Quantiphi vs Revelo: full comparison for 2026
Quick verdict
Quantiphi (4.2/5) edges ahead of Revelo (4.0/5) overall. Quantiphi is the better choice for procurement teams that want a defined staffing product from a large AI-only firm. Revelo is the stronger option for U.S. companies that want a Latin American AI engineer without upfront fees or a long contract. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs Revelo: head-to-head summary
| Criterion | Quantiphi | Revelo |
|---|---|---|
| Founded | 2013 | 2014 |
| HQ | Marlborough, Massachusetts, USA | Miami, Florida, USA |
| Team size | 3,000–4,000+ | 400,000+ network (company figure) |
| Rating | 4.2 / 5 | 4.0 / 5 |
| Primary differentiator | Elastic Staffing, a packaged staffing program built with AWS | No upfront fees, no long-term contract and published salary benchmarks |
| Pricing model | Elastic Staffing billed per specialist; consulting quoted separately; rates on request | Monthly per engineer; no upfront fees or long-term contracts (per company); senior AI/ML all-in cost about $103,000/yr (company benchmark) |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, PyTorch, TensorFlow |
| Industries served | Healthcare, Financial services, Energy, Retail, Media | Technology, Fintech, SaaS, Healthcare, AI labs |
Quantiphi vs Revelo: overview
Quantiphi
Quantiphi, founded in 2013 in Marlborough, Massachusetts, employs between 3,000 and 4,000+ people on AI and data work alone. For buyers, its most useful feature is that staffing comes as a named product. Elastic Staffing, built with AWS, places generative AI and ML specialists into client teams, which gives procurement something defined to sign. It is the right call when you need many roles at once. Smaller requests compete with large consulting programs, and rates appear only after scoping.
Revelo
Revelo started in 2014 (one listing says 2015) and has a U.S. base in Miami and operations in São Paulo. It places engineers from a Latin American network it puts at over 400,000, and its platform pages promise no upfront fees and no long-term contracts. Revelo also publishes salary benchmarks. Its figure for a senior AI or ML engineer is about $103,000 a year all-in, roughly 55% below a comparable U.S. hire. LLM training work made up 22% of its 2024 revenue, according to TechCrunch.
Services and capabilities: Quantiphi vs Revelo
| Capability | Quantiphi | Revelo |
|---|---|---|
| Full-time dedicated engineers | ✓ | ✓ |
| Part-time / fractional experts | ✗ | ✗ |
| Dedicated team | ✓ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Published rates | ✗ | ✗ |
| Direct hire option | ✗ | ✗ |
| Subscription or output-based pricing | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✓ |
| LLM / GenAI engineers | ✓ | ✓ |
| MLOps | ✓ | ✗ |
| Computer vision | ✗ | ✗ |
| Data engineering | ✓ | ✗ |
Tech stack comparison: Quantiphi vs Revelo
| Framework / platform | Quantiphi | Revelo |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Google Cloud | ✓ | ✓ |
| Databricks | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Quantiphi vs Revelo
| Criterion | Quantiphi | Revelo |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated, Dedicated team, Project delivery | Full-time dedicated, Freelance contract |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Quantiphi vs Revelo
| Dimension | Quantiphi | Revelo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Financial services, Energy | Technology, Fintech, SaaS |
| Best use cases | Buying ten GenAI specialists under one contract, Staffing a SageMaker migration | Hiring a Brazilian ML engineer without a long contract, Budgeting a nearshore AI team from published salary data |
| Typical project type | Full-time dedicated | Full-time dedicated |
Quantiphi vs Revelo: pros and cons
| Quantiphi | |
|---|---|
| + | A named staffing product simplifies procurement |
| + | Can fill many AI roles at once |
| + | Senior partner status with Google Cloud and AWS |
| - | Small requests get less attention |
| - | No public rates or trial |
| - | Headcount estimates vary |
| Revelo | |
|---|---|
| + | No upfront fee or long-term contract |
| + | Published salary benchmarks help with budgeting |
| + | Large Latin American network on U.S. hours |
| - | Part of revenue comes from AI-lab training work |
| - | Salary figures come from Revelo itself |
| - | Founding year and headquarters differ by source |
Who should choose Quantiphi?
A typical fit: buying ten GenAI specialists under one contract.
Elastic Staffing, a packaged staffing program built with AWS. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Energy, Retail, Media.
Who should choose Revelo?
A typical fit: hiring a Brazilian ML engineer without a long contract.
No upfront fees, no long-term contract and published salary benchmarks. Minimum engagement is not publicly disclosed. Works best with clients in Technology, Fintech, SaaS, Healthcare, AI labs.
Decision matrix: Quantiphi vs Revelo
| Your situation | Recommended choice |
|---|---|
| You want one engineer full-time on a monthly contract | Both; Quantiphi rates higher overall |
| You only need a specialist a few days a week | Neither advertises part-time experts; ask about reduced hours |
| You want to test an engineer before committing | Neither publishes a trial; negotiate a short first term |
| You need a rate before the first call | Neither publishes rates; ask both for a written rate card |
| Your budget is at the lower end | Compare: Quantiphi (Not published) vs Revelo (Not published) |
| You may want to hire the engineer permanently later | Neither lists direct hire; agree conversion terms up front |
| You want several engineers working as one team | Quantiphi |
Use case fit: Quantiphi vs Revelo
| Use case | Quantiphi fit | Revelo fit | Winner |
|---|---|---|---|
| Buying ten GenAI specialists under one contract | Strong | Limited | Quantiphi |
| Staffing a SageMaker migration | Strong | Limited | Quantiphi |
| Hiring a Brazilian ML engineer without a long contract | Limited | Strong | Revelo |
| Budgeting a nearshore AI team from published salary data | Limited | Strong | Revelo |
Verdict: Quantiphi vs Revelo
Quantiphi (4.2/5) is the stronger overall choice for most AI Staff Augmentation projects. Elastic Staffing, a packaged staffing program built with AWS.
Revelo (4.0/5) is worth a look if you need budgeting a nearshore AI team from published salary data. If your situation matches that, Revelo is a competitive option.
Related comparisons
Quantiphi vs Revelo FAQ
Is Quantiphi better than Revelo?
Quantiphi (4.2/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: a named staffing product simplifies procurement. Revelo's strongest advantage: no upfront fee or long-term contract.
How do Quantiphi and Revelo differ in pricing?
Quantiphi uses elastic staffing billed per specialist; consulting quoted separately; rates on request pricing. Revelo uses monthly per engineer; no upfront fees or long-term contracts (per company); senior ai/ml all-in cost about $103,000/yr (company benchmark) pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Quantiphi or Revelo?
Quantiphi is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each provider before shortlisting.
What are the main differences between Quantiphi and Revelo?
Quantiphi's primary differentiator is: elastic Staffing, a packaged staffing program built with AWS. Revelo's primary differentiator is: no upfront fees, no long-term contract and published salary benchmarks. They also differ in team size (3,000–4,000+ vs 400,000+ network (company figure)), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Financial services vs Technology, Fintech).
Verify all details directly with each provider before making a decision.