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 generative AI research capacity. Infosys is the stronger option for global enterprises needing generative 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 | Research-oriented engagement style built for startup speed, not enterprise procurement | 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, OpenAI API | Python, OpenAI API, AWS |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Financial services, Manufacturing, Retail & e-commerce, Telecom |
DataRoot Labs vs Infosys: overview
DataRoot Labs
DataRoot Labs runs out of Kyiv and has focused on applied data science research since founding in 2016. Public staff counts vary widely, from about 11 to nearly 200 depending on the source, likely a function of how contractors get counted differently across trackers. Its generative AI and machine learning work sits alongside computer vision pipelines and hands-on AI R&D for startups that need research capability without hiring a full internal team.
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 generative 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 layer on top. At this scale, generative AI development is one thread inside one of the world's largest IT services organizations.
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 | ✓ | ✓ |
| OpenAI API | ✓ | ✓ |
| PyTorch | ✓ | 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 | Standing up a generative AI proof of concept ahead of a seed round., Getting a second, independent build on a generative AI or computer vision pipeline. | Running a generative 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 suits startups needing genuine experimentation over templated builds. |
| + | Small team keeps direct communication between founders and the engineers doing the work. |
| + | Kyiv talent pool offers strong ML fundamentals at lower cost than US or Western European teams. |
| + | Named computer vision and generative AI projects back up the firm's stated specialty. |
| - | Employee counts differ substantially across public sources, making capacity hard to verify |
| - | Little public evidence of enterprise-scale delivery experience |
| Infosys | |
|---|---|
| + | Massive global scale (330,000-plus employees) supports the largest enterprise generative 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. |
| - | Generative 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: standing up a generative AI proof of concept ahead of a seed round.
Research-oriented engagement style built for startup speed, not enterprise procurement. Minimum engagement is not publicly disclosed. Works best with clients in Healthtech, Fintech, Retail & e-commerce.
Who should choose Infosys?
A typical fit: running a generative 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 | Infosys |
Use case fit: DataRoot Labs vs Infosys
| Use case | DataRoot Labs fit | Infosys fit | Winner |
|---|---|---|---|
| Standing up a generative AI proof of concept ahead of a seed round. | Strong | Limited | DataRoot Labs |
| Getting a second, independent build on a generative AI or computer vision pipeline. | Strong | Limited | DataRoot Labs |
| Running a generative 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 Generative AI Development projects. Research-oriented engagement style built for startup speed, not enterprise procurement.
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 suits startups needing genuine experimentation over templated builds. Infosys's strongest advantage: massive global scale (330,000-plus employees) supports the largest enterprise generative 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 company before shortlisting.
What are the main differences between DataRoot Labs and Infosys?
DataRoot Labs's primary differentiator is: research-oriented engagement style built for startup speed, not enterprise procurement. 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 company before making a decision.