Quantiphi vs Oxagile: full comparison for 2026
Last updated: July 2026
Quick verdict
Quantiphi (4.3/5) edges ahead of Oxagile (4.0/5) overall. Quantiphi is the better choice for enterprises needing production ML on AWS with strong MLOps infrastructure and intelligent document processing. Oxagile is the stronger option for media, healthcare, and manufacturing enterprises needing production computer vision or video AI systems. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs Oxagile: head-to-head summary
| Criterion | Quantiphi | Oxagile |
|---|---|---|
| Founded | 2013 | 2005 |
| HQ | Marlborough, MA, USA | Minsk, Belarus / Warsaw, Poland |
| Team size | 2,670 | 400+ |
| Rating | 4.3 / 5 | 4.0 / 5 |
| Best for | Enterprises needing production ML on AWS with strong MLOps infrastructure and intelligent document processing | Media, healthcare, and manufacturing enterprises needing production computer vision or video AI systems |
| Pricing model | Fixed project, T&M | Fixed project, T&M, Dedicated team |
| Min. engagement | $50K | $25K |
| Primary tech stack | AWS, Python, TensorFlow | Python, TensorFlow, PyTorch |
| Industries served | Healthcare, Financial Services, Retail / E-commerce, Manufacturing, Technology / SaaS | Media / Entertainment, Healthcare, Manufacturing, Technology / SaaS, Logistics |
Quantiphi vs Oxagile: overview
Quantiphi
Quantiphi is an AI-first digital engineering company founded in 2013 and headquartered in Marlborough, Massachusetts, with approximately 2,670 employees as of mid-2026. It is an AWS Premier Global Consulting Partner with the Machine Learning Consulting Competency and has raised $63M in funding. Quantiphi specialises in intelligent document processing, contact centre AI, custom MLOps infrastructure, and data lakes, with delivery depth across healthcare, financial services, retail, and manufacturing. Its NeuralOps framework breaks through common ML bottlenecks by automating repetitive ML engineering tasks, shortening time from model training to production deployment.
Oxagile
Oxagile was founded in 2005 and operates with primary delivery centres in Minsk, Belarus, and Warsaw, Poland, employing 400+ professionals. The company's AI practice centres on computer vision, LLM integration, ML-supported content analysis, and video processing — capabilities that stem from its long heritage in media technology and video infrastructure for broadcasters and OTT platforms. Oxagile's computer vision work spans automated content moderation for media companies, visual quality inspection for manufacturing, and AI-assisted diagnostics for healthcare, making it one of the more vertically diverse computer vision specialists in this review.
Services and capabilities: Quantiphi vs Oxagile
| Capability | Quantiphi | Oxagile |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Deep learning | ✗ | ✗ |
| NLP / Text analytics | ✓ | ✓ |
| Computer vision | ✗ | ✓ |
| MLOps & deployment | ✓ | ✓ |
| Generative AI | ✓ | ✓ |
| AI strategy | ✗ | ✗ |
| Staff augmentation | ✗ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: Quantiphi vs Oxagile
| Framework / platform | