Vstorm vs Dataroots: full comparison for 2026
Quick verdict
Vstorm (4.0/5) edges ahead of Dataroots (3.8/5) overall. Vstorm is the better choice for teams whose agent prototype works in a demo but fails in production. 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.
Vstorm vs Dataroots: head-to-head summary
| Criterion | Vstorm | Dataroots |
|---|---|---|
| Founded | 2017 | 2016 |
| HQ | Wrocław, Poland | Leuven, Belgium |
| Team size | 40+ (25+ AI engineers per company) | 100+ (at 2022 acquisition) |
| Rating | 4.0 / 5 | 3.8 / 5 |
| Primary differentiator | Senior agent engineers who join an existing team to fix reliability and integration | Benelux data platform specialists backed by Talan's wider consulting group |
| Pricing model | Monthly agentic-engineering retainer or 3+ month embedded package; $100–$149/hr (Clutch band) | Consultant day rates; rates on request |
| Min. engagement | $10,000+ (Clutch) | Not published |
| Primary tech stack | Python, PydanticAI, LangChain | Python, dbt, Databricks |
| Industries served | Fintech & payments, SaaS, Professional services | Financial services, Public sector, Retail, Energy |
Vstorm vs Dataroots: overview
Vstorm
Vstorm has built AI systems since 2017 and now concentrates on LLM agents, with about 25 AI engineers on its bench and 40+ staff in total, mostly in Wrocław and remote across Poland. Its website names three situations it fixes, and one is an existing team that has stalled; there, Vstorm adds senior engineers who specialize in agent design, reliability and integration. Its longest package embeds a manager, a tech lead and engineers for three months or more. Deloitte and EY have both recognized the company, according to directory listings.
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: Vstorm vs Dataroots
| Capability | Vstorm | 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: Vstorm vs Dataroots
| Framework / platform | Vstorm | 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 | N/A | ✓ |
| Google Cloud | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | ✓ |
Pricing comparison: Vstorm vs Dataroots
| Criterion | Vstorm | Dataroots |
|---|---|---|
| Minimum engagement | $10,000+ (Clutch) | Not published |
| Engagement models | Embedded team, Dedicated engineers, Project delivery | Dedicated engineers, Project delivery |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Vstorm vs Dataroots
| Dimension | Vstorm | Dataroots |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech & payments, SaaS, Professional services | Financial services, Public sector, Retail |
| Best use cases | Rescuing an agent rollout that keeps failing in production, Embedding a tech lead and two engineers for a quarter | Placing data engineers in a Belgian bank's platform team, Building an MLOps setup on Azure |
| Typical project type | Embedded team | Dedicated engineers |
Vstorm vs Dataroots: pros and cons
| Vstorm | |
|---|---|
| + | Agent reliability is its main specialty |
| + | Embedded package includes a tech lead, so you get engineering leadership too |
| + | Clutch data shows 45+ clients across 9 countries |
| - | A bench of about 25 engineers limits how many people it can place at once |
| - | Hourly rates are at the upper end for a Polish firm |
| - | Narrow focus on agents; classic ML or computer-vision staffing is a weaker fit |
| 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 Vstorm?
A typical fit: rescuing an agent rollout that keeps failing in production.
Senior agent engineers who join an existing team to fix reliability and integration. Minimum engagement starts at $10,000+ (Clutch). Works best with clients in Fintech & payments, SaaS, Professional services.
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: Vstorm 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; Vstorm 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: Vstorm ($10,000+ (Clutch)) vs Dataroots (Not published) |
| You need engineers deployed inside your organization | Vstorm |
| You need specialist depth in a specific vertical | Dataroots |
Use case fit: Vstorm vs Dataroots
| Use case | Vstorm fit | Dataroots fit | Winner |
|---|---|---|---|
| Rescuing an agent rollout that keeps failing in production | Strong | Limited | Vstorm |
| Embedding a tech lead and two engineers for a quarter | Strong | Limited | Vstorm |
| Placing data engineers in a Belgian bank's platform team | Limited | Strong | Dataroots |
| Building an MLOps setup on Azure | Limited | Strong | Dataroots |
Verdict: Vstorm vs Dataroots
Vstorm (4.0/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Senior agent engineers who join an existing team to fix reliability and integration.
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
Vstorm vs Dataroots FAQ
Is Vstorm better than Dataroots?
Vstorm (4.0/5) scores higher overall, but "better" depends on your use case. Vstorm's strongest advantage: agent reliability is its main specialty. Dataroots's strongest advantage: strong data platform skills to go with ML work.
How do Vstorm and Dataroots differ in pricing?
Vstorm uses monthly agentic-engineering retainer or 3+ month embedded package; $100–$149/hr (clutch band) pricing with a minimum engagement of $10,000+ (Clutch). 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: Vstorm 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 Vstorm and Dataroots?
Vstorm's primary differentiator is: senior agent engineers who join an existing team to fix reliability and integration. Dataroots's primary differentiator is: benelux data platform specialists backed by Talan's wider consulting group. They also differ in team size (40+ (25+ AI engineers per company) vs 100+ (at 2022 acquisition)), minimum engagement ($10,000+ (Clutch) vs Not published), and primary industries served (Fintech & payments, SaaS vs Financial services, Public sector).
Verify all details directly with each company before making a decision.