Kanerika vs Revelo: full comparison for 2026
Quick verdict
Kanerika (3.9/5) edges ahead of Revelo (3.8/5) overall. Kanerika is the better choice for microsoft Fabric and Databricks shops needing AI-ready data. 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.
Kanerika vs Revelo: head-to-head summary
| Criterion | Kanerika | Revelo |
|---|---|---|
| Founded | 2015 | 2014 |
| HQ | Austin, Texas, USA | São Paulo, Brazil |
| Team size | 250–500 | 400,000+ developer network (per company) |
| Rating | 3.9 / 5 | 3.8 / 5 |
| Primary differentiator | Platform specialists for Fabric, Databricks and Snowflake | A very large Latin American pool with payroll and compliance included |
| Pricing model | Onshore, nearshore and offshore rates; time and materials; rates on request | Marketplace placement with monthly billing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Microsoft Fabric, Databricks, Snowflake | Python, OpenAI, Hugging Face |
| Industries served | Manufacturing, Financial services, Healthcare & life sciences, Logistics | Software & SaaS, AI research labs, Financial services |
Kanerika vs Revelo: overview
Kanerika
Kanerika was founded in 2015 and is based in Austin, Texas, with offices in India, Argentina and Singapore. Directories list 250 to 500 employees. Its staff augmentation service supplies AI engineers, data engineers and platform specialists for Microsoft Fabric, Databricks and Snowflake, either as single specialists or extended teams. The company's own blog ranks it first for data and AI staff augmentation, which should be read as self-promotion.
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: Kanerika vs Revelo
| Capability | Kanerika | 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: Kanerika vs Revelo
| Framework / platform | Kanerika | Revelo |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | ✓ |
| OpenAI | N/A | ✓ |
| AWS | N/A | ✓ |
| Azure | ✓ | N/A |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Kanerika vs Revelo
| Criterion | Kanerika | Revelo |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team | Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Kanerika vs Revelo
| Dimension | Kanerika | Revelo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Financial services, Healthcare & life sciences | Software & SaaS, AI research labs, Financial services |
| Best use cases | Adding a Fabric engineer before an analytics copilot rollout, Migrating data to Databricks for ML workloads | 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 |
Kanerika vs Revelo: pros and cons
| Kanerika | |
|---|---|
| + | Clear specialization in the data platforms most AI work depends on |
| + | Onshore, nearshore and offshore rate options |
| + | Can supply one specialist or a full team |
| - | Few independent client reviews |
| - | Its self-published rankings should not be treated as evidence |
| - | Less depth in model research than AI-only firms |
| 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 Kanerika?
A typical fit: adding a Fabric engineer before an analytics copilot rollout.
Platform specialists for Fabric, Databricks and Snowflake. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Financial services, Healthcare & life sciences, Logistics.
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: Kanerika vs Revelo
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Kanerika |
| You want the supplier to own delivery as well as staffing | Neither offers managed delivery; you will lead the work |
| 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: Kanerika (Not published) vs Revelo (Not published) |
| You need overlap with U.S. working hours | Revelo |
| You need specialist depth in a specific vertical | Kanerika |
Use case fit: Kanerika vs Revelo
| Use case | Kanerika fit | Revelo fit | Winner |
|---|---|---|---|
| Adding a Fabric engineer before an analytics copilot rollout | Strong | Strong | Both equally |
| Migrating data to Databricks for ML workloads | Strong | Limited | Kanerika |
| 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: Kanerika vs Revelo
Kanerika (3.9/5) is the stronger overall choice for most AI Staff Augmentation projects. Platform specialists for Fabric, Databricks and Snowflake.
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
Kanerika vs Revelo FAQ
Is Kanerika better than Revelo?
Kanerika (3.9/5) scores higher overall, but "better" depends on your use case. Kanerika's strongest advantage: clear specialization in the data platforms most AI work depends on. Revelo's strongest advantage: very large pool across Latin America.
How do Kanerika and Revelo differ in pricing?
Kanerika uses onshore, nearshore and offshore rates; time and materials; 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: Kanerika 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 Kanerika and Revelo?
Kanerika's primary differentiator is: platform specialists for Fabric, Databricks and Snowflake. Revelo's primary differentiator is: a very large Latin American pool with payroll and compliance included. They also differ in team size (250–500 vs 400,000+ developer network (per company)), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Financial services vs Software & SaaS, AI research labs).
Verify all details directly with each company before making a decision.