Azumo vs Xenoss: full comparison for 2026
Quick verdict
Azumo (4.1/5) edges ahead of Xenoss (3.8/5) overall. Azumo is the better choice for nearshore LLM and NLP builds for U.S. mid-market. Xenoss is the stronger option for AdTech and MarTech firms needing real-time data plus AI. The right choice depends on your project size, budget, and required tech stack.
Azumo vs Xenoss: head-to-head summary
| Criterion | Azumo | Xenoss |
|---|---|---|
| Founded | 2016 | 2013 |
| HQ | San Francisco, California, USA | New York, New York, USA |
| Team size | 100–500 (sources vary) | 100–200 |
| Rating | 4.1 / 5 | 3.8 / 5 |
| Primary differentiator | A nearshore team that also builds its own NLP products | Real-time, high-load data engineering from AdTech roots |
| Pricing model | Monthly rates for augmented engineers; dedicated teams; project pricing; rates on request | Team extension and project pricing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, LangChain, OpenAI | Python, Kafka, Spark |
| Industries served | Healthcare & life sciences, Media, Software & SaaS, Financial services | Media, Retail & e-commerce, Software & SaaS |
Azumo vs Xenoss: overview
Azumo
Azumo is headquartered in San Francisco and has built AI-driven applications since 2016, with most of its engineers in Latin America. Directory headcounts range from under 100 to several hundred people. It offers staff augmentation, dedicated teams and full product outsourcing, and it also maintains its own AI products, including an NLU toolkit. Named clients include Meta and UnitedHealth (per company website; independently unverifiable).
Xenoss
Xenoss was founded in 2013 by AdTech veterans and lists its headquarters in New York, with CEO Dmitry Sverdlik. Directories put headcount between 100 and 200. It specializes in AI and data engineering, including AI agents, real-time data systems and LLM knowledge bases, and favors small senior teams. Team extension appears in its history, but it does not run a dedicated staff augmentation offer.
Services and capabilities: Azumo vs Xenoss
| Capability | Azumo | 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: Azumo vs Xenoss
| Framework / platform | Azumo | Xenoss |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Azumo vs Xenoss
| Criterion | Azumo | Xenoss |
|---|---|---|
| Minimum engagement | Not published | Not published |
| 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: Azumo vs Xenoss
| Dimension | Azumo | Xenoss |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare & life sciences, Media, Software & SaaS | Media, Retail & e-commerce, Software & SaaS |
| Best use cases | Adding a conversational-AI engineer to a healthcare app team, Building a document-search assistant on internal knowledge | Adding real-time feature engineering for a bidding model, Building an LLM knowledge base on marketing data |
| Typical project type | Full-time dedicated engineers | Dedicated team |
Azumo vs Xenoss: pros and cons
| Azumo | |
|---|---|
| + | Its own AI products show applied NLP experience |
| + | Latin American engineers share U.S. working hours |
| + | Flexible mix of augmentation and project delivery |
| - | Headcount reports vary widely, so ask how many AI engineers are actually on staff |
| - | Smaller bench than the large nearshore firms on this list |
| - | Founding year differs across sources (2013 or 2016) |
| Xenoss | |
|---|---|
| + | High-load, real-time data experience |
| + | Small senior teams with low management overhead |
| + | Builds agents and knowledge bases on its own data work |
| - | No dedicated staff augmentation page |
| - | Industry focus is narrow outside AdTech and MarTech |
| - | Headcount data varies |
Who should choose Azumo?
A typical fit: adding a conversational-AI engineer to a healthcare app team.
A nearshore team that also builds its own NLP products. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare & life sciences, Media, Software & SaaS, Financial services.
Who should choose Xenoss?
A typical fit: adding real-time feature engineering for a bidding model.
Real-time, high-load data engineering from AdTech roots. Minimum engagement is not publicly disclosed. Works best with clients in Media, Retail & e-commerce, Software & SaaS.
Decision matrix: Azumo vs Xenoss
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; Azumo rates higher overall |
| You want the supplier to own delivery as well as staffing | Both; Azumo 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: Azumo (Not published) vs Xenoss (Not published) |
| You need overlap with U.S. working hours | Azumo |
| You need specialist depth in a specific vertical | Azumo |
Use case fit: Azumo vs Xenoss
| Use case | Azumo fit | Xenoss fit | Winner |
|---|---|---|---|
| Adding a conversational-AI engineer to a healthcare app team | Strong | Strong | Both equally |
| Building a document-search assistant on internal knowledge | Strong | Strong | Both equally |
| Adding real-time feature engineering for a bidding model | Strong | Strong | Both equally |
| Building an LLM knowledge base on marketing data | Strong | Strong | Both equally |
Verdict: Azumo vs Xenoss
Azumo (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. A nearshore team that also builds its own NLP products.
Xenoss (3.8/5) is worth a look if you need building an LLM knowledge base on marketing data. If your situation matches that, Xenoss is a competitive option.
Related comparisons
Azumo vs Xenoss FAQ
Is Azumo better than Xenoss?
Azumo (4.1/5) scores higher overall, but "better" depends on your use case. Azumo's strongest advantage: its own AI products show applied NLP experience. Xenoss's strongest advantage: High-load, real-time data experience.
How do Azumo and Xenoss differ in pricing?
Azumo uses monthly rates for augmented engineers; dedicated teams; project pricing; rates on request pricing. Xenoss uses team extension and project pricing; 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: Azumo or Xenoss?
Azumo 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 Azumo and Xenoss?
Azumo's primary differentiator is: a nearshore team that also builds its own NLP products. Xenoss's primary differentiator is: Real-time, high-load data engineering from AdTech roots. They also differ in team size (100–500 (sources vary) vs 100–200), minimum engagement (Not published vs Not published), and primary industries served (Healthcare & life sciences, Media vs Media, Retail & e-commerce).
Verify all details directly with each company before making a decision.