Infosys vs DataRoot Labs: full comparison for 2026
Quick verdict
Infosys (4.0/5) edges ahead of DataRoot Labs (3.9/5) overall. Infosys is the better choice for global enterprises needing AI advisory inside a full IT services contract. DataRoot Labs is the stronger option for startups needing applied AI research capacity. The right choice depends on your project size, budget, and required tech stack.
Infosys vs DataRoot Labs: head-to-head summary
| Criterion | Infosys | DataRoot Labs |
|---|---|---|
| Founded | 1981 | 2016 |
| HQ | Bengaluru, India | Kyiv, Ukraine |
| Team size | 330,000+ | 11-50 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | One of the world's largest IT services firms with a dedicated London-based advisory arm | A research-oriented engagement style built for startup speed, not enterprise procurement |
| Pricing model | Retainer, enterprise contracting | Dedicated team or fixed project |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, AWS, Azure | Python, PyTorch, scikit-learn |
| Industries served | Financial services, Manufacturing, Retail & e-commerce, Telecom | Healthtech, Fintech, Retail & e-commerce |
Infosys vs DataRoot Labs: overview
Infosys
Infosys was founded in 1981 and is headquartered in Bengaluru, India, employing approximately 330,429 people worldwide as of March 2026. It runs a full suite of enterprise AI advisory services, and its wholly-owned subsidiary Infosys Consulting, founded in 2004 and headquartered in London, adds a dedicated strategy arm separate from the parent's much larger delivery organization.
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, likely a function of how contractors get counted differently across trackers. Its work centers on machine learning models, computer vision pipelines, and hands-on AI research and development for startups that need real research capability and technical AI advisory without hiring a full internal team.
Services and capabilities: Infosys vs DataRoot Labs
| Capability | Infosys | DataRoot Labs |
|---|---|---|
| AI strategy consulting | ✓ | ✓ |
| Generative AI | ✗ | ✗ |
| Machine learning | ✓ | ✓ |
| Data engineering | ✓ | ✓ |
| MLOps | ✗ | ✗ |
| Fixed-price projects | ✗ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: Infosys vs DataRoot Labs
| Framework / platform | Infosys | DataRoot Labs |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| LangChain | N/A | N/A |
| PyTorch | N/A | ✓ |
Pricing comparison: Infosys vs DataRoot Labs
| Criterion | Infosys | DataRoot Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Retainer, Dedicated team | Dedicated team, Fixed project |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Infosys vs DataRoot Labs
| Dimension | Infosys | DataRoot Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Manufacturing, Retail & e-commerce | Healthtech, Fintech, Retail & e-commerce |
| Best use cases | Running an AI advisory initiative as part of a much larger enterprise IT services contract., Needing a globally recognized agency for board-level procurement approval. | Getting an independent AI strategy assessment ahead of a seed round., Bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. |
| Typical project type | Retainer | Dedicated team |
Infosys vs DataRoot Labs: pros and cons
| Infosys | |
|---|---|
| + | Massive global scale, 330,000-plus employees, supports the largest enterprise AI programs. |
| + | The dedicated Infosys Consulting subsidiary adds a London-based advisory layer. |
| + | Four decades of operating history and deep enterprise procurement relationships. |
| + | Cloud and enterprise software partnerships span multiple platforms, reducing lock-in. |
| - | AI advisory is one part of an enormous general IT services business, not a specialized focus |
| - | Scale generally means slower engagement setup than smaller, more agile agencies |
| DataRoot Labs | |
|---|---|
| + | A research culture suits startups needing genuine experimentation over templated builds. |
| + | A small team keeps direct communication between founders and the engineers doing the work. |
| + | Kyiv's talent pool offers strong ML fundamentals at lower cost than US or Western European teams. |
| + | Named computer vision projects back up the agency's stated specialty. |
| - | Employee counts differ substantially across public sources, making capacity hard to verify |
| - | Little public evidence of enterprise-scale delivery experience |
Who should choose Infosys?
A typical fit: running an AI advisory 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 advisory arm. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Manufacturing, Retail & e-commerce, Telecom.
Who should choose DataRoot Labs?
A typical fit: getting an independent AI strategy assessment ahead of a seed round.
A 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.
Decision matrix: Infosys vs DataRoot Labs
| 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 | Infosys |
| Your budget is at the lower end | Compare: Infosys (Not disclosed) vs DataRoot Labs (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: Infosys vs DataRoot Labs
| Use case | Infosys fit | DataRoot Labs fit | Winner |
|---|---|---|---|
| Running an AI advisory initiative as part of a much larger enterprise IT services contract. | Strong | Limited | Infosys |
| Needing a globally recognized agency for board-level procurement approval. | Strong | Limited | Infosys |
| Getting an independent AI strategy assessment ahead of a seed round. | Limited | Strong | DataRoot Labs |
| Bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. | Limited | Strong | DataRoot Labs |
| Fixed-price project | Limited | Limited | Both equally |
| Dedicated team model | Limited | Strong | DataRoot Labs |
Verdict: Infosys vs DataRoot Labs
Infosys (4.0/5) is the stronger overall choice for most AI Consulting projects. One of the world's largest IT services firms with a dedicated London-based advisory arm.
DataRoot Labs (3.9/5) is worth a look if you need bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. If your situation matches that, DataRoot Labs is a competitive option.
Related comparisons
Infosys vs DataRoot Labs FAQ
Is Infosys better than DataRoot Labs?
Infosys (4.0/5) scores higher overall, but "better" depends on your use case. Infosys's strongest advantage: massive global scale, 330,000-plus employees, supports the largest enterprise AI programs. DataRoot Labs's strongest advantage: a research culture suits startups needing genuine experimentation over templated builds.
How do Infosys and DataRoot Labs differ in pricing?
Infosys uses retainer, enterprise contracting pricing. DataRoot Labs uses dedicated team or fixed project pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Infosys or DataRoot Labs?
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 agency before shortlisting.
What are the main differences between Infosys and DataRoot Labs?
Infosys's primary differentiator is: one of the world's largest IT services firms with a dedicated London-based advisory arm. DataRoot Labs's primary differentiator is: a research-oriented engagement style built for startup speed, not enterprise procurement. They also differ in team size (330,000+ vs 11-50), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Manufacturing vs Healthtech, Fintech).
Verify all details directly with each agency before making a decision.