InData Labs vs Dataroots: full comparison for 2026
Quick verdict
InData Labs (4.2/5) edges ahead of Dataroots (3.8/5) overall. InData Labs is the better choice for buyers who want an R&D-minded data science team without paying Western European rates. 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.
InData Labs vs Dataroots: head-to-head summary
| Criterion | InData Labs | Dataroots |
|---|---|---|
| Founded | 2014 | 2016 |
| HQ | Nicosia, Cyprus | Leuven, Belgium |
| Team size | 50–99 (directory estimates range up to 201–500) | 100+ (at 2022 acquisition) |
| Rating | 4.2 / 5 | 3.8 / 5 |
| Primary differentiator | Research-led data science with a dedicated-team option | Benelux data platform specialists backed by Talan's wider consulting group |
| Pricing model | Dedicated team or project pricing; Clutch shows projects from under $50,000 to over $100,000; rates on request | Consultant day rates; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, dbt, Databricks |
| Industries served | Healthcare, Fintech, Retail, Media | Financial services, Public sector, Retail, Energy |
InData Labs vs Dataroots: overview
InData Labs
Since 2014, InData Labs has done nothing but data science and AI, and it says it has completed more than 150 projects across healthcare, fintech and retail. The company is registered in Nicosia, Cyprus, with a second office in Singapore and delivery staff in Lithuania and Poland. Dedicated teams and staff augmentation appear in its service list next to generative AI, predictive analytics and computer vision, though the firm publishes little about how those engagements are structured. Clutch reviewers praise value for money and flexibility.
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: InData Labs vs Dataroots
| Capability | InData Labs | 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: InData Labs vs Dataroots
| Framework / platform | InData Labs | Dataroots |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | ✓ |
Pricing comparison: InData Labs vs Dataroots
| Criterion | InData Labs | Dataroots |
|---|---|---|
| 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: InData Labs vs Dataroots
| Dimension | InData Labs | Dataroots |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Fintech, Retail | Financial services, Public sector, Retail |
| Best use cases | Staffing a computer-vision R&D effort for a health-tech product, Adding NLP engineers to a fintech document workflow | Placing data engineers in a Belgian bank's platform team, Building an MLOps setup on Azure |
| Typical project type | Dedicated engineers | Dedicated engineers |
InData Labs vs Dataroots: pros and cons
| InData Labs | |
|---|---|
| + | 150+ completed AI projects (per company website; independently unverifiable) |
| + | Computer vision and NLP are long-standing specialties |
| + | Clutch reviewers mention flexibility when scope changes |
| - | Very little public detail on augmentation terms, team size or billing |
| - | Headcount estimates vary from about 50 to 500, so bench depth is unclear |
| - | One reviewer asked for better-prepared planning sessions |
| 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 InData Labs?
A typical fit: staffing a computer-vision R&D effort for a health-tech product.
Research-led data science with a dedicated-team option. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Fintech, Retail, Media.
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: InData Labs 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; InData Labs 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: InData Labs (Not published) vs Dataroots (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 | InData Labs |
Use case fit: InData Labs vs Dataroots
| Use case | InData Labs fit | Dataroots fit | Winner |
|---|---|---|---|
| Staffing a computer-vision R&D effort for a health-tech product | Strong | Limited | InData Labs |
| Adding NLP engineers to a fintech document workflow | Strong | Limited | InData Labs |
| Placing data engineers in a Belgian bank's platform team | Limited | Strong | Dataroots |
| Building an MLOps setup on Azure | Limited | Strong | Dataroots |
Verdict: InData Labs vs Dataroots
InData Labs (4.2/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Research-led data science with a dedicated-team option.
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.
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InData Labs vs Dataroots FAQ
Is InData Labs better than Dataroots?
InData Labs (4.2/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: 150+ completed AI projects (per company website; independently unverifiable). Dataroots's strongest advantage: strong data platform skills to go with ML work.
How do InData Labs and Dataroots differ in pricing?
InData Labs uses dedicated team or project pricing; clutch shows projects from under $50,000 to over $100,000; 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: InData Labs or Dataroots?
InData 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 company before shortlisting.
What are the main differences between InData Labs and Dataroots?
InData Labs's primary differentiator is: research-led data science with a dedicated-team option. 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 estimates range up to 201–500) vs 100+ (at 2022 acquisition)), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Fintech vs Financial services, Public sector).
Verify all details directly with each company before making a decision.