Revelo vs Xenoss: full comparison for 2026
Quick verdict
Revelo (3.8/5) edges ahead of Xenoss (3.8/5) overall. Revelo is the better choice for hiring Latin American developers through a marketplace. 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.
Revelo vs Xenoss: head-to-head summary
| Criterion | Revelo | Xenoss |
|---|---|---|
| Founded | 2014 | 2013 |
| HQ | São Paulo, Brazil | New York, New York, USA |
| Team size | 400,000+ developer network (per company) | 100–200 |
| Rating | 3.8 / 5 | 3.8 / 5 |
| Primary differentiator | A very large Latin American pool with payroll and compliance included | Real-time, high-load data engineering from AdTech roots |
| Pricing model | Marketplace placement with monthly billing; rates on request | Team extension and project pricing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, OpenAI, Hugging Face | Python, Kafka, Spark |
| Industries served | Software & SaaS, AI research labs, Financial services | Media, Retail & e-commerce, Software & SaaS |
Revelo vs Xenoss: overview
Revelo
Revelo was founded in late 2014 in Brazil (some sources say 2015) and began as a domestic hiring platform called Contratado. It now runs a network of more than 400,000 Latin American developers and handles hiring and payment for U.S. customers. TechCrunch reported that work on foundation models made up 22% of Revelo's revenue in 2024. Revelo is a marketplace, so engineers are matched through its platform rather than employed in a delivery center.
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: Revelo vs Xenoss
| Capability | Revelo | 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: Revelo vs Xenoss
| Framework / platform | Revelo | Xenoss |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Revelo vs Xenoss
| Criterion | Revelo | Xenoss |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Revelo vs Xenoss
| Dimension | Revelo | Xenoss |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Software & SaaS, AI research labs, Financial services | Media, Retail & e-commerce, Software & SaaS |
| Best use cases | Hiring LLM data specialists for a model-training effort, Adding a Brazilian developer to a U.S. SaaS team | 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 |
Revelo vs Xenoss: pros and cons
| Revelo | |
|---|---|
| + | Very large pool across Latin America |
| + | Handles hiring, payroll and compliance |
| + | Foundation-model work gives some engineers LLM training experience |
| - | Marketplace matching means quality varies by candidate |
| - | Founding year is reported as both 2014 and 2015 |
| - | Less hands-on management than employer-based firms |
| 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 Revelo?
A typical fit: hiring LLM data specialists for a model-training effort.
A very large Latin American pool with payroll and compliance included. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, AI research labs, 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: Revelo vs Xenoss
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Xenoss |
| You want the supplier to own delivery as well as staffing | Xenoss |
| 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: Revelo (Not published) vs Xenoss (Not published) |
| You need overlap with U.S. working hours | Revelo |
| You need specialist depth in a specific vertical | Revelo |
Use case fit: Revelo vs Xenoss
| Use case | Revelo fit | Xenoss fit | Winner |
|---|---|---|---|
| Hiring LLM data specialists for a model-training effort | Strong | Limited | Revelo |
| Adding a Brazilian developer to a U.S. SaaS team | 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 | Limited | Strong | Xenoss |
Verdict: Revelo vs Xenoss
Revelo (3.8/5) is the stronger overall choice for most AI Staff Augmentation projects. A very large Latin American pool with payroll and compliance included.
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
Revelo vs Xenoss FAQ
Is Revelo better than Xenoss?
Revelo (3.8/5) scores higher overall, but "better" depends on your use case. Revelo's strongest advantage: very large pool across Latin America. Xenoss's strongest advantage: High-load, real-time data experience.
How do Revelo and Xenoss differ in pricing?
Revelo uses marketplace placement with monthly billing; 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: Revelo or Xenoss?
Revelo 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 Revelo and Xenoss?
Revelo's primary differentiator is: a very large Latin American pool with payroll and compliance included. Xenoss's primary differentiator is: Real-time, high-load data engineering from AdTech roots. They also differ in team size (400,000+ developer network (per company) vs 100–200), minimum engagement (Not published vs Not published), and primary industries served (Software & SaaS, AI research labs vs Media, Retail & e-commerce).
Verify all details directly with each company before making a decision.