InData Labs vs Quantiphi: full comparison for 2026
Quick verdict
InData Labs (4.5/5) edges ahead of Quantiphi (4.4/5) overall. InData Labs is the better choice for Fintech, healthcare, SaaS — specialist data-science boutique. Quantiphi is the stronger option for financial-services enterprises, cloud-native AI at scale. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Quantiphi: head-to-head summary
| Criterion | InData Labs | Quantiphi |
|---|---|---|
| Founded | 2014 | 2013 |
| HQ | Nicosia, Cyprus | Marlborough, Massachusetts, USA |
| Team size | 51–200 | 1,001–5,000 |
| Rating | 4.5 / 5 | 4.4 / 5 |
| Primary differentiator | Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing | AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing |
| Pricing model | Fixed project and Time & Material | Fixed project and managed AI services |
| Min. engagement | $20K | Not published |
| Primary tech stack | Python, Scikit-learn, TensorFlow | Python, TensorFlow, Google Cloud Vertex AI |
| Industries served | FinTech, Healthcare, Technology/SaaS, Retail, Logistics | Financial Services, Healthcare, Media, Technology/SaaS |
InData Labs vs Quantiphi: overview
InData Labs
InData Labs is a data science and AI consultancy founded in 2014 by Marat Karpeko, headquartered in Nicosia, Cyprus, with additional offices in Lithuania and the US. The 80+ person firm (per company website) runs its own R&D center and focuses on production AI systems for fintech, healthcare, SaaS, retail, and logistics clients.
Quantiphi
Quantiphi is an AI-first digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, headquartered in Marlborough, Massachusetts. Reported headcount is roughly 2,670–3,927 employees depending on source, making it one of the larger, more established AI-native firms on this list, with strong focus on financial services and cloud-native ML platform engineering.
Services and capabilities: InData Labs vs Quantiphi
| Capability | InData Labs | Quantiphi |
|---|---|---|
| Custom ML model development | ✓ | ✓ |
| Deep learning & computer vision | ✗ | ✗ |
| NLP & LLM / Generative AI | ✗ | ✗ |
| MLOps & production deployment | ✗ | ✓ |
| Data engineering | ✓ | ✓ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: InData Labs vs Quantiphi
| Framework / platform | InData Labs | Quantiphi |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | N/A | ✓ |
| Kubernetes | N/A | ✓ |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: InData Labs vs Quantiphi
| Criterion | InData Labs | Quantiphi |
|---|---|---|
| Minimum engagement | $20K | Not published |
| Engagement models | Fixed project, Time & Material | Fixed project, Managed services |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Enterprise / not published |
Target audience comparison: InData Labs vs Quantiphi
| Dimension | InData Labs | Quantiphi |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Healthcare, Technology/SaaS | Financial Services, Healthcare, Media |
| Best use cases | Building a fintech risk-scoring or fraud model with a specialist data-science team, Standing up a healthcare predictive-analytics pilot with a boutique partner | Enterprise financial-services AI programs requiring both scale and deep ML expertise, Cloud-native ML platform builds on GCP, AWS, or Azure at production scale |
| Typical project type | Fixed project | Fixed project |
InData Labs vs Quantiphi: pros and cons
| InData Labs | |
|---|---|
| + | Founder brought data-analytics experience from the gaming industry, an unusually data-intensive prior domain |
| + | Multi-country footprint (Cyprus, Lithuania, US) without the very large headcount of enterprise IT firms |
| + | 10+ years of focused data science practice rather than a recent AI pivot from generalist dev work |
| + | Named vertical focus (FinTech, Healthcare, Logistics) supports domain-specific model design |
| - | 80-person team limits capacity for very large multi-year enterprise programs |
| - | Less brand recognition in North America than US-headquartered competitors |
| - | Public case studies rarely disclose named enterprise clients |
| Quantiphi | |
|---|---|
| + | Founded as an AI-first company rather than a generalist IT firm that later added an AI practice |
| + | Enterprise-scale headcount (2,600+) supports large, multi-region programs |
| + | Strong cloud-native ML platform engineering, reducing gaps between model development and production deployment |
| + | 13 years of continuous focus on applied AI and analytics |
| - | Scale and enterprise sales process may be slower and less accessible for small pilot projects than boutique competitors |
| - | Recent employee counts show a reported year-over-year headcount decline (~4% per one source), worth asking about directly |
| - | Minimum engagement size and standard pricing are not publicly disclosed |
Who should choose InData Labs?
A typical fit: building a fintech risk-scoring or fraud model with a specialist data-science team.
Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing. Minimum engagement starts at $20K. Works best with clients in FinTech, Healthcare, Technology/SaaS, Retail, Logistics.
Who should choose Quantiphi?
A typical fit: enterprise financial-services AI programs requiring both scale and deep ML expertise.
AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing. Minimum engagement starts at Not published. Works best with clients in Financial Services, Healthcare, Media, Technology/SaaS.
Decision matrix: InData Labs vs Quantiphi
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Compare: InData Labs ($20K) vs Quantiphi (Not published) |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs Quantiphi
| Use case | InData Labs fit | Quantiphi fit | Winner |
|---|---|---|---|
| Building a fintech risk-scoring or fraud model with a specialist data-science team | Strong | Limited | InData Labs |
| Standing up a healthcare predictive-analytics pilot with a boutique partner | Strong | Limited | InData Labs |
| Enterprise financial-services AI programs requiring both scale and deep ML expertise | Limited | Strong | Quantiphi |
| Cloud-native ML platform builds on GCP, AWS, or Azure at production scale | Limited | Strong | Quantiphi |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: InData Labs vs Quantiphi
InData Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing.
Quantiphi (4.4/5) is worth a look if you need cloud-native ML platform builds on GCP, AWS, or Azure at production scale. If your situation matches that, Quantiphi is a competitive option.
Related comparisons
InData Labs vs Quantiphi FAQ
Is InData Labs better than Quantiphi?
InData Labs (4.5/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: founder brought data-analytics experience from the gaming industry, an unusually data-intensive prior domain. Quantiphi's strongest advantage: founded as an AI-first company rather than a generalist IT firm that later added an AI practice.
How do InData Labs and Quantiphi differ in pricing?
InData Labs uses fixed project and time & material pricing with a minimum engagement of $20K. Quantiphi uses fixed project and managed ai services pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: InData Labs or Quantiphi?
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 agency before shortlisting.
What are the main differences between InData Labs and Quantiphi?
InData Labs's primary differentiator is: dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing. Quantiphi's primary differentiator is: AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing. They also differ in team size (51–200 vs 1,001–5,000), minimum engagement ($20K vs Not published), and primary industries served (FinTech, Healthcare vs Financial Services, Healthcare).