DataRoot Labs vs SoftServe: full comparison for 2026
Last updated: July 2026
Quick verdict
DataRoot Labs (4.5/5) edges ahead of SoftServe (4.0/5) 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.. SoftServe is the stronger option for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices.. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs SoftServe: head-to-head summary
| Criterion | DataRoot Labs | SoftServe |
|---|---|---|
| Founded | 2016 | 1993 |
| HQ | Kyiv, Ukraine | Austin, Texas, United States / Lviv, Ukraine |
| Team size | 27–50 | 12,000+ |
| Rating | 4.5 / 5 | 4.0 / 5 |
| Best for | Startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer. | Enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices. |
| Pricing model | Project-based, dedicated team | Time & materials, managed engagement |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, Hugging Face | AWS, Azure, Google Cloud |
| Industries served | Startups (cross-industry), FinTech, Healthcare | Healthcare, FinTech, Retail & E-commerce, Manufacturing, Energy |
DataRoot Labs vs SoftServe: 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.
SoftServe
SoftServe was founded in 1993 in Lviv, Ukraine and now operates with a US headquarters in Austin, Texas and a European headquarters in Lviv, employing more than 12,000 people across 58 offices in 14 countries (with one source citing roughly 10,336 as of a recent count). The company's offerings span digital engineering, data analytics, cloud services, AI, machine learning, and IoT, and it ranked seventh among more than 130 Western European companies in Clutch's 2019 software development category. Its scale and 30+ year history make it a large, generalist engineering firm with AI as one of several core practices.
Services and capabilities: DataRoot Labs vs SoftServe
| Capability | DataRoot Labs | SoftServe |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✗ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: DataRoot Labs vs SoftServe
| Framework / platform | DataRoot Labs | SoftServe |
|---|---|---|
| TensorFlow | N/A | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: DataRoot Labs vs SoftServe
| Criterion | DataRoot Labs | SoftServe |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs SoftServe
| Dimension | DataRoot Labs | SoftServe |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Startups (cross-industry), FinTech, Healthcare | Healthcare, FinTech, Retail & E-commerce |
| 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. | Large enterprise wants a single vendor covering AI/ML alongside cloud, data analytics, and IoT services., Company needs a choice between US and EU contracting jurisdictions from the same firm. |
| Typical project type | Project-based | Managed engagement |
DataRoot Labs vs SoftServe: 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. |
| SoftServe | |
|---|---|
| + | 12,000+ employees across 58 offices in 14 countries gives it enterprise-scale delivery capacity and geographic redundancy. |
| + | 31 years of continuous operation (since 1993) through multiple technology cycles, including the post-2022 relocation pressures on Ukraine-founded firms. |
| + | Ranked 7th among 130+ Western European companies in Clutch's 2019 software development category, an independently sourced recognition. |
| + | Dual US/Ukraine headquarters structure gives clients a choice of contracting jurisdiction. |
| - | 12,000+ person scale means AI/ML is one of several mature practices (alongside cloud, data analytics, IoT) rather than the firm's core identity. |
| - | Reported employee counts vary by thousands across sources (10,336 vs. 12,000+), reflecting the difficulty of pinning down exact current headcount at this scale. |
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 SoftServe?
SoftServe is the right choice for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices..
31 years of engineering history (since 1993) with dual US and Ukraine headquarters and 12,000+ employees.. Minimum engagement starts at Not published. Works best with clients in Healthcare, FinTech, Retail & E-commerce, Manufacturing, Energy.
Decision matrix: DataRoot Labs vs SoftServe
| 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 SoftServe (Not published) |
| You need specialist depth in a specific vertical | SoftServe |
| You need production MLOps support after model launch | SoftServe |
| You need consulting before committing to a build | SoftServe |
Use case fit: DataRoot Labs vs SoftServe
| Use case | DataRoot Labs fit | SoftServe 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 |
| Large enterprise wants a single vendor covering AI/ML alongside cloud, data analytics, and IoT services. | Strong | Strong | Both equally |
| Company needs a choice between US and EU contracting jurisdictions from the same firm. | Strong | Strong | Both equally |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs SoftServe
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..
SoftServe (4.0/5) is the better choice when enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices.. If your situation matches those criteria, SoftServe is a competitive option.
Related comparisons
DataRoot Labs vs SoftServe FAQ
Is DataRoot Labs better than SoftServe?
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.. SoftServe is better for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices..
How do DataRoot Labs and SoftServe differ in pricing?
DataRoot Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. SoftServe uses time & materials, managed engagement 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 SoftServe?
SoftServe 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 SoftServe?
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.. SoftServe's primary differentiator is: 31 years of engineering history (since 1993) with dual us and ukraine headquarters and 12,000+ employees.. They also differ in team size (27–50 vs 12,000+), minimum engagement (Not published vs Not published), and primary industries served (Startups (cross-industry), FinTech vs Healthcare, FinTech).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.