Dataroots vs Dataforest: full comparison for 2026
Quick verdict
Dataroots (3.8/5) edges ahead of Dataforest (3.7/5) overall. Dataroots is the better choice for benelux enterprises that need ML and data engineers inside their own teams. Dataforest is the stronger option for companies that need data engineers who can also build AI features on top. The right choice depends on your project size, budget, and required tech stack.
Dataroots vs Dataforest: head-to-head summary
| Criterion | Dataroots | Dataforest |
|---|---|---|
| Founded | 2016 | 2018 |
| HQ | Leuven, Belgium | Kyiv, Ukraine |
| Team size | 100+ (at 2022 acquisition) | 50–249 (directory estimate) |
| Rating | 3.8 / 5 | 3.7 / 5 |
| Primary differentiator | Benelux data platform specialists backed by Talan's wider consulting group | Data engineering depth with AI agent work on top |
| Pricing model | Consultant day rates; rates on request | Project or dedicated-team pricing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, dbt, Databricks | Python, Spark, Airflow |
| Industries served | Financial services, Public sector, Retail, Energy | Telecom, E-commerce, Software & SaaS, Real estate |
Dataroots vs Dataforest: overview
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.
Dataforest
Dataforest is a Kyiv data engineering company, founded in 2018 according to directory data, that also builds AI agents and support automation. It works either by project or by assigning a dedicated team, and directory listings include team augmentation among its engagement models. One Clutch reviewer said the firm felt like a dedicated technical team extension. Uvik's 2026 roundup groups it with InData Labs as a data engineering vendor with strong AI overlap.
Services and capabilities: Dataroots vs Dataforest
| Capability | Dataroots | Dataforest |
|---|---|---|
| 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: Dataroots vs Dataforest
| Framework / platform | Dataroots | Dataforest |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | N/A | ✓ |
| Databricks | ✓ | N/A |
| MLflow | ✓ | N/A |
Pricing comparison: Dataroots vs Dataforest
| Criterion | Dataroots | Dataforest |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineers, Project delivery | Dedicated engineers, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Dataroots vs Dataforest
| Dimension | Dataroots | Dataforest |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Public sector, Retail | Telecom, E-commerce, Software & SaaS |
| Best use cases | Placing data engineers in a Belgian bank's platform team, Building an MLOps setup on Azure | Building an AI support assistant for a telecom provider, Adding data engineers to clean and enrich product data |
| Typical project type | Dedicated engineers | Dedicated engineers |
Dataroots vs Dataforest: pros and cons
| 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 |
| Dataforest | |
|---|---|
| + | Clients describe it as working like part of their own team |
| + | Combines data engineering with AI agent development |
| + | Ukrainian rates |
| - | Founding year and size come from a single directory |
| - | Web product work makes it less AI-pure than others here |
| - | Ukrainian operations carry wartime risk |
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.
Who should choose Dataforest?
A typical fit: building an AI support assistant for a telecom provider.
Data engineering depth with AI agent work on top. Minimum engagement is not publicly disclosed. Works best with clients in Telecom, E-commerce, Software & SaaS, Real estate.
Decision matrix: Dataroots vs Dataforest
| 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; Dataroots 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: Dataroots (Not published) vs Dataforest (Not published) |
| You need engineers deployed inside your organization | Both place engineers on request; confirm on-site terms |
| You need specialist depth in a specific vertical | Dataroots |
Use case fit: Dataroots vs Dataforest
| Use case | Dataroots fit | Dataforest fit | Winner |
|---|---|---|---|
| Placing data engineers in a Belgian bank's platform team | Strong | Limited | Dataroots |
| Building an MLOps setup on Azure | Strong | Strong | Both equally |
| Building an AI support assistant for a telecom provider | Strong | Strong | Both equally |
| Adding data engineers to clean and enrich product data | Limited | Strong | Dataforest |
Verdict: Dataroots vs Dataforest
Dataroots (3.8/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Benelux data platform specialists backed by Talan's wider consulting group.
Dataforest (3.7/5) is worth a look if you need adding data engineers to clean and enrich product data. If your situation matches that, Dataforest is a competitive option.
Related comparisons
Dataroots vs Dataforest FAQ
Is Dataroots better than Dataforest?
Dataroots (3.8/5) scores higher overall, but "better" depends on your use case. Dataroots's strongest advantage: strong data platform skills to go with ML work. Dataforest's strongest advantage: clients describe it as working like part of their own team.
How do Dataroots and Dataforest differ in pricing?
Dataroots uses consultant day rates; rates on request pricing. Dataforest uses project or dedicated-team pricing; 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: Dataroots or Dataforest?
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 Dataroots and Dataforest?
Dataroots's primary differentiator is: benelux data platform specialists backed by Talan's wider consulting group. Dataforest's primary differentiator is: data engineering depth with AI agent work on top. They also differ in team size (100+ (at 2022 acquisition) vs 50–249 (directory estimate)), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Public sector vs Telecom, E-commerce).
Verify all details directly with each company before making a decision.