DataRoot Labs vs SoluLab: full comparison for 2026
Last updated: July 2026
Quick verdict
DataRoot Labs (4.5/5) edges ahead of SoluLab (4.1/5) overall. DataRoot Labs is the better choice for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer.. SoluLab is the stronger option for companies that want AI development from a vendor also fluent in blockchain/Web3 integration.. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs SoluLab: head-to-head summary
| Criterion | DataRoot Labs | SoluLab |
|---|---|---|
| Founded | 2016 | 2014 |
| HQ | Kyiv, Ukraine | Woodland Hills, California, United States |
| Team size | 27–50 | 246–250 |
| Rating | 4.5 / 5 | 4.1 / 5 |
| Best for | Startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer. | Companies that want AI development from a vendor also fluent in blockchain/Web3 integration. |
| Pricing model | Project-based, dedicated team | Project-based, dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, Hugging Face | OpenAI API, LangChain, Python |
| Industries served | Startups (cross-industry), FinTech, Healthcare | Media & Entertainment, Automotive, Education, FinTech |
DataRoot Labs vs SoluLab: overview
DataRoot Labs
DataRoot Labs was founded in 2016 in Kyiv, Ukraine and has worked exclusively in AI and R&D since inception, building generative AI, machine learning, and data engineering systems for startups and enterprises. The company is notably lean — roughly 27 employees across three continents as of late 2025 — and also runs DataRoot University, a free ML and data engineering school with more than 6,000 graduates, which doubles as its own technical talent pipeline. Its small size and academic ties make it a lower-cost, highly specialized option relative to larger regional peers.
SoluLab
SoluLab was founded in 2014–2015 by Chintan Thakkar and Rajat Lala and is headquartered in Woodland Hills, California, with a team of roughly 246–250 engineers, data scientists, and AI specialists. The firm positions itself as an 'AI-native, Blockchain, and Web3' development company and reports having delivered 1,500+ projects across 15+ countries for clients including The Walt Disney Company, Mercedes-Benz, and the University of Cambridge (per company website; independently unverifiable at this scale). Its dual focus on AI and blockchain/Web3 makes it broader than a pure ML specialist.
Services and capabilities: DataRoot Labs vs SoluLab
| Capability | DataRoot Labs | SoluLab |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: DataRoot Labs vs SoluLab
| Framework / platform | DataRoot Labs | SoluLab |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Google Cloud | N/A | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DataRoot Labs vs SoluLab
| Criterion | DataRoot Labs | SoluLab |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team | Project-based, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs SoluLab
| Dimension | DataRoot Labs | SoluLab |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Startups (cross-industry), FinTech, Healthcare | Media & Entertainment, Automotive, Education |
| Best use cases | Startup with a limited AI budget needs senior-level generative AI or ML engineering without enterprise agency overhead., Company wants a lean, R&D-focused partner for an experimental ML feature rather than a large staffing engagement. | Company building an AI product with a blockchain or Web3 component needs a single integrated vendor., Enterprise wants a vendor with named brand-name reference clients for procurement comfort. |
| Typical project type | Project-based | Project-based |
DataRoot Labs vs SoluLab: pros and cons
| DataRoot Labs | |
|---|---|
| + | Team of roughly 27 keeps overhead low, which typically translates into lower blended rates than 500+ person firms. |
| + | Exclusive AI/R&D focus since 2016 with no general software-development sideline diluting expertise. |
| + | DataRoot University (6,000+ graduates) gives the firm a homegrown, vetted junior-to-mid talent pipeline instead of relying purely on open-market hiring. |
| + | Cost/accessibility standout among the researched companies for startups with constrained AI budgets. |
| - | 27–50 person team size limits capacity for multiple large concurrent enterprise engagements. |
| - | Small headcount means less bench depth if a key engineer rotates off a project mid-engagement. |
| - | Thinner public enterprise case-study base than larger Ukraine-headquartered peers like N-iX or ELEKS. |
| SoluLab | |
|---|---|
| + | Named enterprise clients (The Walt Disney Company, Mercedes-Benz, University of Cambridge) offer verifiable reference points, though the specific scope of each engagement is unconfirmed. |
| + | 246–250 team size supports mid-to-large engagements without enterprise-firm overhead. |
| + | Combined AI and blockchain/Web3 capability is useful for clients building tokenized or decentralized AI products. |
| + | 10 years of company history (since 2014–2015) under continuous founder leadership. |
| - | 1,500+ projects claim across 15+ countries is difficult to independently verify at face value. |
| - | Blockchain/Web3 focus alongside AI means clients purely interested in ML may be paying for adjacent expertise they don't need. |
Who should choose DataRoot Labs?
DataRoot Labs is the right choice for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer..
Runs its own free ML/data-engineering school (DataRoot University, 6,000+ graduates) as a self-built talent pipeline.. Minimum engagement starts at Not published. Works best with clients in Startups (cross-industry), FinTech, Healthcare.
Who should choose SoluLab?
SoluLab is the right choice for companies that want AI development from a vendor also fluent in blockchain/Web3 integration..
Combines AI-native development with blockchain/Web3 expertise under one delivery team.. Minimum engagement starts at Not published. Works best with clients in Media & Entertainment, Automotive, Education, FinTech.
Decision matrix: DataRoot Labs vs SoluLab
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs (Not published) vs SoluLab (Not published) |
| You need specialist depth in a specific vertical | SoluLab |
| You need production MLOps support after model launch | Both offer MLOps support |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: DataRoot Labs vs SoluLab
| Use case | DataRoot Labs fit | SoluLab fit | Winner |
|---|---|---|---|
| Startup with a limited AI budget needs senior-level generative AI or ML engineering without enterprise agency overhead. | Strong | Limited | DataRoot Labs |
| Company wants a lean, R&D-focused partner for an experimental ML feature rather than a large staffing engagement. | Strong | Strong | Both equally |
| Company building an AI product with a blockchain or Web3 component needs a single integrated vendor. | Strong | Strong | Both equally |
| Enterprise wants a vendor with named brand-name reference clients for procurement comfort. | Strong | Strong | Both equally |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs SoluLab
DataRoot Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Runs its own free ML/data-engineering school (DataRoot University, 6,000+ graduates) as a self-built talent pipeline.. It is best for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer..
SoluLab (4.1/5) is the better choice when companies that want AI development from a vendor also fluent in blockchain/Web3 integration.. If your situation matches those criteria, SoluLab is a competitive option.
Related comparisons
DataRoot Labs vs SoluLab FAQ
Is DataRoot Labs better than SoluLab?
DataRoot Labs (4.5/5) scores higher overall, but "better" depends on your use case. DataRoot Labs is better for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer.. SoluLab is better for companies that want AI development from a vendor also fluent in blockchain/Web3 integration..
How do DataRoot Labs and SoluLab differ in pricing?
DataRoot Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. SoluLab uses project-based, dedicated team 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: DataRoot Labs or SoluLab?
SoluLab 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 SoluLab?
DataRoot Labs's primary differentiator is: runs its own free ml/data-engineering school (dataroot university, 6,000+ graduates) as a self-built talent pipeline.. SoluLab's primary differentiator is: combines ai-native development with blockchain/web3 expertise under one delivery team.. They also differ in team size (27–50 vs 246–250), minimum engagement (Not published vs Not published), and primary industries served (Startups (cross-industry), FinTech vs Media & Entertainment, Automotive).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.