InData Labs vs Revelo: full comparison for 2026
Quick verdict
InData Labs (4.1/5) edges ahead of Revelo (3.8/5) overall. InData Labs is the better choice for mid-sized companies adding data scientists to product teams. Revelo is the stronger option for hiring Latin American developers through a marketplace. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Revelo: head-to-head summary
| Criterion | InData Labs | Revelo |
|---|---|---|
| Founded | 2014 | 2014 |
| HQ | Nicosia, Cyprus | São Paulo, Brazil |
| Team size | 50–249 | 400,000+ developer network (per company) |
| Rating | 4.1 / 5 | 3.8 / 5 |
| Primary differentiator | A data-science-only firm small enough that senior staff stay involved | A very large Latin American pool with payroll and compliance included |
| Pricing model | Time and materials; dedicated engineers; rates on request | Marketplace placement with monthly billing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, OpenAI, Hugging Face |
| Industries served | Retail & e-commerce, Healthcare & life sciences, Financial services, Media | Software & SaaS, AI research labs, Financial services |
InData Labs vs Revelo: overview
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with additional locations including Vilnius and Miami. Most directories put its headcount below 250 people. The company works only on data science and AI, covering predictive analytics, NLP, computer vision and generative AI, and it supplies engineers to client teams as well as delivering projects.
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.
Services and capabilities: InData Labs vs Revelo
| Capability | InData Labs | 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: InData Labs vs Revelo
| Framework / platform | InData Labs | Revelo |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | 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: InData Labs vs Revelo
| Criterion | InData Labs | Revelo |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Managed delivery | Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs Revelo
| Dimension | InData Labs | Revelo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail & e-commerce, Healthcare & life sciences, Financial services | Software & SaaS, AI research labs, Financial services |
| Best use cases | Adding a computer-vision engineer to a retail analytics team, Building churn and demand models with in-house analysts | Hiring LLM data specialists for a model-training effort, Adding a Brazilian developer to a U.S. SaaS team |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
InData Labs vs Revelo: pros and cons
| InData Labs | |
|---|---|
| + | Data science and AI are its only line of work |
| + | Experience across vision, language and predictive models |
| + | Clients deal with a small firm where senior staff stay close to the work |
| - | Headcount estimates vary widely, so confirm bench depth for your role |
| - | Limited capacity for large multi-team programs |
| - | Less visible LLM-agent work than some newer specialists |
| 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 |
Who should choose InData Labs?
A typical fit: adding a computer-vision engineer to a retail analytics team.
A data-science-only firm small enough that senior staff stay involved. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare & life sciences, Financial services, Media.
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.
Decision matrix: InData Labs vs Revelo
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Confirm how many engineers each can staff at once |
| You want the supplier to own delivery as well as staffing | InData Labs |
| 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: InData Labs (Not published) vs Revelo (Not published) |
| You need overlap with U.S. working hours | Revelo |
| You need specialist depth in a specific vertical | InData Labs |
Use case fit: InData Labs vs Revelo
| Use case | InData Labs fit | Revelo fit | Winner |
|---|---|---|---|
| Adding a computer-vision engineer to a retail analytics team | Strong | Strong | Both equally |
| Building churn and demand models with in-house analysts | Strong | Limited | InData Labs |
| Hiring LLM data specialists for a model-training effort | Limited | Strong | Revelo |
| Adding a Brazilian developer to a U.S. SaaS team | Strong | Strong | Both equally |
Verdict: InData Labs vs Revelo
InData Labs (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. A data-science-only firm small enough that senior staff stay involved.
Revelo (3.8/5) is worth a look if you need adding a Brazilian developer to a U.S. SaaS team. If your situation matches that, Revelo is a competitive option.
Related comparisons
InData Labs vs Revelo FAQ
Is InData Labs better than Revelo?
InData Labs (4.1/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: data science and AI are its only line of work. Revelo's strongest advantage: very large pool across Latin America.
How do InData Labs and Revelo differ in pricing?
InData Labs uses time and materials; dedicated engineers; rates on request pricing. Revelo uses marketplace placement with monthly billing; 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: InData Labs 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 company before shortlisting.
What are the main differences between InData Labs and Revelo?
InData Labs's primary differentiator is: a data-science-only firm small enough that senior staff stay involved. Revelo's primary differentiator is: a very large Latin American pool with payroll and compliance included. They also differ in team size (50–249 vs 400,000+ developer network (per company)), minimum engagement (Not published vs Not published), and primary industries served (Retail & e-commerce, Healthcare & life sciences vs Software & SaaS, AI research labs).
Verify all details directly with each company before making a decision.