DataRoot Labs vs AtliQ Technologies: full comparison for 2026
Quick verdict
DataRoot Labs (4.4/5) edges ahead of AtliQ Technologies (3.9/5) overall. DataRoot Labs is the better choice for startups needing applied ML research capacity. AtliQ Technologies is the stronger option for budget-conscious teams wanting AI added to a product build. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs AtliQ Technologies: head-to-head summary
| Criterion | DataRoot Labs | AtliQ Technologies |
|---|---|---|
| Founded | 2016 | 2017 |
| HQ | Kyiv, Ukraine | Vadodara, India |
| Team size | 11-50 | 50-220 |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Primary differentiator | Research-oriented engagement style built for startup speed, not enterprise procurement | US and India presence at startup-friendly pricing for a firm founded in 2017 |
| Pricing model | Dedicated team or fixed project | Fixed project or dedicated team |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, scikit-learn | Python, scikit-learn, AWS |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Retail & e-commerce, SaaS, Fintech |
DataRoot Labs vs AtliQ Technologies: 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. What's consistent is the specialty: machine learning models, computer vision pipelines, and hands-on AI R&D for startups that need research capability without hiring a full internal team.
AtliQ Technologies
AtliQ Technologies was founded in 2017 by Bhavin Patel and Dhaval Patel, based in Vadodara, Gujarat with an additional office in New Jersey. Public employee counts vary sharply, from roughly 42 to over 220 depending on the source and reporting date, worth confirming directly given how young the company is relative to others on this list. Its core work is software product and application development, with AI-driven data analysis added as a newer service rather than a founding specialty.
Services and capabilities: DataRoot Labs vs AtliQ Technologies
| Capability | DataRoot Labs | AtliQ Technologies |
|---|---|---|
| Generative AI | ✗ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✓ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataRoot Labs vs AtliQ Technologies
| Framework / platform | DataRoot Labs | AtliQ Technologies |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DataRoot Labs vs AtliQ Technologies
| Criterion | DataRoot Labs | AtliQ Technologies |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Fixed project | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs AtliQ Technologies
| Dimension | DataRoot Labs | AtliQ Technologies |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthtech, Fintech, Retail & e-commerce | Retail & e-commerce, SaaS, Fintech |
| Best use cases | Standing up an ML proof of concept ahead of a seed round., Getting a second, independent build on a computer vision pipeline. | Adding basic AI-driven analytics to a product already in development., Getting a budget-friendly product build where AI is a smaller part of the overall scope. |
| Typical project type | Dedicated team | Fixed project |
DataRoot Labs vs AtliQ Technologies: 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 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 |
| AtliQ Technologies | |
|---|---|
| + | Combined India and New Jersey presence gives clients a US point of contact at India-based delivery cost. |
| + | Founder-led team stays close to project delivery at this size. |
| + | AI added on top of an existing product development practice, not offered in isolation. |
| + | Younger firm tends to price more competitively than established mid-market vendors. |
| - | Public employee figures vary by nearly 5x, making true team capacity hard to confirm |
| - | Shorter operating history than most other agencies on this list |
Who should choose DataRoot Labs?
A typical fit: standing up an ML 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 AtliQ Technologies?
A typical fit: adding basic AI-driven analytics to a product already in development.
US and India presence at startup-friendly pricing for a firm founded in 2017. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, SaaS, Fintech.
Decision matrix: DataRoot Labs vs AtliQ Technologies
| 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 AtliQ Technologies (Not disclosed) |
| You need specialist depth in a specific vertical | DataRoot Labs |
| 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 AtliQ Technologies
| Use case | DataRoot Labs fit | AtliQ Technologies fit | Winner |
|---|---|---|---|
| Standing up an ML proof of concept ahead of a seed round. | Strong | Limited | DataRoot Labs |
| Getting a second, independent build on a computer vision pipeline. | Strong | Strong | Both equally |
| Adding basic AI-driven analytics to a product already in development. | Strong | Strong | Both equally |
| Getting a budget-friendly product build where AI is a smaller part of the overall scope. | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs AtliQ Technologies
DataRoot Labs (4.4/5) is the stronger overall choice for most AI Development projects. Research-oriented engagement style built for startup speed, not enterprise procurement.
AtliQ Technologies (3.9/5) is worth a look if you need getting a budget-friendly product build where AI is a smaller part of the overall scope. If your situation matches that, AtliQ Technologies is a competitive option.
Related comparisons
DataRoot Labs vs AtliQ Technologies FAQ
Is DataRoot Labs better than AtliQ Technologies?
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. AtliQ Technologies's strongest advantage: combined India and New Jersey presence gives clients a US point of contact at India-based delivery cost.
How do DataRoot Labs and AtliQ Technologies differ in pricing?
DataRoot Labs uses dedicated team or fixed project pricing. AtliQ Technologies uses fixed project or dedicated team 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 AtliQ Technologies?
AtliQ Technologies 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 DataRoot Labs and AtliQ Technologies?
DataRoot Labs's primary differentiator is: research-oriented engagement style built for startup speed, not enterprise procurement. AtliQ Technologies's primary differentiator is: US and India presence at startup-friendly pricing for a firm founded in 2017. They also differ in team size (11-50 vs 50-220), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthtech, Fintech vs Retail & e-commerce, SaaS).
Verify all details directly with each agency before making a decision.