DataRoot Labs vs Infosys: full comparison for 2026
Quick verdict
DataRoot Labs (4.4/5) edges ahead of Infosys (3.9/5) overall. DataRoot Labs is the better choice for startups needing applied ML research on demand. Infosys is the stronger option for global enterprises needing AI inside a full IT services contract. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs Infosys: head-to-head summary
| Criterion | DataRoot Labs | Infosys |
|---|---|---|
| Founded | 2016 | 1981 |
| HQ | Kyiv, Ukraine | Bengaluru, India |
| Team size | 11-50 | 330,000+ |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Primary differentiator | R&D-oriented engagement style built for startup pace, not enterprise procurement cycles | One of the world's largest IT services firms with a dedicated London-based consulting arm |
| Pricing model | Dedicated team or fixed project | Retainer, enterprise contracting |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, scikit-learn | Python, AWS, Azure |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Financial services, Manufacturing, Retail & e-commerce, Telecom |
DataRoot Labs vs Infosys: overview
DataRoot Labs
Kyiv is home base for DataRoot Labs, founded in 2016 with a stated focus on applied data science research rather than broad IT outsourcing. Sources disagree on staff size, some citing as few as 11 employees and others closer to 200, likely reflecting how contractor networks get counted differently across platforms. What stays consistent across sources is the firm's specialization: machine learning models, computer vision pipelines, and hands-on AI R&D for startups that need research capability without building an internal team from scratch.
Infosys
Infosys was founded in 1981 and is headquartered in Bengaluru, India, employing approximately 330,429 people worldwide as of March 2026. The company delivers a comprehensive suite of enterprise AI development services alongside automation, cybersecurity, and advanced data analytics, and its wholly-owned subsidiary Infosys Consulting, founded in 2004 and headquartered in London, adds a dedicated strategy and consulting layer on top. At this scale, AI development is one thread inside one of the world's largest IT services organizations rather than a boutique specialty.
Services and capabilities: DataRoot Labs vs Infosys
| Capability | DataRoot Labs | Infosys |
|---|---|---|
| Generative AI | ✗ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✓ | ✓ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataRoot Labs vs Infosys
| Framework / platform | DataRoot Labs | Infosys |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: DataRoot Labs vs Infosys
| Criterion | DataRoot Labs | Infosys |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Fixed project | Retainer, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs Infosys
| Dimension | DataRoot Labs | Infosys |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthtech, Fintech, Retail & e-commerce | Financial services, Manufacturing, Retail & e-commerce |
| Best use cases | Building an ML proof of concept ahead of a seed-stage fundraise., Getting an independent second opinion or build on a computer vision pipeline. | Running an AI initiative as part of a much larger enterprise IT services contract., Needing a globally recognized vendor for board-level procurement approval. |
| Typical project type | Dedicated team | Retainer |
DataRoot Labs vs Infosys: pros and cons
| DataRoot Labs | |
|---|---|
| + | Research culture fits startups needing genuine experimentation over templated builds. |
| + | Small enough that founders talk directly to the engineers doing the work. |
| + | Kyiv-based ML talent typically comes at lower rates than US or Western European equivalents. |
| + | Named computer vision projects back up the specialization claim. |
| - | Employee counts vary widely across public sources, making capacity hard to pin down precisely |
| - | Limited public evidence of enterprise-scale delivery experience |
| Infosys | |
|---|---|
| + | Massive global scale (330,000-plus employees) supports the largest enterprise AI programs. |
| + | Dedicated Infosys Consulting subsidiary adds a strategy layer alongside technical delivery. |
| + | Four decades of operating history and deep enterprise procurement relationships. |
| + | Broad cloud and enterprise software partnerships reduce platform risk. |
| - | AI is one part of an enormous general IT services business, not a specialized focus |
| - | Scale typically means slower engagement setup than smaller, more agile firms |
Who should choose DataRoot Labs?
A typical fit: building an ML proof of concept ahead of a seed-stage fundraise.
R&D-oriented engagement style built for startup pace, not enterprise procurement cycles. Minimum engagement is not publicly disclosed. Works best with clients in Healthtech, Fintech, Retail & e-commerce.
Who should choose Infosys?
A typical fit: running an AI initiative as part of a much larger enterprise IT services contract.
One of the world's largest IT services firms with a dedicated London-based consulting arm. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Manufacturing, Retail & e-commerce, Telecom.
Decision matrix: DataRoot Labs vs Infosys
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs (Not disclosed) vs Infosys (Not disclosed) |
| You need specialist depth in a specific vertical | Infosys |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | DataRoot Labs |
Use case fit: DataRoot Labs vs Infosys
| Use case | DataRoot Labs fit | Infosys fit | Winner |
|---|---|---|---|
| Building an ML proof of concept ahead of a seed-stage fundraise. | Strong | Limited | DataRoot Labs |
| Getting an independent second opinion or build on a computer vision pipeline. | Strong | Limited | DataRoot Labs |
| Running an AI initiative as part of a much larger enterprise IT services contract. | Limited | Strong | Infosys |
| Needing a globally recognized vendor for board-level procurement approval. | Limited | Strong | Infosys |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs Infosys
DataRoot Labs (4.4/5) is the stronger overall choice for most AI Development projects. R&D-oriented engagement style built for startup pace, not enterprise procurement cycles.
Infosys (3.9/5) is worth a look if you need needing a globally recognized vendor for board-level procurement approval. If your situation matches that, Infosys is a competitive option.
Related comparisons
DataRoot Labs vs Infosys FAQ
Is DataRoot Labs better than Infosys?
DataRoot Labs (4.4/5) scores higher overall, but "better" depends on your use case. DataRoot Labs's strongest advantage: research culture fits startups needing genuine experimentation over templated builds. Infosys's strongest advantage: massive global scale (330,000-plus employees) supports the largest enterprise AI programs.
How do DataRoot Labs and Infosys differ in pricing?
DataRoot Labs uses dedicated team or fixed project pricing. Infosys uses retainer, enterprise contracting pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataRoot Labs or Infosys?
Infosys is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each firm before shortlisting.
What are the main differences between DataRoot Labs and Infosys?
DataRoot Labs's primary differentiator is: R&D-oriented engagement style built for startup pace, not enterprise procurement cycles. Infosys's primary differentiator is: one of the world's largest IT services firms with a dedicated London-based consulting arm. They also differ in team size (11-50 vs 330,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthtech, Fintech vs Financial services, Manufacturing).
Verify all details directly with each firm before making a decision.