DataRoot Labs vs Addepto: full comparison for 2026
Last updated: July 2026
Quick verdict
DataRoot Labs (4.5/5) edges ahead of Addepto (4.3/5) 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.. Addepto is the stronger option for companies seeking a Forbes/Deloitte-recognized AI consultancy, provided they factor in post-acquisition integration risk.. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs Addepto: head-to-head summary
| Criterion | DataRoot Labs | Addepto |
|---|---|---|
| Founded | 2016 | 2018 |
| HQ | Kyiv, Ukraine | Warsaw, Poland |
| Team size | 27–50 | 50–100 |
| Rating | 4.5 / 5 | 4.3 / 5 |
| Best for | Startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer. | Companies seeking a Forbes/Deloitte-recognized AI consultancy, provided they factor in post-acquisition integration risk. |
| Pricing model | Project-based, dedicated team | Project-based, consulting retainer |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, Hugging Face | Python, TensorFlow, AWS |
| Industries served | Startups (cross-industry), FinTech, Healthcare | FinTech, Manufacturing, Retail & E-commerce |
DataRoot Labs vs Addepto: 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.
Addepto
Addepto was founded in Warsaw in 2018 by Data Science enthusiasts Edwin Lisowski and Artur Haponik, delivering AI consulting and data-driven solutions recognized by Forbes, Deloitte, and the Financial Times. In December 2025, Addepto was acquired by KMS Technology, and prospective clients should confirm how delivery teams, pricing, and leadership continuity have changed post-acquisition. Reported employee counts vary from roughly 11–50 to 72, reflecting the transition period around the acquisition.
Services and capabilities: DataRoot Labs vs Addepto
| Capability | DataRoot Labs | Addepto |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: DataRoot Labs vs Addepto
| Framework / platform | DataRoot Labs | Addepto |
|---|---|---|
| TensorFlow | N/A | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DataRoot Labs vs Addepto
| Criterion | DataRoot Labs | Addepto |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team | Project-based, Consulting retainer |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs Addepto
| Dimension | DataRoot Labs | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Startups (cross-industry), FinTech, Healthcare | FinTech, Manufacturing, 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. | Company wants an AI consultancy with independent press recognition for vendor due diligence., Client is comfortable evaluating a firm mid-acquisition and confirming continuity directly before signing. |
| Typical project type | Project-based | Project-based |
DataRoot Labs vs Addepto: 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. |
| Addepto | |
|---|---|
| + | Press recognition from Forbes, Deloitte, and the Financial Times provides independent third-party validation beyond client testimonials. |
| + | Founder-led AI consulting model since 2018, prior to being acquired. |
| + | Now backed by KMS Technology's broader resources post-acquisition, which may add delivery capacity. |
| - | Acquired by KMS Technology in December 2025 — leadership continuity, pricing, and delivery-team stability during integration are unconfirmed. |
| - | Reported headcount varies significantly across sources (11–50 vs. 72), making current team size hard to pin down. |
| - | Recent acquisition means the company's standalone track record may not reflect how engagements are run going forward. |
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 Addepto?
Addepto is the right choice for companies seeking a Forbes/Deloitte-recognized AI consultancy, provided they factor in post-acquisition integration risk..
AI consulting boutique with third-party press recognition (Forbes, Deloitte, Financial Times), now part of KMS Technology.. Minimum engagement starts at Not published. Works best with clients in FinTech, Manufacturing, Retail & E-commerce.
Decision matrix: DataRoot Labs vs Addepto
| 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 Addepto (Not published) |
| You need specialist depth in a specific vertical | DataRoot Labs |
| You need production MLOps support after model launch | Both offer MLOps support |
| You need consulting before committing to a build | Addepto |
Use case fit: DataRoot Labs vs Addepto
| Use case | DataRoot Labs fit | Addepto 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 wants an AI consultancy with independent press recognition for vendor due diligence. | Strong | Strong | Both equally |
| Client is comfortable evaluating a firm mid-acquisition and confirming continuity directly before signing. | Limited | Strong | Addepto |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs Addepto
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..
Addepto (4.3/5) is the better choice when companies seeking a Forbes/Deloitte-recognized AI consultancy, provided they factor in post-acquisition integration risk.. If your situation matches those criteria, Addepto is a competitive option.
Related comparisons
DataRoot Labs vs Addepto FAQ
Is DataRoot Labs better than Addepto?
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.. Addepto is better for companies seeking a Forbes/Deloitte-recognized AI consultancy, provided they factor in post-acquisition integration risk..
How do DataRoot Labs and Addepto differ in pricing?
DataRoot Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. Addepto uses project-based, consulting retainer 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 Addepto?
Addepto 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 Addepto?
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.. Addepto's primary differentiator is: ai consulting boutique with third-party press recognition (forbes, deloitte, financial times), now part of kms technology.. They also differ in team size (27–50 vs 50–100), minimum engagement (Not published vs Not published), and primary industries served (Startups (cross-industry), FinTech vs FinTech, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.