and e-commerce | Retail and e-commerce, Fintech, Proptech, Mobility |
Xenoss vs Netguru: 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.
Netguru
Netguru was founded in 2008 and is based in Poznań, Poland, with 500–999 employees according to several directories. It is a certified B Corporation whose clients include IKEA, Volkswagen, OLX and Vinted. Staff augmentation and delivery centers are listed among its engagement models, and AI development is part of its service line next to mobile, web and design work.
Services and capabilities: Xenoss vs Netguru
| Capability | Xenoss | Netguru |
|---|---|---|
| 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: Xenoss vs Netguru
| Framework / platform | Xenoss | Netguru |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Xenoss vs Netguru
| Criterion | Xenoss | Netguru |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Xenoss vs Netguru
| Dimension | Xenoss | Netguru |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Ad tech, Media, Fintech | Retail and e-commerce, Fintech, Proptech |
| Best use cases | Adding streaming-data engineers ahead of an ML launch, Placing ML engineers in a bidding or attribution product | Adding an LLM feature team to a consumer app, Augmenting a retailer's digital team with GenAI and design skills |
| Typical project type | Full-time dedicated engineers | Dedicated team |
Xenoss vs Netguru: pros and cons
| 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 |
| Netguru | |
|---|---|
| + | Well-known enterprise and consumer client list |
| + | Product designers and engineers can join together |
| + | B Corp certification may matter to ESG-focused buyers |
| - | AI is a newer service line within a product agency |
| - | Agency rates sit at the higher end for Poland |
| - | Less suited to single-seat ML hires |
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.
Who should choose Netguru?
A typical fit: adding an LLM feature team to a consumer app.
Design and product talent alongside AI engineers. Minimum engagement is not publicly disclosed. Works best with clients in Retail and e-commerce, Fintech, Proptech, Mobility.
Decision matrix: Xenoss vs Netguru
| 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 | Xenoss |
| Your budget is at the lower end | Compare: Xenoss (Not disclosed) vs Netguru (Not disclosed) |
| You need specialist depth in a specific vertical | Xenoss |
| 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: Xenoss vs Netguru
| Use case | Xenoss fit | Netguru fit | Winner |
|---|---|---|---|
| Adding streaming-data engineers ahead of an ML launch | Strong | Strong | Both equally |
| Placing ML engineers in a bidding or attribution product | Strong | Limited | Xenoss |
| Adding an LLM feature team to a consumer app | Strong | Strong | Both equally |
| Augmenting a retailer's digital team with GenAI and design skills | Limited | Strong | Netguru |
Verdict: Xenoss vs Netguru
Xenoss (4.3/5) is the stronger overall choice for most AI Staffing projects. Data engineers with ad-tech throughput experience.
Netguru (4.0/5) is worth a look if you need augmenting a retailer's digital team with GenAI and design skills. If your situation matches that, Netguru is a competitive option.
Related comparisons
Xenoss vs Netguru FAQ
Is Xenoss better than Netguru?
Xenoss (4.3/5) scores higher overall, but "better" depends on your use case. Xenoss's strongest advantage: strong on real-time data infrastructure that ML features depend on. Netguru's strongest advantage: well-known enterprise and consumer client list.
How do Xenoss and Netguru differ in pricing?
Xenoss uses time and materials; staff augmentation; rates on request pricing. Netguru uses time and materials; team 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: Xenoss or Netguru?
Netguru 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 Xenoss and Netguru?
Xenoss's primary differentiator is: data engineers with ad-tech throughput experience. Netguru's primary differentiator is: design and product talent alongside AI engineers. They also differ in team size (50–249 vs 500–999), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Ad tech, Media vs Retail and e-commerce, Fintech).
Verify all details directly with each agency before making a decision.