Xenoss vs Revelo: full comparison for 2026
Quick verdict
Xenoss (4.3/5) edges ahead of Revelo (3.9/5) overall. Xenoss is the better choice for ad-tech and high-volume data teams. Revelo is the stronger option for companies hiring LatAm developers without a local entity. The right choice depends on your project size, budget, and required tech stack.
Xenoss vs Revelo: head-to-head summary
| Criterion | Xenoss | Revelo |
|---|---|---|
| Founded | 2013 | 2014 |
| HQ | New York, USA | Miami, Florida, USA |
| Team size | 50–249 | 251–500 |
| Rating | 4.3 / 5 | 3.9 / 5 |
| Primary differentiator | Data engineers with ad-tech throughput experience | Payroll and compliance handled for LatAm hires |
| Pricing model | Time and materials; staff augmentation; rates on request | Monthly per developer including payroll and compliance; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Apache Spark, Kafka | Python, OpenAI, AWS |
| Industries served | Ad tech, Media, Fintech, Retail and e-commerce | SaaS, Fintech, E-commerce, AI labs |
Xenoss vs Revelo: 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.
Revelo
Revelo traces its start to late 2014 and is headquartered in Miami, with 251–500 employees according to one directory. It runs a platform of more than 400,000 Latin American developers and handles sourcing, compliance, local payroll and benefits, so clients can hire individuals or whole teams without opening a local entity. It has also moved into LLM post-training work, supplying developers for supervised fine-tuning and RLHF projects. It has raised more than $48 million from investors including Social Capital and Valor Capital Group.
Services and capabilities: Xenoss vs Revelo
| Capability | Xenoss | Revelo |
|---|---|---|
| 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 Revelo
| Framework / platform | Xenoss | Revelo |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | 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 Revelo
| Criterion | Xenoss | Revelo |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Xenoss vs Revelo
| Dimension | Xenoss | Revelo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Ad tech, Media, Fintech | SaaS, Fintech, E-commerce |
| 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 developer with payroll handled, Staffing LLM post-training projects |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Xenoss vs Revelo: 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 |
| Revelo | |
|---|---|
| + | Removes the legal and payroll work of hiring in Latin America |
| + | Large candidate pool |
| + | Experience supplying engineers for LLM training work |
| - | Platform model means vetting is lighter than at engineering agencies |
| - | AI focus leans toward LLM training data over product engineering |
| - | Pricing requires a call |
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 Revelo?
A typical fit: hiring a full-time LatAm developer with payroll handled.
Payroll and compliance handled for LatAm hires. Minimum engagement is not publicly disclosed. Works best with clients in SaaS, Fintech, E-commerce, AI labs.
Decision matrix: Xenoss vs Revelo
| 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 Revelo (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 Revelo
| Use case | Xenoss fit | Revelo fit | Winner |
|---|---|---|---|
| Adding streaming-data engineers ahead of an ML launch | Strong | Limited | Xenoss |
| Placing ML engineers in a bidding or attribution product | Strong | Limited | Xenoss |
| Hiring a full-time LatAm developer with payroll handled | Strong | Strong | Both equally |
| Staffing LLM post-training projects | Limited | Strong | Revelo |
Verdict: Xenoss vs Revelo
Xenoss (4.3/5) is the stronger overall choice for most AI Staffing projects. Data engineers with ad-tech throughput experience.
Revelo (3.9/5) is worth a look if you need staffing LLM post-training projects. If your situation matches that, Revelo is a competitive option.
Related comparisons
Xenoss vs Revelo FAQ
Is Xenoss better than Revelo?
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. Revelo's strongest advantage: removes the legal and payroll work of hiring in Latin America.
How do Xenoss and Revelo differ in pricing?
Xenoss uses time and materials; staff augmentation; rates on request pricing. Revelo uses monthly per developer including payroll and compliance; 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 Revelo?
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 agency before shortlisting.
What are the main differences between Xenoss and Revelo?
Xenoss's primary differentiator is: data engineers with ad-tech throughput experience. Revelo's primary differentiator is: payroll and compliance handled for LatAm hires. They also differ in team size (50–249 vs 251–500), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Ad tech, Media vs SaaS, Fintech).
Verify all details directly with each agency before making a decision.