Tensorway vs Dataroots: full comparison for 2026
Quick verdict
Tensorway (4.5/5) edges ahead of Dataroots (3.8/5) overall. Tensorway is the better choice for product teams that want senior AI engineers inside their own workflow and want the know-how to stay. 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.
Tensorway vs Dataroots: head-to-head summary
| Criterion | Tensorway | Dataroots |
|---|---|---|
| Founded | 2019 | 2016 |
| HQ | Alicante, Spain | Leuven, Belgium |
| Team size | 50–249 | 100+ (at 2022 acquisition) |
| Rating | 4.5 / 5 | 3.8 / 5 |
| Primary differentiator | Senior AI engineers run the screening, and knowledge transfer to in-house staff is part of every engagement | Benelux data platform specialists backed by Talan's wider consulting group |
| Pricing model | Monthly rate for full-time dedicated engineers; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card on request | Consultant day rates; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, dbt, Databricks |
| Industries served | Financial services, SaaS, Logistics, Healthcare, Retail & e-commerce, Manufacturing | Financial services, Public sector, Retail, Energy |
Tensorway vs Dataroots: overview
Tensorway
Tensorway was set up in Alicante, Spain in 2019 to do one thing: AI engineering. Its delivery practice draws on more than two decades of software engineering. Its staff-augmentation service supplies ML engineers, AI agent developers, data engineers and other specialists who work inside the client's own Slack, Jira and repositories. Most engagements start as a squad of two to five people and change shape as the work moves from research to production, with a part-time fractional expert as an option when a full seat is too much. The company's case studies include a multi-billion-euro Swedish private equity fund, where an AI-agent system reportedly cut deal-sourcing time by 80% and screens more than 5,000 opportunities in hours (per company website; independently unverifiable).
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: Tensorway vs Dataroots
| Capability | Tensorway | 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: Tensorway vs Dataroots
| Framework / platform | Tensorway | Dataroots |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | ✓ |
Pricing comparison: Tensorway vs Dataroots
| Criterion | Tensorway | Dataroots |
|---|---|---|
| Minimum engagement | Not disclosed | Not published |
| Engagement models | Dedicated engineers, Fractional experts, Trial sprint | Dedicated engineers, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Tensorway vs Dataroots
| Dimension | Tensorway | Dataroots |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, SaaS, Logistics | Financial services, Public sector, Retail |
| Best use cases | Building an AI-agent system for deal sourcing at an investment firm, Adding a fractional MLOps expert to cut inference costs | Placing data engineers in a Belgian bank's platform team, Building an MLOps setup on Azure |
| Typical project type | Dedicated engineers | Dedicated engineers |
Tensorway vs Dataroots: pros and cons
| Tensorway | |
|---|---|
| + | Candidates pass a code review, a practical task in their specialty and a communication check, all run by senior AI engineers |
| + | A two-week trial sprint lets you judge real output before the monthly commitment starts |
| + | Fractional experts cover narrow needs, such as a few days a week of fine-tuning or GPU cost work |
| + | Code, documentation and trained models stay in your repositories, and handover to in-house staff is planned from the start |
| + | Shortlist in days and first engineer in one to two weeks (per company website; independently unverifiable) |
| - | No published rates, so budgeting needs a call |
| - | The bench is far smaller than Quantiphi's, so a request for ten engineers at once would stretch it |
| - | Time-zone overlap is agreed per engagement; there is no fixed nearshore promise |
| - | Staffs AI and ML roles only, so general web or mobile developers have to come from elsewhere |
| 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 Tensorway?
A typical fit: building an AI-agent system for deal sourcing at an investment firm.
Senior AI engineers run the screening, and knowledge transfer to in-house staff is part of every engagement. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, SaaS, Logistics, Healthcare, Retail & e-commerce, Manufacturing.
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: Tensorway vs Dataroots
| Your situation | Recommended choice |
|---|---|
| You need one AI specialist part-time | Tensorway |
| You need several engineers working as one team | Both; Tensorway rates higher overall |
| You want to test an engineer before committing | Tensorway |
| Your budget is at the lower end | Compare: Tensorway (Not disclosed) 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 | Tensorway |
Use case fit: Tensorway vs Dataroots
| Use case | Tensorway fit | Dataroots fit | Winner |
|---|---|---|---|
| Building an AI-agent system for deal sourcing at an investment firm | Strong | Strong | Both equally |
| Adding a fractional MLOps expert to cut inference costs | Strong | Limited | Tensorway |
| Placing data engineers in a Belgian bank's platform team | Limited | Strong | Dataroots |
| Building an MLOps setup on Azure | Strong | Strong | Both equally |
Verdict: Tensorway vs Dataroots
Tensorway (4.5/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Senior AI engineers run the screening, and knowledge transfer to in-house staff is part of every engagement.
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
Tensorway vs Dataroots FAQ
Is Tensorway better than Dataroots?
Tensorway (4.5/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: candidates pass a code review, a practical task in their specialty and a communication check, all run by senior AI engineers. Dataroots's strongest advantage: strong data platform skills to go with ML work.
How do Tensorway and Dataroots differ in pricing?
Tensorway uses monthly rate for full-time dedicated engineers; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card 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: Tensorway 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 Tensorway and Dataroots?
Tensorway's primary differentiator is: senior AI engineers run the screening, and knowledge transfer to in-house staff is part of every engagement. Dataroots's primary differentiator is: benelux data platform specialists backed by Talan's wider consulting group. They also differ in team size (50–249 vs 100+ (at 2022 acquisition)), minimum engagement (Not disclosed vs Not published), and primary industries served (Financial services, SaaS vs Financial services, Public sector).
Verify all details directly with each company before making a decision.