Globant
Publicly traded (NYSE: GLOB) digital engineering company founded in 2003, recognized as an IDC MarketScape AI Services Leader.
What is Globant?
Globant was founded in 2003 in Buenos Aires by Martin Migoya, Guibert Englebienne, Martin Umaran, and Nestor Nocetti, and is now headquartered in Luxembourg while trading publicly on the NYSE under GLOB. The company reports roughly 29,000 employees and organizes its AI capability around eight industry-specific studios that produce what it calls "AI Pods," tailored solutions for specific industry challenges spanning financial services, life sciences, and airlines among others. Globant was recognized by IDC MarketScape as a Worldwide Leader in AI Services in 2023, and has named client work including LALIGA for agentic AI in sports, presented at NVIDIA GTC 2026.
Globant was founded in 2003 and is headquartered in Luxembourg City, Luxembourg. The firm employs 10,000+ people and works primarily with clients in Financial services, Life sciences, Airlines/travel, Sports and entertainment sectors. Its primary differentiator is: 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..
Globant tech stack and services
| Service area | Details |
|---|---|
| Large enterprises wanting pre-packaged, industry-specific AI solutions delivered quickly via studio teams | Available for Financial services, Life sciences, Airlines/travel, Sports and entertainment clients |
| Sports, entertainment, or media companies exploring agentic AI applications | Available for Financial services, Life sciences, Airlines/travel, Sports and entertainment clients |
| Financial services or life sciences companies wanting an IDC-validated AI services partner | Available for Financial services, Life sciences, Airlines/travel, Sports and entertainment clients |
| Organizations comfortable with a subscription-style engagement model for ongoing AI capability | Available for Financial services, Life sciences, Airlines/travel, Sports and entertainment clients |
Globant use cases
Short answer: Globant is best suited for large enterprises wanting industry-specific pre-packaged AI solutions ("AI Pods") delivered through a studio-based model rather than fully bespoke consulting..
| Use case | Industries | Approach |
|---|---|---|
| Large enterprises wanting pre-packaged, industry-specific AI solutions delivered quickly via studio teams | Financial services, Life sciences | Proprietary Glob.AI OS platform, Computer vision (via Synthesis AI partnership) |
| Sports, entertainment, or media companies exploring agentic AI applications | Financial services, Life sciences | Proprietary Glob.AI OS platform, Computer vision (via Synthesis AI partnership) |
| Financial services or life sciences companies wanting an IDC-validated AI services partner | Financial services, Life sciences | Proprietary Glob.AI OS platform, Computer vision (via Synthesis AI partnership) |
| Organizations comfortable with a subscription-style engagement model for ongoing AI capability | Financial services, Life sciences | Proprietary Glob.AI OS platform, Computer vision (via Synthesis AI partnership) |
Globant pricing
Short answer: Globant uses a not published; moving toward subscription-style pricing for ai pods (per third-party commentary; independently unverifiable in detail) pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Studio-based engagement | Variable; depends on team size | Large programmes or team augmentation |
| Enterprise project engagement | Variable; depends on team size | Large programmes or team augmentation |
| Subscription (AI Pods) | Variable; depends on team size | Large programmes or team augmentation |
Globant pros and cons
| Advantages | Things to consider |
|---|---|
| +IDC MarketScape Worldwide Leader in AI Services (2023), an independently sourced third-party analyst validation. | -Studio/Pod delivery model provides less MLOps/infrastructure-specific documented depth than peers like EPAM or Persistent. |
| +Named, checkable client work (LALIGA agentic AI, presented publicly at NVIDIA GTC 2026). | -No clearly located aggregate Clutch/G2 star rating in available public sources. |
| +Industry-specific studio model can accelerate time-to-value versus fully custom engagements. | -Pricing details, including the reported move to subscription models, are not fully independently verifiable. |
| +Publicly traded (NYSE: GLOB) with substantial scale (29,000+ employees). | -Large scale means individual client attention may vary depending on which studio is engaged. |
Globant vs alternatives
How Globant compares to the other top ML Model Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market fintech, supply chain, and SaaS companies that... | 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 firms wanting a boutique, engineering-led partner... | 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 and mid-market companies wanting a senior, AI-only... | 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 that need ML/computer-vision capability bundled with broader... | 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 that need cloud data infrastructure and... | 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 feasibility/strategy phase before committing... | 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 that specifically need MLOps consulting or... | 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 wanting a large, heavily certified engineering... | 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 a focused predictive-analytics or computer-vision model... | Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. | 4.3 | Full comparison |
| MobiDev | Small and mid-sized companies wanting a dedicated ML/data-science... | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Companies needing a research-oriented boutique for NLP, digital... | 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 whose primary bottleneck is data infrastructure and... | 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 needing vertical-specific analytics and ML applied to... | 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 | Enterprises standardized on AWS wanting a partner with... | 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 large, diversified engineering group with... | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting an enterprise-name client roster and a... | 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 wanting a long-established European software engineering partner... | 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 wanting a scaled analytics and AI... | 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 wanting a large, engineering-craftsmanship-rooted consultancy that has... | 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 wanting the financial transparency and... | 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 | Data-sensitive enterprises (e.g., regulated industries) that require AI... | 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 organizations wanting a remote-first partner that pairs... | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 4.0 | Full comparison |
| Aptus Data Labs | Companies wanting a boutique, India-based data engineering and... | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises needing edge computer vision or asset-monitoring ML... | 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 |
| DataRobot | Enterprises that want to standardize on a single... | 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. | 3.9 | Full comparison |
| Persistent Systems | Mid-market and enterprise buyers wanting a publicly traded,... | 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 wanting a publicly traded, AWS... | 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 |
| LTIMindtree | Large enterprises, particularly in BFSI and technology/media sectors,... | 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 | Large enterprises, especially in healthcare, wanting a very... | 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 wanting a full-stack AI vendor... | 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 | Very large global enterprises wanting a substantial library... | 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 | The largest global enterprises needing AI model development... | 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 who want Devbridge's original product-engineering delivery model... | 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 |
Globant FAQ
What is Globant?
Globant was founded in 2003 in Buenos Aires by Martin Migoya, Guibert Englebienne, Martin Umaran, and Nestor Nocetti, and is now headquartered in Luxembourg while trading publicly on the NYSE under GLOB. The company reports roughly 29,000 employees and organizes its AI capability around eight industry-specific studios that produce what it calls "AI Pods," tailored solutions for specific industry challenges spanning financial services, life sciences, and airlines among others. Globant was recognized by IDC MarketScape as a Worldwide Leader in AI Services in 2023, and has named client work including LALIGA for agentic AI in sports, presented at NVIDIA GTC 2026.
How much does Globant charge?
Globant uses not published; moving toward subscription-style pricing for ai pods (per third-party commentary; independently unverifiable in detail) pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does Globant use?
Globant works with Proprietary Glob.AI OS platform, Computer vision (via Synthesis AI partnership), Cloud ML platforms. Primary industries served include Financial services, Life sciences, Airlines/travel, Sports and entertainment.
Is Globant right for enterprise?
Large enterprises wanting industry-specific pre-packaged AI solutions ("AI Pods") delivered through a studio-based model rather than fully bespoke consulting.. 10,000+ team size. Key consideration: Studio/Pod delivery model provides less MLOps/infrastructure-specific documented depth than peers like EPAM or Persistent..
What are the best Globant alternatives?
The best alternatives to Globant 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 Globant with other ML Model Development companies
Last reviewed: July 2026. Verify all details directly with Globant before making a decision.