ScienceSoft vs Revelo: full comparison for 2026
Quick verdict
ScienceSoft (4.0/5) edges ahead of Revelo (3.9/5) overall. ScienceSoft is the better choice for regulated industries hiring experienced data scientists. 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.
ScienceSoft vs Revelo: head-to-head summary
| Criterion | ScienceSoft | Revelo |
|---|---|---|
| Founded | 1989 | 2014 |
| HQ | McKinney, Texas, USA | Miami, Florida, USA |
| Team size | 750+ | 251–500 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Senior data scientists with a published hiring timeline | Payroll and compliance handled for LatAm hires |
| Pricing model | Time and materials; rates sent with CVs | Monthly per developer including payroll and compliance; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, R, Azure ML | Python, OpenAI, AWS |
| Industries served | Healthcare, Manufacturing, Fintech, Retail | SaaS, Fintech, E-commerce, AI labs |
ScienceSoft vs Revelo: overview
ScienceSoft
ScienceSoft dates its IT work to 1989 and is headquartered in McKinney, Texas, with representative offices in the UAE, Saudi Arabia, Europe and Mexico. Its staff-augmentation pool covers more than 750 professionals, including data scientists with 7–20 years of experience. The company says it sends CVs with rates within 24 hours, arranges interviews in two to four days and has people starting within one to two weeks (per company website; independently unverifiable).
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: ScienceSoft vs Revelo
| Capability | ScienceSoft | 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: ScienceSoft vs Revelo
| Framework / platform | ScienceSoft | 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 |
| Azure ML | ✓ | N/A |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: ScienceSoft vs Revelo
| Criterion | ScienceSoft | 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: ScienceSoft vs Revelo
| Dimension | ScienceSoft | Revelo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Manufacturing, Fintech | SaaS, Fintech, E-commerce |
| Best use cases | Adding a senior data scientist to a healthcare analytics team, Staffing a manufacturing predictive-maintenance project | 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 |
ScienceSoft vs Revelo: pros and cons
| ScienceSoft | |
|---|---|
| + | Rates arrive with the CVs, before any sales calls |
| + | Long history in healthcare and manufacturing IT |
| + | Experienced data scientists rather than junior ML hires |
| - | AI is one of many service lines |
| - | Smaller bench than the large nearshore firms |
| - | Headcount figures differ between the company's own pages |
| 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 ScienceSoft?
A typical fit: adding a senior data scientist to a healthcare analytics team.
Senior data scientists with a published hiring timeline. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Manufacturing, Fintech, Retail.
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: ScienceSoft 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 | ScienceSoft |
| Your budget is at the lower end | Compare: ScienceSoft (Not disclosed) vs Revelo (Not disclosed) |
| You need specialist depth in a specific vertical | ScienceSoft |
| 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: ScienceSoft vs Revelo
| Use case | ScienceSoft fit | Revelo fit | Winner |
|---|---|---|---|
| Adding a senior data scientist to a healthcare analytics team | Strong | Limited | ScienceSoft |
| Staffing a manufacturing predictive-maintenance project | Strong | Strong | Both equally |
| Hiring a full-time LatAm developer with payroll handled | Limited | Strong | Revelo |
| Staffing LLM post-training projects | Strong | Strong | Both equally |
Verdict: ScienceSoft vs Revelo
ScienceSoft (4.0/5) is the stronger overall choice for most AI Staffing projects. Senior data scientists with a published hiring timeline.
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
ScienceSoft vs Revelo FAQ
Is ScienceSoft better than Revelo?
ScienceSoft (4.0/5) scores higher overall, but "better" depends on your use case. ScienceSoft's strongest advantage: rates arrive with the CVs, before any sales calls. Revelo's strongest advantage: removes the legal and payroll work of hiring in Latin America.
How do ScienceSoft and Revelo differ in pricing?
ScienceSoft uses time and materials; rates sent with cvs 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: ScienceSoft 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 ScienceSoft and Revelo?
ScienceSoft's primary differentiator is: senior data scientists with a published hiring timeline. Revelo's primary differentiator is: payroll and compliance handled for LatAm hires. They also differ in team size (750+ vs 251–500), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthcare, Manufacturing vs SaaS, Fintech).
Verify all details directly with each agency before making a decision.