Globant vs InData Labs: full comparison for 2026
Quick verdict
Globant (4.1/5) edges ahead of InData Labs (4.1/5) overall. Globant is the better choice for enterprises wanting AI capacity on a subscription model. InData Labs is the stronger option for mid-sized companies adding data scientists to product teams. The right choice depends on your project size, budget, and required tech stack.
Globant vs InData Labs: head-to-head summary
| Criterion | Globant | InData Labs |
|---|---|---|
| Founded | 2003 | 2014 |
| HQ | Luxembourg | Nicosia, Cyprus |
| Team size | 28,000+ | 50–249 |
| Rating | 4.1 / 5 | 4.1 / 5 |
| Primary differentiator | Subscription-based AI Pods as an alternative to per-engineer billing | A data-science-only firm small enough that senior staff stay involved |
| Pricing model | AI Pods monthly subscription with token-based capacity; staff augmentation and SOW contracts; rates on request | Time and materials; dedicated engineers; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, OpenAI, Azure ML | Python, PyTorch, TensorFlow |
| Industries served | Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences | Retail & e-commerce, Healthcare & life sciences, Financial services, Media |
Globant vs InData Labs: 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.
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with additional locations including Vilnius and Miami. Most directories put its headcount below 250 people. The company works only on data science and AI, covering predictive analytics, NLP, computer vision and generative AI, and it supplies engineers to client teams as well as delivering projects.
Services and capabilities: Globant vs InData Labs
| Capability | Globant | InData Labs |
|---|---|---|
| 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 InData Labs
| Framework / platform | Globant | InData Labs |
|---|---|---|
| PyTorch | N/A | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Globant vs InData Labs
| Criterion | Globant | InData Labs |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, Managed delivery, Full-time dedicated engineers | Full-time dedicated engineers, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Globant vs InData Labs
| Dimension | Globant | InData Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Media, Financial services, Travel | Retail & e-commerce, Healthcare & life sciences, Financial services |
| Best use cases | Buying a monthly AI engineering pod for a marketing-tech roadmap, Staffing agent development across several brands | Adding a computer-vision engineer to a retail analytics team, Building churn and demand models with in-house analysts |
| Typical project type | Dedicated team | Full-time dedicated engineers |
Globant vs InData Labs: 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 |
| InData Labs | |
|---|---|
| + | Data science and AI are its only line of work |
| + | Experience across vision, language and predictive models |
| + | Clients deal with a small firm where senior staff stay close to the work |
| - | Headcount estimates vary widely, so confirm bench depth for your role |
| - | Limited capacity for large multi-team programs |
| - | Less visible LLM-agent work than some newer specialists |
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 InData Labs?
A typical fit: adding a computer-vision engineer to a retail analytics team.
A data-science-only firm small enough that senior staff stay involved. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare & life sciences, Financial services, Media.
Decision matrix: Globant vs InData Labs
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Globant |
| 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 InData Labs (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 InData Labs
| Use case | Globant fit | InData Labs 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 a computer-vision engineer to a retail analytics team | Limited | Strong | InData Labs |
| Building churn and demand models with in-house analysts | Limited | Strong | InData Labs |
Verdict: Globant vs InData Labs
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.
InData Labs (4.1/5) is worth a look if you need building churn and demand models with in-house analysts. If your situation matches that, InData Labs is a competitive option.
Related comparisons
Globant vs InData Labs FAQ
Is Globant better than InData Labs?
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. InData Labs's strongest advantage: data science and AI are its only line of work.
How do Globant and InData Labs differ in pricing?
Globant uses ai pods monthly subscription with token-based capacity; staff augmentation and sow contracts; rates on request pricing. InData Labs uses time and materials; dedicated engineers; 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 InData Labs?
InData Labs 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 InData Labs?
Globant's primary differentiator is: subscription-based AI Pods as an alternative to per-engineer billing. InData Labs's primary differentiator is: a data-science-only firm small enough that senior staff stay involved. They also differ in team size (28,000+ vs 50–249), minimum engagement (Not published vs Not published), and primary industries served (Media, Financial services vs Retail & e-commerce, Healthcare & life sciences).
Verify all details directly with each company before making a decision.