Fusemachines vs Dataroots: full comparison for 2026
Quick verdict
Fusemachines (4.3/5) edges ahead of Dataroots (3.8/5) overall. Fusemachines is the better choice for mid-market and enterprise buyers who want AI engineers plus a product platform from one vendor. 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.
Fusemachines vs Dataroots: head-to-head summary
| Criterion | Fusemachines | Dataroots |
|---|---|---|
| Founded | 2013 | 2016 |
| HQ | New York, New York, USA | Leuven, Belgium |
| Team size | Not confirmed in sources reviewed (Nasdaq filer; see SEC reports) | 100+ (at 2022 acquisition) |
| Rating | 4.3 / 5 | 3.8 / 5 |
| Primary differentiator | Its own AI education program feeds the engineering bench | Benelux data platform specialists backed by Talan's wider consulting group |
| Pricing model | Squad or per-engineer billing for services; product licences priced separately; 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 | Financial services, Media, Retail, Healthcare | Financial services, Public sector, Retail, Energy |
Fusemachines vs Dataroots: overview
Fusemachines
Fusemachines was founded in New York in 2013 by Sameer Maskey, a Columbia adjunct professor, around a simple idea: train AI engineers in places big tech ignores, then put them to work for enterprise clients. Its AI Fellowship program has trained engineers in Nepal, the Dominican Republic and Rwanda. The company went public on the Nasdaq Global Market (ticker FUSE) on October 23, 2025, through a merger with the SPAC CSLM Acquisition Corp. Today it sells its own AI Studio and agent products alongside forward-deployed engineers and small squads of data and ML specialists who work inside client organizations.
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: Fusemachines vs Dataroots
| Capability | Fusemachines | 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: Fusemachines vs Dataroots
| Framework / platform | Fusemachines | Dataroots |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | 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: Fusemachines vs Dataroots
| Criterion | Fusemachines | Dataroots |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Embedded team, Dedicated engineers, Project delivery | Dedicated engineers, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Fusemachines vs Dataroots
| Dimension | Fusemachines | Dataroots |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Media, Retail | Financial services, Public sector, Retail |
| Best use cases | Placing a data engineering squad inside a mid-market retailer, Customizing agent products for a financial services back office | Placing data engineers in a Belgian bank's platform team, Building an MLOps setup on Azure |
| Typical project type | Embedded team | Dedicated engineers |
Fusemachines vs Dataroots: pros and cons
| Fusemachines | |
|---|---|
| + | Public-company reporting means audited financials, which few staffing vendors offer |
| + | Engineers trained through its own fellowship arrive with a shared baseline |
| + | Forward-deployed engineers can tune the company's own agent products in your environment |
| + | Offshore delivery from Nepal keeps costs below U.S. hiring |
| - | Ownership changed through the October 2025 SPAC listing, and public-market pressure may shift priorities toward its products |
| - | Product sales and staffing share the same engineers, so availability can tighten |
| - | Nepal time zones offer limited overlap with the Americas |
| 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 Fusemachines?
A typical fit: placing a data engineering squad inside a mid-market retailer.
Its own AI education program feeds the engineering bench. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Media, Retail, Healthcare.
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: Fusemachines 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; Fusemachines 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: Fusemachines (Not published) vs Dataroots (Not published) |
| You need engineers deployed inside your organization | Fusemachines |
| You need specialist depth in a specific vertical | Fusemachines |
Use case fit: Fusemachines vs Dataroots
| Use case | Fusemachines fit | Dataroots fit | Winner |
|---|---|---|---|
| Placing a data engineering squad inside a mid-market retailer | Strong | Strong | Both equally |
| Customizing agent products for a financial services back office | Strong | Limited | Fusemachines |
| Placing data engineers in a Belgian bank's platform team | Strong | Strong | Both equally |
| Building an MLOps setup on Azure | Limited | Strong | Dataroots |
Verdict: Fusemachines vs Dataroots
Fusemachines (4.3/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Its own AI education program feeds the engineering bench.
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
Fusemachines vs Dataroots FAQ
Is Fusemachines better than Dataroots?
Fusemachines (4.3/5) scores higher overall, but "better" depends on your use case. Fusemachines's strongest advantage: public-company reporting means audited financials, which few staffing vendors offer. Dataroots's strongest advantage: strong data platform skills to go with ML work.
How do Fusemachines and Dataroots differ in pricing?
Fusemachines uses squad or per-engineer billing for services; product licences priced separately; 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: Fusemachines 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 Fusemachines and Dataroots?
Fusemachines's primary differentiator is: its own AI education program feeds the engineering bench. Dataroots's primary differentiator is: benelux data platform specialists backed by Talan's wider consulting group. They also differ in team size (Not confirmed in sources reviewed (Nasdaq filer; see SEC reports) vs 100+ (at 2022 acquisition)), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Media vs Financial services, Public sector).
Verify all details directly with each company before making a decision.