DataRobot
Enterprise AI platform company founded in 2012, offering professional services alongside its automated ML software.
Founded 2012 | Boston, USA | 501–1,000 employees
mlops-pipelinemodel-deploymentml-infrastructurecustom-model-training
What is DataRobot?
DataRobot was founded in 2012 by Jeremy Achin and Tom De Godoy and is headquartered in Boston, Massachusetts, with roughly 869 employees spread across six continents. The company's core product is an enterprise AI platform that automates building, deploying, and managing machine learning models, and it maintains a professional services function that supports clients through implementation, custom model development support, and platform adoption. Unlike the pure client-services firms in this comparison, DataRobot is fundamentally a software vendor whose services arm exists to support platform-based model development rather than fully bespoke, platform-independent model builds.
DataRobot was founded in 2012 and is headquartered in Boston, USA. The firm employs 501–1,000 people and works primarily with clients in Financial services, Healthcare, Insurance, Public sector sectors. Its primary differentiator is: The only platform-first vendor in this comparison, meaning model development work happens on and around DataRobot's own automated ML software rather than being platform-agnostic.
DataRobot tech stack and services
DataRobot AI Platform (proprietary)AutoML toolingCloud deployment (AWS/Azure/GCP)
| Service area |
|---|
| MLOps Pipeline |
| Model Deployment & Serving |
| ML Infrastructure Management |
| Custom Model Training |
DataRobot use cases
Short answer: DataRobot is best suited for enterprises standardizing on one automated ML platform.
| Use case |
|---|
| Standardizing enterprise ML model development on a single automated platform with vendor support |
| Accelerating time-to-deployment for common predictive modeling use cases |
| Getting implementation and adoption support when rolling out AutoML capability internally |
| Governing and monitoring many models centrally across a large enterprise using one platform |
DataRobot pricing
Short answer: DataRobot uses a platform licensing plus professional services; not fully published pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Platform subscription | Variable; depends on team size | Large programmes or team augmentation |
| Professional services (implementation support) | Variable; depends on team size | Large programmes or team augmentation |
DataRobot does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
DataRobot pros and cons
| Advantages | Things to consider |
|---|---|
| +Automated ML platform can significantly speed up model development and deployment cycles for standard use cases. | -Model development is tied to DataRobot's own platform, limiting flexibility for clients wanting a fully platform-agnostic, bespoke build. |
| +Professional services team supports clients directly through platform adoption rather than leaving them to self-serve. | -As a software vendor first, professional services depth is generally narrower than dedicated consultancies in this list. |
| +Global presence across six continents with a workforce spanning sales, engineering, and customer success. | -No clearly located aggregate Clutch/G2 star rating specific to its services arm in available public sources. |
| +Over a decade of focused operation as an enterprise AI/ML platform company. | -Pricing is a mix of platform licensing and services, making total cost of ownership less transparent than pure T&M consultancies. |
DataRobot vs alternatives
How DataRobot compares to the other top ML Model Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market fintech and SaaS, hybrid forecasting models. | Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. | 4.8 | Full comparison |
| Neurons Lab | Financial services, engineering-led production AI. | End-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services. | 4.6 | Full comparison |
| DataRoot Labs | Startups needing a senior AI-only team, LLM/CV projects. | Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University). | 4.6 | Full comparison |
| Miquido | Companies wanting ML/CV bundled with mobile/web engineering. | Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor. | 4.6 | Full comparison |
| Provectus | Mid-market companies, cloud data plus ML in one. | Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves. | 4.5 | Full comparison |
| Neoteric | Organizations wanting a structured AI feasibility phase. | Two-decade operating history combined with a formal upfront feasibility-assessment stage before any model-building work begins. | 4.5 | Full comparison |
| Addepto | Cost-conscious teams, MLOps consulting or advisory-only. | Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option. | 4.4 | Full comparison |
| N-iX | Enterprise buyers, certified data-platform plus ML partner. | Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice. | 4.4 | Full comparison |
| InData Labs | Companies needing predictive-analytics or CV, documented accuracy. | Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. | 4.3 | Full comparison |
| MobiDev | Small/mid-size companies, dedicated ML consulting arm. | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Research-oriented boutique, NLP, DSP, computer vision. | R&D-first culture with named specializations in digital signal processing and NLP that are less commonly offered as distinct practice areas by peers. | 4.2 | Full comparison |
| Sigmoid | Enterprises with data-infrastructure bottlenecks ahead of ML. | Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse. | 4.2 | Full comparison |
| Tredence | Enterprises, vertical analytics for supply chain at scale. | Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting. | 4.2 | Full comparison |
| Quantiphi | AWS-standardized enterprises, deepest AWS AI/ML credentials. | Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. | 4.2 | Full comparison |
| Sigma Software Group | Companies wanting a Snowflake-certified data platform plus ML. | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting enterprise clients, dedicated AI Lab. | Unusually strong roster of large, publicly named enterprise clients (EY, Qualcomm, London Stock Exchange) for a company of its relatively modest team size. | 4.1 | Full comparison |
| ELEKS | Enterprises, established European engineering plus data science. | One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. | 4.1 | Full comparison |
