Azumo vs Xenoss: full comparison for 2026
Quick verdict
Azumo (4.5/5) edges ahead of Xenoss (4.3/5) overall. Azumo is the better choice for startups adding GenAI engineers on U.S. hours. 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.
Azumo vs Xenoss: head-to-head summary
| Criterion | Azumo | Xenoss |
|---|---|---|
| Founded | 2016 | 2013 |
| HQ | San Francisco, USA | New York, USA |
| Team size | 100–249 | 50–249 |
| Rating | 4.5 / 5 | 4.3 / 5 |
| Primary differentiator | Nearshore staffing with a hiring focus on GenAI and agent roles | Data engineers with ad-tech throughput experience |
| Pricing model | Monthly per engineer; dedicated team; project-based; rates on request | Time and materials; staff augmentation; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | OpenAI, LangChain, Hugging Face | Python, Apache Spark, Kafka |
| Industries served | SaaS, Fintech, Healthcare, Media | Ad tech, Media, Fintech, Retail and e-commerce |
Azumo vs Xenoss: overview
Azumo
Azumo was founded in San Francisco in 2016 by former investment banker Chike Agbai, whose first client was Twitter. Its engineers are based in more than 20 Latin American countries and work U.S. hours. The company sells three formats: staff augmentation alongside an existing team, dedicated teams, and project delivery, and its recent hiring is weighted toward generative-AI, agent and forward-deployed engineering roles. Headcount estimates range from about 80 to just over 100 depending on the source.
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: 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 | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | ✓ | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: Azumo vs Xenoss
| Criterion | Azumo | 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: Azumo vs Xenoss
| Dimension | Azumo | Xenoss |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | Ad tech, Media, Fintech |
| Best use cases | Adding an LLM engineer to ship a first GenAI feature, Hiring an agent developer to prototype internal automation | 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 |
Azumo vs Xenoss: pros and cons
| Azumo | |
|---|---|
| + | Hiring pattern shows real investment in LLM and agent engineering, beyond generic web developers |
| + | No long-term commitment required for augmentation seats |
| + | U.S. time zones and a U.S.-based management team |
| + | Small enough that founders and senior staff stay involved in client accounts |
| - | Headcount is modest, so very large teams may take longer to assemble |
| - | Public detail on how candidates are technically screened is thin |
| - | No published 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 Azumo?
A typical fit: adding an LLM engineer to ship a first GenAI feature.
Nearshore staffing with a hiring focus on GenAI and agent roles. Minimum engagement is not publicly disclosed. Works best with clients in SaaS, Fintech, Healthcare, Media.
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: Azumo 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 | Azumo |
| Your budget is at the lower end | Compare: Azumo (Not disclosed) vs Xenoss (Not disclosed) |
| You need specialist depth in a specific vertical | Azumo |
| 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: Azumo vs Xenoss
| Use case | Azumo fit | Xenoss fit | Winner |
|---|---|---|---|
| Adding an LLM engineer to ship a first GenAI feature | Strong | Strong | Both equally |
| Hiring an agent developer to prototype internal automation | Strong | Strong | Both equally |
| 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: Azumo vs Xenoss
Azumo (4.5/5) is the stronger overall choice for most AI Staffing projects. Nearshore staffing with a hiring focus on GenAI and agent roles.
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
Azumo vs Xenoss FAQ
Is Azumo better than Xenoss?
Azumo (4.5/5) scores higher overall, but "better" depends on your use case. Azumo's strongest advantage: hiring pattern shows real investment in LLM and agent engineering, beyond generic web developers. Xenoss's strongest advantage: strong on real-time data infrastructure that ML features depend on.
How do Azumo and Xenoss differ in pricing?
Azumo uses monthly per engineer; dedicated team; project-based; 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: 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 agency before shortlisting.
What are the main differences between Azumo and Xenoss?
Azumo's primary differentiator is: nearshore staffing with a hiring focus on GenAI and agent roles. Xenoss's primary differentiator is: data engineers with ad-tech throughput experience. They also differ in team size (100–249 vs 50–249), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (SaaS, Fintech vs Ad tech, Media).
Verify all details directly with each agency before making a decision.