Globant vs STX Next: full comparison for 2026
Quick verdict
Globant (4.1/5) edges ahead of STX Next (3.9/5) overall. Globant is the better choice for enterprises wanting AI capacity on a subscription model. STX Next is the stronger option for python codebases adding LLM and data engineers. The right choice depends on your project size, budget, and required tech stack.
Globant vs STX Next: head-to-head summary
| Criterion | Globant | STX Next |
|---|---|---|
| Founded | 2003 | 2005 |
| HQ | Luxembourg | Poznań, Poland |
| Team size | 28,000+ | 250–500 |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | Subscription-based AI Pods as an alternative to per-engineer billing | Python specialization applied to data and AI delivery |
| Pricing model | AI Pods monthly subscription with token-based capacity; staff augmentation and SOW contracts; rates on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, OpenAI, Azure ML | Python, Django, FastAPI |
| Industries served | Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences | Financial services, Software & SaaS, Media, Healthcare & life sciences |
Globant vs STX Next: overview
Globant
Globant was founded in Buenos Aires in 2003 and is now headquartered in Luxembourg. The NYSE-listed company reported 28,773 employees at the end of 2025. In 2025 it launched AI Pods, a monthly subscription for AI-assisted engineering capacity metered by tokens. Third-party reviews say classic staff augmentation runs mainly through Belatrix, a firm Globant acquired, while large accounts usually buy managed pods or statements of work.
STX Next
STX Next was founded in Poznań in March 2005 and built its reputation as one of Europe's largest Python software houses. Its 2025 anniversary release cites about 500 staff and more than 1,000 delivered projects, with delivery centers in Poland and Mexico. The firm now presents itself as a data and AI consultancy, and Python's dominance in ML makes its bench a natural fit for model and data work.
Services and capabilities: Globant vs STX Next
| Capability | Globant | STX Next |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✓ |
| MLOps & deployment | ✗ | ✗ |
| Computer vision | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| AI agent development | ✓ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✓ | ✗ |
Tech stack comparison: Globant vs STX Next
| Framework / platform | Globant | STX Next |
|---|---|---|
| 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 |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Globant vs STX Next
| Criterion | Globant | STX Next |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, Managed delivery, Full-time dedicated engineers | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Globant vs STX Next
| Dimension | Globant | STX Next |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Media, Financial services, Travel | Financial services, Software & SaaS, Media |
| Best use cases | Buying a monthly AI engineering pod for a marketing-tech roadmap, Staffing agent development across several brands | Adding LLM features to a Django product, Building data jobs in Python for analytics |
| Typical project type | Dedicated team | Full-time dedicated engineers |
Globant vs STX Next: pros and cons
| Globant | |
|---|---|
| + | AI Pods give finance teams a predictable monthly cost |
| + | Large Latin American delivery footprint on U.S.-friendly hours |
| + | Public-company governance suits procurement-heavy buyers |
| - | Individual staff augmentation is a side channel run largely through the acquired Belatrix business |
| - | Headcount fell about 8% during 2025, according to Bloomberg Línea |
| - | Pod and token-based pricing is hard to compare with per-engineer quotes |
| STX Next | |
|---|---|
| + | Python depth fits most AI codebases |
| + | Delivery from both Poland and Mexico |
| + | Long history of extending client teams |
| - | AI positioning is recent compared with its Python history |
| - | Polish rates are above Ukrainian and Latin American options |
| - | Fewer specialist roles such as computer vision |
Who should choose Globant?
A typical fit: buying a monthly AI engineering pod for a marketing-tech roadmap.
Subscription-based AI Pods as an alternative to per-engineer billing. Minimum engagement is not publicly disclosed. Works best with clients in Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences.
Who should choose STX Next?
A typical fit: adding LLM features to a Django product.
Python specialization applied to data and AI delivery. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Software & SaaS, Media, Healthcare & life sciences.
Decision matrix: Globant vs STX Next
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; Globant rates higher overall |
| You want the supplier to own delivery as well as staffing | Both; Globant rates higher overall |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: Globant (Not published) vs STX Next (Not published) |
| You need overlap with U.S. working hours | Globant |
| You need specialist depth in a specific vertical | Globant |
Use case fit: Globant vs STX Next
| Use case | Globant fit | STX Next fit | Winner |
|---|---|---|---|
| Buying a monthly AI engineering pod for a marketing-tech roadmap | Strong | Limited | Globant |
| Staffing agent development across several brands | Strong | Limited | Globant |
| Adding LLM features to a Django product | Limited | Strong | STX Next |
| Building data jobs in Python for analytics | Limited | Strong | STX Next |
Verdict: Globant vs STX Next
Globant (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. Subscription-based AI Pods as an alternative to per-engineer billing.
STX Next (3.9/5) is worth a look if you need building data jobs in Python for analytics. If your situation matches that, STX Next is a competitive option.
Related comparisons
Globant vs STX Next FAQ
Is Globant better than STX Next?
Globant (4.1/5) scores higher overall, but "better" depends on your use case. Globant's strongest advantage: AI Pods give finance teams a predictable monthly cost. STX Next's strongest advantage: python depth fits most AI codebases.
How do Globant and STX Next differ in pricing?
Globant uses ai pods monthly subscription with token-based capacity; staff augmentation and sow contracts; rates on request pricing. STX Next uses time and materials; dedicated teams; 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: Globant or STX Next?
STX Next 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 Globant and STX Next?
Globant's primary differentiator is: subscription-based AI Pods as an alternative to per-engineer billing. STX Next's primary differentiator is: python specialization applied to data and AI delivery. They also differ in team size (28,000+ vs 250–500), minimum engagement (Not published vs Not published), and primary industries served (Media, Financial services vs Financial services, Software & SaaS).
Verify all details directly with each company before making a decision.