Quantiphi vs Data Science UA: full comparison for 2026
Quick verdict
Quantiphi (4.2/5) edges ahead of Data Science UA (3.8/5) overall. Quantiphi is the better choice for procurement teams that want a defined staffing product from a large AI-only firm. Data Science UA is the stronger option for companies that want to choose between a recruiting fee and monthly outstaffing for Ukrainian AI talent. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs Data Science UA: head-to-head summary
| Criterion | Quantiphi | Data Science UA |
|---|---|---|
| Founded | 2013 | 2016 |
| HQ | Marlborough, Massachusetts, USA | Kyiv, Ukraine (legal HQ London) |
| Team size | 3,000–4,000+ | 50–200 |
| Rating | 4.2 / 5 | 3.8 / 5 |
| Primary differentiator | Elastic Staffing, a packaged staffing program built with AWS | Recruiting fee or monthly outstaffing from an AI-only recruiter |
| Pricing model | Elastic Staffing billed per specialist; consulting quoted separately; rates on request | Recruiting fee per hire; outstaffing billed monthly; rates on request |
| 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, Healthcare, Retail, Gaming |
Quantiphi vs Data Science UA: 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.
Data Science UA
Data Science UA grew out of a 2016 data science conference in Kyiv and now runs recruiting, outstaffing and AI consulting, with a legal base in London. It offers two ways to buy. You can pay a recruiting fee for a permanent hire, which it says takes two to four weeks on average, or take the engineer on monthly outstaffing terms first. Its AI community, quoted at 10,000 to 30,000 people, gives it reach. Screening is done by recruiters, so plan your own technical interview.
Services and capabilities: Quantiphi vs Data Science UA
| Capability | Quantiphi | Data Science UA |
|---|---|---|
| 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 Data Science UA
| Framework / platform | Quantiphi | Data Science UA |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Google Cloud | ✓ | N/A |
| Databricks | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Quantiphi vs Data Science UA
| Criterion | Quantiphi | Data Science UA |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated, Dedicated team, Project delivery | Direct hire, Full-time dedicated, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Quantiphi vs Data Science UA
| Dimension | Quantiphi | Data Science UA |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Financial services, Energy | Technology, Fintech, Healthcare |
| Best use cases | Buying ten GenAI specialists under one contract, Staffing a SageMaker migration | Hiring a permanent ML engineer in Ukraine, Outstaffing a computer vision engineer before a permanent offer |
| Typical project type | Full-time dedicated | Direct hire |
Quantiphi vs Data Science UA: 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 |
| Data Science UA | |
|---|---|
| + | Both recruiting and outstaffing |
| + | Recruiters focused on AI roles |
| + | Large Ukrainian AI community |
| - | Recruiter-led screening |
| - | Size and headquarters vary by source |
| - | Wartime continuity risk |
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 Data Science UA?
A typical fit: hiring a permanent ML engineer in Ukraine.
Recruiting fee or monthly outstaffing from an AI-only recruiter. Minimum engagement is not publicly disclosed. Works best with clients in Technology, Fintech, Healthcare, Retail, Gaming.
Decision matrix: Quantiphi vs Data Science UA
| 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 Data Science UA (Not published) |
| You may want to hire the engineer permanently later | Data Science UA |
| You want several engineers working as one team | Both; Quantiphi rates higher overall |
Use case fit: Quantiphi vs Data Science UA
| Use case | Quantiphi fit | Data Science UA fit | Winner |
|---|---|---|---|
| Buying ten GenAI specialists under one contract | Strong | Limited | Quantiphi |
| Staffing a SageMaker migration | Strong | Strong | Both equally |
| Hiring a permanent ML engineer in Ukraine | Limited | Strong | Data Science UA |
| Outstaffing a computer vision engineer before a permanent offer | Limited | Strong | Data Science UA |
Verdict: Quantiphi vs Data Science UA
Quantiphi (4.2/5) is the stronger overall choice for most AI Staff Augmentation projects. Elastic Staffing, a packaged staffing program built with AWS.
Data Science UA (3.8/5) is worth a look if you need outstaffing a computer vision engineer before a permanent offer. If your situation matches that, Data Science UA is a competitive option.
Related comparisons
Quantiphi vs Data Science UA FAQ
Is Quantiphi better than Data Science UA?
Quantiphi (4.2/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: a named staffing product simplifies procurement. Data Science UA's strongest advantage: both recruiting and outstaffing.
How do Quantiphi and Data Science UA differ in pricing?
Quantiphi uses elastic staffing billed per specialist; consulting quoted separately; rates on request pricing. Data Science UA uses recruiting fee per hire; outstaffing billed monthly; 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: Quantiphi or Data Science UA?
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 Data Science UA?
Quantiphi's primary differentiator is: elastic Staffing, a packaged staffing program built with AWS. Data Science UA's primary differentiator is: recruiting fee or monthly outstaffing from an AI-only recruiter. They also differ in team size (3,000–4,000+ vs 50–200), 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.