| Fractal Analytics | Large enterprises, scaled AI partner with a research... | Maintains a dedicated internal foundational AI research team alongside client delivery work, and is now a publicly listed company (NSE/BSE) rather than privately held like most peers of similar size. | 4.1 | Full comparison |
| Xebia | Enterprises, craftsmanship-rooted production AI consultancy. | Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone. | 4.0 | Full comparison |
| Grid Dynamics | Fortune 1000 companies, publicly-traded ML partner. | The only publicly traded company (NASDAQ: GDYN) in this comparison among the mid-to-large tier, giving buyers audited financial transparency unavailable from private peers. | 4.0 | Full comparison |
| Iterate.ai | Regulated enterprises, fully private-infrastructure AI deployment. | Purpose-built for on-premise/private-infrastructure AI deployment, so client data and proprietary code never leave the client's own environment. | 4.0 | Full comparison |
| Modus Create | Distributed orgs, remote-first data plus AI/ML delivery. | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 4.0 | Full comparison |
| Aptus Data Labs | Boutique India-based data engineering, AWS AI depth. | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises, edge CV and asset-monitoring ML at scale. | Only company in this list simultaneously holding AWS Premier, Google Cloud AI/ML Specialization, and NVIDIA Elite Consulting Partner status, reflecting particular strength in edge and GPU-accelerated computer vision. | 4.0 | Full comparison |
| Persistent Systems | Mid-market/enterprise buyers, MLOps plus explainable-AI accelerators. | Purpose-built DxH accelerator suite for MLOps and bias detection, plus a specific Everest Group Leader ranking in the mid-market Data & AI segment rather than only the largest enterprise tier. | 3.9 | Full comparison |
| EPAM Systems | Very large enterprises, proprietary AI orchestration platform. | Proprietary EPAM DIAL platform for enterprise AI orchestration, combined with the 2025 AWS Global Innovation Partner of the Year distinction, an award-level differentiator not held by most peers. | 3.9 | Full comparison |
| Globant | Large enterprises, pre-packaged industry "AI Pods". | Only company in this list organized around a formal "studio + AI Pods" delivery model, and the only one with an IDC MarketScape Worldwide Leader in AI Services designation. | 3.9 | Full comparison |
| LTIMindtree | BFSI and tech enterprises, dedicated ModelOps and governance. | Explicit ModelOps templates and model-governance/responsible-AI tooling as named, productized capabilities rather than only bespoke consulting delivery, backed by an IBM watsonx Center of Excellence. | 3.9 | Full comparison |
| Cognizant | Healthcare-focused enterprises, large AI/analytics consulting bench. | Dedicated, named MLOps platform specifically built for healthcare, combined with one of the largest disclosed data/AI consultant headcounts (23,000+) in this comparison. | 3.9 | Full comparison |
| HCLTech | Very large enterprises, full-stack AI from chip to... | Unusually broad "chip-to-cloud" AI stack claim backed by two named proprietary platforms (Graviton for ML development, AION for AI lifecycle management), a combination not matched by most peers in this list. | 3.9 | Full comparison |
| Infosys | Global enterprises, large library of reusable AI assets. | Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds. | 3.9 | Full comparison |
| Accenture | Largest global enterprises, AI within multi-year transformation. | By far the largest scale of any company in this comparison (approximately 779,000 employees, $69.67B FY2025 revenue), trading breadth and compliance maturity for less niche, hands-on model-engineering depth than boutique specialists. | 3.9 | Full comparison |
| Devbridge (a Cognizant company) | Clients wanting Devbridge's model within Cognizant's structure. | The clearest ownership-change disclosure in this comparison: a formerly independent boutique now operating explicitly as a Cognizant subsidiary, combining boutique delivery heritage with large-parent-company backing. | 3.8 | Full comparison |
DataRobot FAQ
What is DataRobot?
DataRobot was founded in 2012 by Jeremy Achin and Tom De Godoy and is headquartered in Boston, Massachusetts, with roughly 869 employees spread across six continents. The company's core product is an enterprise AI platform that automates building, deploying, and managing machine learning models, and it maintains a professional services function that supports clients through implementation, custom model development support, and platform adoption. Unlike the pure client-services firms in this comparison, DataRobot is fundamentally a software vendor whose services arm exists to support platform-based model development rather than fully bespoke, platform-independent model builds.
How much does DataRobot charge?
DataRobot uses platform licensing plus professional services; not fully published pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does DataRobot use?
DataRobot works with DataRobot AI Platform (proprietary), AutoML tooling, Cloud deployment (AWS/Azure/GCP). Primary industries served include Financial services, Healthcare, Insurance, Public sector.
Is DataRobot right for enterprise?
Enterprises standardizing on one automated ML platform. 501–1,000 team size. Key consideration: Model development is tied to DataRobot's own platform, limiting flexibility for clients wanting a fully platform-agnostic, bespoke build.
What are the best DataRobot alternatives?
The best alternatives to DataRobot depend on your use case. Top options are:
- Tensorway: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
- Neurons Lab: end-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services.
- DataRoot Labs: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).
Compare DataRobot with other ML Model Development companies
DataRobot vs Tensorway DataRobot vs Neurons Lab DataRobot vs DataRoot Labs DataRobot vs Miquido DataRobot vs Provectus DataRobot vs Neoteric DataRobot vs Addepto DataRobot vs N-iX DataRobot vs InData Labs DataRobot vs MobiDev DataRobot vs Sciforce DataRobot vs Sigmoid DataRobot vs Tredence DataRobot vs Quantiphi DataRobot vs Sigma Software Group DataRobot vs Intellectsoft DataRobot vs ELEKS DataRobot vs Fractal Analytics DataRobot vs Xebia DataRobot vs Grid Dynamics DataRobot vs Iterate.ai DataRobot vs Modus Create DataRobot vs Aptus Data Labs DataRobot vs SoftServe DataRobot vs Persistent Systems DataRobot vs EPAM Systems DataRobot vs Globant DataRobot vs LTIMindtree DataRobot vs Cognizant DataRobot vs HCLTech DataRobot vs Infosys DataRobot vs Accenture DataRobot vs Devbridge (a Cognizant company)