Globant vs InData Labs: full comparison for 2026
Quick verdict
Globant (4.0/5) edges ahead of InData Labs (3.9/5) overall. Globant is the better choice for US firms wanting nearshore AI engineering. InData Labs is the stronger option for mid-size firms with one focused AI project. The right choice depends on how far your pilot has got, your budget, and who will run the system after launch.
Globant vs InData Labs: head-to-head summary
| Criterion | Globant | InData Labs |
|---|---|---|
| Founded | 2003 | 2014 |
| HQ | Luxembourg (operations: Buenos Aires) | Nicosia, Cyprus |
| Team size | 28,500+ | 80+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Nearshore Latin American engineering teams in US time zones | Discovery, proof of concept, and production build from a small data science team |
| Pricing model | Time & materials and dedicated teams; rates not published | Project-based; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Google Cloud, AWS, Azure | AWS, Azure, Python |
| Industries served | Media, Retail, Financial services, Travel, Healthcare | Retail, Healthcare, Fintech, Logistics, Media |
Globant vs InData Labs: overview
Globant
Globant was founded in 2003 in Buenos Aires, is legally domiciled in Luxembourg, and employs more than 28,500 people. It is listed on the New York Stock Exchange. Its AI work runs through studios that combine design and engineering, with nearshore delivery that keeps time zones close for US clients. Production AI usually comes inside a wider digital product program.
InData Labs
InData Labs was founded in 2014, is headquartered in Nicosia, Cyprus, and has around 80 engineers and data scientists. It builds machine learning, computer vision, and language model systems for mid-size companies, usually starting with a discovery phase and a proof of concept. Its size suits one focused production project better than a long program.
Services and capabilities: Globant vs InData Labs
| Capability | Globant | InData Labs |
|---|---|---|
| LLM gateway & model routing | ✗ | ✗ |
| Regression tests on prompt and model changes | ✗ | ✗ |
| Canary or staged rollout | ✗ | ✗ |
| Hallucination and drift monitoring | ✗ | ✗ |
| MLOps & model serving | ✗ | ✓ |
| Fixed-scope pilot | ✗ | ✓ |
| Managed run after launch | ✗ | ✗ |
Tech stack comparison: Globant vs InData Labs
| Framework / platform | Globant | InData Labs |
|---|---|---|
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | N/A | N/A |
| MLflow | N/A | N/A |
| Databricks | ✓ | N/A |
| Amazon Bedrock | N/A | N/A |
| Vertex AI | N/A | N/A |
| LangChain | N/A | N/A |
| Snowflake | N/A | N/A |
Pricing comparison: Globant vs InData Labs
| Criterion | Globant | InData Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Time & materials, Dedicated team | Fixed-scope pilot, Fixed-scope project, Dedicated team |
| 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 | Enterprise | Startup to mid-market |
| Best industries | Media, Retail, Financial services | Retail, Healthcare, Fintech |
| Best use cases | AI features inside consumer digital products., Nearshore AI engineering teams for US companies. | A single computer vision system taken to production., Proof of concept followed by a production build. |
| Typical project type | Time & materials | Fixed-scope pilot |
Globant vs InData Labs: pros and cons
| Globant | |
|---|---|
| + | Nearshore teams share working hours with US clients |
| + | Large design and engineering capacity |
| + | Public company with regular disclosures |
| - | AI is one part of a broad digital business |
| - | Large-firm process for small pilots |
| - | Rates not published |
| InData Labs | |
|---|---|
| + | Clear path from proof of concept to production |
| + | Senior data scientists on small projects |
| + | Computer vision and language model experience |
| - | Small team limits large or parallel programs |
| - | No named LLMOps tooling |
| - | No managed-run service listed |
Who should choose Globant?
A typical fit: AI features inside consumer digital products.
Nearshore Latin American engineering teams in US time zones. Minimum engagement is not publicly disclosed. Works best with clients in Media, Retail, Financial services, Travel, Healthcare.
Who should choose InData Labs?
A typical fit: a single computer vision system taken to production.
Discovery, proof of concept, and production build from a small data science team. Minimum engagement is not publicly disclosed. Works best with clients in Retail, Healthcare, Fintech, Logistics, Media.
Decision matrix: Globant vs InData Labs
| Your situation | Recommended choice |
|---|---|
| Your pilot works but nobody trusts it in production yet | Ask each for its release and rollback process |
| Model API spend is growing faster than usage | Neither lists a gateway; ask how cost per feature is tracked |
| You need someone to watch quality after launch | Neither; plan for your own team to monitor it |
| You run classic ML models that need serving and retraining | InData Labs |
| You want a priced pilot before a full rollout | InData Labs |
| Your budget is at the lower end | Compare: Globant (Not disclosed) vs InData Labs (Not disclosed) |
| You need a large team for many products at once | Globant |
Use case fit: Globant vs InData Labs
| Use case | Globant fit | InData Labs fit | Winner |
|---|---|---|---|
| AI features inside consumer digital products. | Strong | Limited | Globant |
| Nearshore AI engineering teams for US companies. | Strong | Limited | Globant |
| A single computer vision system taken to production. | Limited | Strong | InData Labs |
| Proof of concept followed by a production build. | Limited | Strong | InData Labs |
Verdict: Globant vs InData Labs
Globant (4.0/5) is the stronger overall choice for most AI Implementation projects. Nearshore Latin American engineering teams in US time zones.
InData Labs (3.9/5) is worth a look if you need proof of concept followed by a production build. 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 (4.0/5) scores higher overall, but "better" depends on your use case. Globant's strongest advantage: nearshore teams share working hours with US clients. InData Labs's strongest advantage: clear path from proof of concept to production.
How do Globant and InData Labs differ in pricing?
Globant's pricing: time & materials and dedicated teams; rates not published. InData Labs's pricing: project-based; rates on request. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.
Which is better for enterprise: Globant or InData Labs?
Globant is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each provider before shortlisting.
What are the main differences between Globant and InData Labs?
Globant's primary differentiator is: nearshore Latin American engineering teams in US time zones. InData Labs's primary differentiator is: discovery, proof of concept, and production build from a small data science team. They also differ in team size (28,500+ vs 80+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Media, Retail vs Retail, Healthcare).
Verify all details directly with each provider before making a decision.