Addepto vs Dataroots: full comparison for 2026
Quick verdict
Addepto (3.9/5) edges ahead of Dataroots (3.8/5) overall. Addepto is the better choice for industrial and automotive companies adding AI and data engineers to an internal team. Dataroots is the stronger option for benelux enterprises that need ML and data engineers inside their own teams. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Dataroots: head-to-head summary
| Criterion | Addepto | Dataroots |
|---|---|---|
| Founded | 2017 | 2016 |
| HQ | Warsaw, Poland | Leuven, Belgium |
| Team size | 50–99 (directory estimate) | 100+ (at 2022 acquisition) |
| Rating | 3.9 / 5 | 3.8 / 5 |
| Primary differentiator | AI-heavy team with manufacturing domain experience, now backed by a larger group | Benelux data platform specialists backed by Talan's wider consulting group |
| Pricing model | Collaborative team model or managed delivery; rates on request | Consultant day rates; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Databricks, Spark | Python, dbt, Databricks |
| Industries served | Manufacturing, Automotive, Retail, Aviation | Financial services, Public sector, Retail, Energy |
Addepto vs Dataroots: overview
Addepto
Addepto has worked on AI and data in Warsaw since 2017, with a strong client base in industrial and automotive companies. KMS Technology, an Atlanta engineering firm backed by Sunstone Partners, acquired it in December 2025. Its collaborative cooperation model puts Addepto engineers alongside the client's own team, and the company has said publicly it is not a body-leasing firm. After the deal, its CEO said 97% of the team are AI engineers.
Dataroots
Bart Smeets founded Dataroots in Leuven in 2016, and it grew into a team of more than 100 ML engineers, data engineers and data architects. Talan, the French consultancy, acquired it in December 2022 and folded it into a data practice of over 800 consultants. Staffing appears among its listed services, and Belgian clients use Dataroots consultants inside their own data teams. Its work centers on AI and next-generation data platforms.
Services and capabilities: Addepto vs Dataroots
| Capability | Addepto | Dataroots |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✗ |
| AI agent development | ✗ | ✗ |
| MLOps & deployment | ✗ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| Fractional / part-time experts | ✗ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Forward-deployed engineers | ✗ | ✗ |
| Access to a wider AI talent network | ✗ | ✗ |
Tech stack comparison: Addepto vs Dataroots
| Framework / platform | Addepto | Dataroots |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Databricks | ✓ | ✓ |
| MLflow | N/A | ✓ |
Pricing comparison: Addepto vs Dataroots
| Criterion | Addepto | Dataroots |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Embedded team, Project delivery | Dedicated engineers, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Addepto vs Dataroots
| Dimension | Addepto | Dataroots |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Retail | Financial services, Public sector, Retail |
| Best use cases | Adding Databricks engineers to a manufacturer's data team, Building a GenAI assistant for automotive service documents | Placing data engineers in a Belgian bank's platform team, Building an MLOps setup on Azure |
| Typical project type | Embedded team | Dedicated engineers |
Addepto vs Dataroots: pros and cons
| Addepto | |
|---|---|
| + | Nearly the whole team is AI engineers, according to its CEO |
| + | Industrial and automotive client experience |
| + | KMS ownership adds broader engineering capacity behind it |
| - | Acquired by KMS Technology in December 2025; ownership changes can bring new contract terms |
| - | Prefers joint delivery to straight staff placement |
| - | Team size estimates range from 8 to 99 |
| Dataroots | |
|---|---|
| + | Strong data platform skills to go with ML work |
| + | Talan backing adds capacity across Europe |
| + | Leuven and Ghent offices put it close to Benelux clients |
| - | Owned by Talan since December 2022, so it no longer operates independently |
| - | Mainly a Benelux business |
| - | Staffing model details are not published |
Who should choose Addepto?
A typical fit: adding Databricks engineers to a manufacturer's data team.
AI-heavy team with manufacturing domain experience, now backed by a larger group. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Automotive, Retail, Aviation.
Who should choose Dataroots?
A typical fit: placing data engineers in a Belgian bank's platform team.
Benelux data platform specialists backed by Talan's wider consulting group. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Public sector, Retail, Energy.
Decision matrix: Addepto vs Dataroots
| Your situation | Recommended choice |
|---|---|
| You need one AI specialist part-time | Neither advertises part-time experts; ask about reduced hours |
| You need several engineers working as one team | Both; Addepto rates higher overall |
| You want to test an engineer before committing | Neither publishes a trial; negotiate a short first term |
| Your budget is at the lower end | Compare: Addepto (Not published) vs Dataroots (Not published) |
| You need engineers deployed inside your organization | Addepto |
| You need specialist depth in a specific vertical | Addepto |
Use case fit: Addepto vs Dataroots
| Use case | Addepto fit | Dataroots fit | Winner |
|---|---|---|---|
| Adding Databricks engineers to a manufacturer's data team | Strong | Limited | Addepto |
| Building a GenAI assistant for automotive service documents | Strong | Strong | Both equally |
| Placing data engineers in a Belgian bank's platform team | Limited | Strong | Dataroots |
| Building an MLOps setup on Azure | Strong | Strong | Both equally |
Verdict: Addepto vs Dataroots
Addepto (3.9/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. AI-heavy team with manufacturing domain experience, now backed by a larger group.
Dataroots (3.8/5) is worth a look if you need building an MLOps setup on Azure. If your situation matches that, Dataroots is a competitive option.
Related comparisons
Addepto vs Dataroots FAQ
Is Addepto better than Dataroots?
Addepto (3.9/5) scores higher overall, but "better" depends on your use case. Addepto's strongest advantage: nearly the whole team is AI engineers, according to its CEO. Dataroots's strongest advantage: strong data platform skills to go with ML work.
How do Addepto and Dataroots differ in pricing?
Addepto uses collaborative team model or managed delivery; rates on request pricing. Dataroots uses consultant day rates; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Dataroots?
Dataroots 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 Addepto and Dataroots?
Addepto's primary differentiator is: AI-heavy team with manufacturing domain experience, now backed by a larger group. Dataroots's primary differentiator is: benelux data platform specialists backed by Talan's wider consulting group. They also differ in team size (50–99 (directory estimate) vs 100+ (at 2022 acquisition)), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Automotive vs Financial services, Public sector).
Verify all details directly with each company before making a decision.