Quantiphi vs Globant: full comparison for 2026
Quick verdict
Quantiphi (4.3/5) edges ahead of Globant (4.0/5) overall. Quantiphi is the better choice for large document and contact-center AI programs. Globant is the stronger option for US firms wanting nearshore AI engineering. The right choice depends on how far your pilot has got, your budget, and who will run the system after launch.
Quantiphi vs Globant: head-to-head summary
| Criterion | Quantiphi | Globant |
|---|---|---|
| Founded | 2013 | 2003 |
| HQ | Marlborough, USA | Luxembourg (operations: Buenos Aires) |
| Team size | 4,000 | 28,500+ |
| Rating | 4.3 / 5 | 4.0 / 5 |
| Primary differentiator | Large Google Cloud and AWS engineering bench for document and speech AI in production | Nearshore Latin American engineering teams in US time zones |
| Pricing model | Project and dedicated-team pricing; rates not published | Time & materials and dedicated teams; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Google Cloud, Vertex AI, AWS | Google Cloud, AWS, Azure |
| Industries served | Healthcare, Insurance, Financial services, Public sector, Media | Media, Retail, Financial services, Travel, Healthcare |
Quantiphi vs Globant: overview
Quantiphi
Quantiphi was founded in 2013 and is headquartered in Marlborough, Massachusetts, with around 4,000 employees. Its own materials claim top partner tiers with Google Cloud, AWS, and NVIDIA, and ISG named it a Leader for AI and machine learning services on Google Cloud in its 2024 Provider Lens report. It has a large bench for document, speech, and contact-center AI in production, especially in healthcare, insurance, and the public sector. Big programs suit it better than one small pilot.
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.
Services and capabilities: Quantiphi vs Globant
| Capability | Quantiphi | Globant |
|---|---|---|
| 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: Quantiphi vs Globant
| Framework / platform | Quantiphi | Globant |
|---|---|---|
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | ✓ | ✓ |
| Kubernetes | N/A | N/A |
| MLflow | N/A | N/A |
| Databricks | N/A | ✓ |
| Amazon Bedrock | N/A | N/A |
| Vertex AI | ✓ | N/A |
| LangChain | N/A | N/A |
| Snowflake | ✓ | N/A |
Pricing comparison: Quantiphi vs Globant
| Criterion | Quantiphi | Globant |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed-scope project, Dedicated team, Managed services | Time & materials, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Quantiphi vs Globant
| Dimension | Quantiphi | Globant |
|---|---|---|
| Best company size | Mid-market to enterprise | Enterprise |
| Best industries | Healthcare, Insurance, Financial services | Media, Retail, Financial services |
| Best use cases | Document AI in production for insurers and healthcare payers., Contact-center AI on Google Cloud. | AI features inside consumer digital products., Nearshore AI engineering teams for US companies. |
| Typical project type | Fixed-scope project | Time & materials |
Quantiphi vs Globant: pros and cons
| Quantiphi | |
|---|---|
| + | ISG's 2024 Provider Lens rated its Google Cloud AI work a Leader |
| + | Large team for document, speech, and contact-center systems |
| + | Experience with healthcare and public-sector data rules |
| + | Covers both Google Cloud and AWS |
| - | Partner-tier claims come mostly from its own recruiting material |
| - | Big-program orientation makes small pilots relatively costly |
| - | Rates not published |
| 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 |
Who should choose Quantiphi?
A typical fit: document AI in production for insurers and healthcare payers.
Large Google Cloud and AWS engineering bench for document and speech AI in production. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Insurance, Financial services, Public sector, Media.
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.
Decision matrix: Quantiphi vs Globant
| 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 | Quantiphi |
| You run classic ML models that need serving and retraining | Quantiphi |
| You want a priced pilot before a full rollout | Neither advertises one; ask for a scoped quote |
| Your budget is at the lower end | Compare: Quantiphi (Not disclosed) vs Globant (Not disclosed) |
| You need a large team for many products at once | Globant |
Use case fit: Quantiphi vs Globant
| Use case | Quantiphi fit | Globant fit | Winner |
|---|---|---|---|
| Document AI in production for insurers and healthcare payers. | Strong | Limited | Quantiphi |
| Contact-center AI on Google Cloud. | Strong | Limited | Quantiphi |
| AI features inside consumer digital products. | Limited | Strong | Globant |
| Nearshore AI engineering teams for US companies. | Limited | Strong | Globant |
Verdict: Quantiphi vs Globant
Quantiphi (4.3/5) is the stronger overall choice for most AI Implementation projects. Large Google Cloud and AWS engineering bench for document and speech AI in production.
Globant (4.0/5) is worth a look if you need nearshore AI engineering teams for US companies. If your situation matches that, Globant is a competitive option.
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Quantiphi vs Globant FAQ
Is Quantiphi better than Globant?
Quantiphi (4.3/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: ISG's 2024 Provider Lens rated its Google Cloud AI work a Leader. Globant's strongest advantage: nearshore teams share working hours with US clients.
How do Quantiphi and Globant differ in pricing?
Quantiphi's pricing: project and dedicated-team pricing; rates not published. Globant's pricing: time & materials and dedicated teams; rates not published. 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: Quantiphi or Globant?
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 Quantiphi and Globant?
Quantiphi's primary differentiator is: large Google Cloud and AWS engineering bench for document and speech AI in production. Globant's primary differentiator is: nearshore Latin American engineering teams in US time zones. They also differ in team size (4,000 vs 28,500+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthcare, Insurance vs Media, Retail).
Verify all details directly with each provider before making a decision.