Xenoss vs Howdy.com: full comparison for 2026
Quick verdict
Xenoss (4.3/5) edges ahead of Howdy.com (4.1/5) overall. Xenoss is the better choice for ad-tech and high-volume data teams. Howdy.com is the stronger option for teams that want transparent nearshore pricing. The right choice depends on your project size, budget, and required tech stack.
Xenoss vs Howdy.com: head-to-head summary
| Criterion | Xenoss | Howdy.com |
|---|---|---|
| Founded | 2013 | 2018 |
| HQ | New York, USA | Austin, Texas, USA |
| Team size | 50–249 | 100–249 |
| Rating | 4.3 / 5 | 4.1 / 5 |
| Primary differentiator | Data engineers with ad-tech throughput experience | Published 15% fee on top of the engineer's pay |
| Pricing model | Time and materials; staff augmentation; rates on request | Engineer salary plus a flat 15% service fee (published) |
| Min. engagement | Not disclosed | 1 engineer |
| Primary tech stack | Python, Apache Spark, Kafka | Python, AWS, Databricks |
| Industries served | Ad tech, Media, Fintech, Retail and e-commerce | SaaS, Fintech, Healthcare, E-commerce |
Xenoss vs Howdy.com: 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.
Howdy.com
Howdy.com was founded in Austin, Texas, in 2018 by Jacqueline Samira, with Frank Licea later joining as co-founder and CTO. Unlike a pure marketplace, it recruits, employs and supports its engineers, and it runs in-country hubs it calls Howdy Houses in Latin American cities. In August 2023 it acquired the Brazilian talent marketplace GeekHunter, citing rising demand for AI and ML skills. A 2026 industry ranking lists its fee as a flat 15% of the billed rate.
Services and capabilities: Xenoss vs Howdy.com
| Capability | Xenoss | Howdy.com |
|---|---|---|
| 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 Howdy.com
| Framework / platform | Xenoss | Howdy.com |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Xenoss vs Howdy.com
| Criterion | Xenoss | Howdy.com |
|---|---|---|
| Minimum engagement | Not disclosed | 1 engineer |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: Xenoss vs Howdy.com
| Dimension | Xenoss | Howdy.com |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Ad tech, Media, Fintech | SaaS, Fintech, Healthcare |
| Best use cases | Adding streaming-data engineers ahead of an ML launch, Placing ML engineers in a bidding or attribution product | Hiring a full-time LatAm data engineer with clear cost visibility, Building a two-to-four person nearshore team for a U.S. startup |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Xenoss vs Howdy.com: 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 |
| Howdy.com | |
|---|---|
| + | Fee structure is published, so you can see what the engineer actually earns |
| + | Engineers are employed and supported locally, which helps retention |
| + | GeekHunter acquisition widened its Brazilian candidate pool |
| + | U.S. time zones |
| - | AI depth depends on who is available; it is a general engineering staffing company |
| - | Disclosure: acquired GeekHunter in 2023, so part of its candidate supply comes through a marketplace subsidiary |
| - | Technical screening is lighter than at AI-only companies |
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 Howdy.com?
A typical fit: hiring a full-time LatAm data engineer with clear cost visibility.
Published 15% fee on top of the engineer's pay. Minimum engagement starts at 1 engineer. Works best with clients in SaaS, Fintech, Healthcare, E-commerce.
Decision matrix: Xenoss vs Howdy.com
| 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 Howdy.com (1 engineer) |
| 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 Howdy.com
| Use case | Xenoss fit | Howdy.com 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 |
| Hiring a full-time LatAm data engineer with clear cost visibility | Strong | Strong | Both equally |
| Building a two-to-four person nearshore team for a U.S. startup | Limited | Strong | Howdy.com |
Verdict: Xenoss vs Howdy.com
Xenoss (4.3/5) is the stronger overall choice for most AI Staffing projects. Data engineers with ad-tech throughput experience.
Howdy.com (4.1/5) is worth a look if you need building a two-to-four person nearshore team for a U.S. startup. If your situation matches that, Howdy.com is a competitive option.
Related comparisons
Xenoss vs Howdy.com FAQ
Is Xenoss better than Howdy.com?
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. Howdy.com's strongest advantage: fee structure is published, so you can see what the engineer actually earns.
How do Xenoss and Howdy.com differ in pricing?
Xenoss uses time and materials; staff augmentation; rates on request pricing. Howdy.com uses engineer salary plus a flat 15% service fee (published) pricing with a minimum engagement of 1 engineer. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Xenoss or Howdy.com?
Howdy.com 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 Howdy.com?
Xenoss's primary differentiator is: data engineers with ad-tech throughput experience. Howdy.com's primary differentiator is: published 15% fee on top of the engineer's pay. They also differ in team size (50–249 vs 100–249), minimum engagement (Not disclosed vs 1 engineer), and primary industries served (Ad tech, Media vs SaaS, Fintech).
Verify all details directly with each agency before making a decision.