DataRoot Labs vs SoftKraft: full comparison for 2026
Quick verdict
DataRoot Labs (4.4/5) edges ahead of SoftKraft (4.0/5) overall. DataRoot Labs is the better choice for startups needing applied ML research capacity. SoftKraft is the stronger option for startups on tight budgets needing data-driven MVPs. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs SoftKraft: head-to-head summary
| Criterion | DataRoot Labs | SoftKraft |
|---|---|---|
| Founded | 2016 | 2015 |
| HQ | Kyiv, Ukraine | Bielsko-Biala, Poland |
| Team size | 11-50 | 11-50 |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Primary differentiator | Research-oriented engagement style built for startup speed, not enterprise procurement | Small dedicated team priced for startup budgets, not enterprise rates |
| 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, PostgreSQL, Apache Airflow |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Fintech, SaaS, Healthtech |
DataRoot Labs vs SoftKraft: 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.
SoftKraft
SoftKraft was founded in 2015 by CEO Marek Petrykowski and CTO Blazej Kosmowski, running a lean 11-50 person team from Bielsko-Biala, Poland. Around 70% of its clients are North American despite the delivery team sitting in Poland, a common pattern for smaller nearshore AI agencies. The firm's positioning centers on data-driven software, AI, and data engineering built specifically for startups and small-to-mid-sized companies, not enterprise accounts.
Services and capabilities: DataRoot Labs vs SoftKraft
| Capability | DataRoot Labs | SoftKraft |
|---|---|---|
| Generative AI | ✗ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✓ | ✓ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataRoot Labs vs SoftKraft
| Framework / platform | DataRoot Labs | SoftKraft |
|---|---|---|
| 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 SoftKraft
| Criterion | DataRoot Labs | SoftKraft |
|---|---|---|
| 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 SoftKraft
| Dimension | DataRoot Labs | SoftKraft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthtech, Fintech, Retail & e-commerce | Fintech, SaaS, Healthtech |
| 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. | Building a data-driven MVP for a pre-seed or seed-stage startup., Getting AI and data engineering handled by one small, accountable team. |
| Typical project type | Dedicated team | Fixed project |
DataRoot Labs vs SoftKraft: 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 |
| SoftKraft | |
|---|---|
| + | Smaller team size keeps overhead, and likely cost, below mid-size and enterprise agencies. |
| + | 70% North American client base shows the team has adapted to US buyer expectations from Poland. |
| + | Founder-led leadership stays close to delivery rather than purely sales. |
| + | Startup and SME focus means scope and pricing fit smaller budgets from the outset. |
| - | Team of 11-50 limits capacity to a handful of concurrent projects |
| - | Less public case-study history than agencies with a decade-plus track record |
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 SoftKraft?
A typical fit: building a data-driven MVP for a pre-seed or seed-stage startup.
Small dedicated team priced for startup budgets, not enterprise rates. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, SaaS, Healthtech.
Decision matrix: DataRoot Labs vs SoftKraft
| 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 SoftKraft (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 SoftKraft
| Use case | DataRoot Labs fit | SoftKraft 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 |
| Building a data-driven MVP for a pre-seed or seed-stage startup. | Limited | Strong | SoftKraft |
| Getting AI and data engineering handled by one small, accountable team. | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs SoftKraft
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.
SoftKraft (4.0/5) is worth a look if you need getting AI and data engineering handled by one small, accountable team. If your situation matches that, SoftKraft is a competitive option.
Related comparisons
DataRoot Labs vs SoftKraft FAQ
Is DataRoot Labs better than SoftKraft?
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. SoftKraft's strongest advantage: smaller team size keeps overhead, and likely cost, below mid-size and enterprise agencies.
How do DataRoot Labs and SoftKraft differ in pricing?
DataRoot Labs uses dedicated team or fixed project pricing. SoftKraft 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 SoftKraft?
DataRoot Labs 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 SoftKraft?
DataRoot Labs's primary differentiator is: research-oriented engagement style built for startup speed, not enterprise procurement. SoftKraft's primary differentiator is: small dedicated team priced for startup budgets, not enterprise rates. They also differ in team size (11-50 vs 11-50), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthtech, Fintech vs Fintech, SaaS).
Verify all details directly with each agency before making a decision.