DataRoot Labs vs Modus Create: full comparison for 2026
Last updated: July 2026
Quick verdict
DataRoot Labs (4.6/5) edges ahead of Modus Create (4.0/5) overall. DataRoot Labs is the better choice for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects.. Modus Create is the stronger option for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery.. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs Modus Create: head-to-head summary
| Criterion | DataRoot Labs | Modus Create |
|---|---|---|
| Founded | 2016 | 2011 |
| HQ | Kyiv, Ukraine | Reston, USA |
| Team size | 51–200 | 501–1,000 |
| Rating | 4.6 / 5 | 4.0 / 5 |
| Best for | Startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects. | Distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery. |
| Pricing model | Time & Material, project-based | Not published; project and dedicated team |
| Min. engagement | $10,000+ | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, AWS, Data governance tooling |
| Industries served | E-commerce, Healthcare, Enterprise software, Robotics | Technology/SaaS, Retail, Healthcare |
DataRoot Labs vs Modus Create: overview
DataRoot Labs
DataRoot Labs is a Ukraine-founded machine learning consultancy established in 2016 that has remained AI/ML-only since inception, in contrast to firms that added AI as a service line later. The company offers AI consulting, custom model development and training, solution architecture, and deployment/monitoring, with stated specializations in large language model fine-tuning, computer vision, reinforcement learning, and vector databases. Publicly named clients include OLX, IBM, Databand, and Moxie (Embodied). The company also runs DataRoot University, a training program it states has produced over 6,000 machine learning graduates (per company website; independently unverifiable), which functions as a talent pipeline and community credibility signal.
Modus Create
Modus Create is a fully remote, distributed product engineering company founded in 2011 and headquartered in Reston, Virginia, with team members spread across more than 55 countries. The company's AI/ML and data engineering practice includes AI Strategy Roadmap assessments and AI Data Foundation assessments intended to ensure underlying data is reliable and properly governed before or alongside model development work. Modus Create has partnered with technology providers including Atlassian, GitHub, and AWS, and has been recognized on the Inc. 5000 list for nine consecutive years.
Services and capabilities: DataRoot Labs vs Modus Create
| Capability | DataRoot Labs | Modus Create |
|---|---|---|
| Custom model training | ✓ | ✓ |
| Fine-tuning & adaptation | ✓ | ✗ |
| MLOps pipeline | ✗ | ✗ |
| Model deployment & serving | ✓ | ✗ |
| Data engineering for ML | ✗ | ✓ |
| ML infrastructure management | ✗ | ✗ |
| Computer vision | ✓ | ✗ |
| NLP & LLM development | ✓ | ✗ |
| Forecasting & time-series modeling | ✗ | ✗ |
| ML strategy consulting | ✗ | ✓ |
Tech stack comparison: DataRoot Labs vs Modus Create
| Framework / platform | DataRoot Labs | Modus Create |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Amazon Bedrock | N/A | N/A |
| Google Cloud | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Kubernetes | ✓ | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: DataRoot Labs vs Modus Create
| Criterion | DataRoot Labs | Modus Create |
|---|---|---|
| Minimum engagement | $10,000+ | Not published |
| Engagement models | Time & Material, Fixed project, Dedicated team | Fixed project, Dedicated team, Assessment/audit engagement |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: DataRoot Labs vs Modus Create
| Dimension | DataRoot Labs | Modus Create |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | E-commerce, Healthcare, Enterprise software | Technology/SaaS, Retail, Healthcare |
| Best use cases | Fine-tuning an open-source LLM for a domain-specific internal tool, Building a computer vision model for retail or logistics quality inspection | Running an AI Data Foundation assessment before committing to a full model-development engagement, Building an AI strategy roadmap for an organization new to machine learning adoption |
| Typical project type | Time & Material | Fixed project |
DataRoot Labs vs Modus Create: pros and cons
| DataRoot Labs | |
|---|---|
| + | Clutch rating of 4.9/5 across 23 verified reviews, among the highest in this comparison set. |
| + | Named, checkable clients (OLX, IBM, Databand, Moxie) rather than anonymized case studies only. |
| + | Full IP transfer to clients is cited as standard practice in reviews. |
| + | AI-only focus since 2016 avoids the generalist dilution seen in broader software houses. |
| - | Small team (51–200) constrains capacity for large, multi-team enterprise rollouts. |
| - | Delivery is concentrated in Ukraine, which some risk-averse enterprise buyers may flag for business-continuity planning. |
| - | Public tech-stack disclosure is limited beyond high-level specialization claims. |
| - | Minimum engagement of $10K+ is accessible, but larger programs will need custom scoping not published on the site. |
| Modus Create | |
|---|---|
| + | Structured AI Data Foundation assessment reduces risk of building models on ungoverned or unreliable data. |
| + | Fully remote, globally distributed team (55+ countries) offers broad timezone coverage. |
| + | Nine consecutive years on the Inc. 5000 list signals sustained growth. |
| + | Technology partnerships with Atlassian, GitHub, and AWS support integrated delivery tooling. |
| - | AI/ML is one of several product engineering service lines rather than the company's sole specialization. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Pricing model and minimum engagement are not published. |
| - | Fully remote delivery model may not suit buyers who prefer localized or on-site teams. |
Who should choose DataRoot Labs?
DataRoot Labs is the right choice for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects..
Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University).. Minimum engagement starts at $10,000+. Works best with clients in E-commerce, Healthcare, Enterprise software, Robotics.
Who should choose Modus Create?
Modus Create is the right choice for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery..
Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development.. Minimum engagement starts at Not published. Works best with clients in Technology/SaaS, Retail, Healthcare.
Decision matrix: DataRoot Labs vs Modus Create
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs ($10,000+) vs Modus Create (Not published) |
| You need specialist depth in a specific vertical | DataRoot Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Modus Create |
Use case fit: DataRoot Labs vs Modus Create
| Use case | DataRoot Labs fit | Modus Create fit | Winner |
|---|---|---|---|
| Fine-tuning an open-source LLM for a domain-specific internal tool | Strong | Limited | DataRoot Labs |
| Building a computer vision model for retail or logistics quality inspection | Strong | Strong | Both equally |
| Running an AI Data Foundation assessment before committing to a full model-development engagement | Limited | Strong | Modus Create |
| Building an AI strategy roadmap for an organization new to machine learning adoption | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs Modus Create
DataRoot Labs (4.6/5) is the stronger overall choice for most ML Model Development projects. Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University).. It is best for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects..
Modus Create (4.0/5) is the better choice when distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery.. If your situation matches those criteria, Modus Create is a competitive option.
Related comparisons
DataRoot Labs vs Modus Create FAQ
Is DataRoot Labs better than Modus Create?
DataRoot Labs (4.6/5) scores higher overall, but "better" depends on your use case. DataRoot Labs is better for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects.. Modus Create is better for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery..
How do DataRoot Labs and Modus Create differ in pricing?
DataRoot Labs uses time & material, project-based pricing with a minimum engagement of $10,000+. Modus Create uses not published; project and dedicated team pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataRoot Labs or Modus Create?
Modus Create 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 DataRoot Labs and Modus Create?
DataRoot Labs's primary differentiator is: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).. Modus Create's primary differentiator is: structured ai data foundation assessment methodology that explicitly evaluates data readiness before committing to model development.. They also differ in team size (51–200 vs 501–1,000), minimum engagement ($10,000+ vs Not published), and primary industries served (E-commerce, Healthcare vs Technology/SaaS, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.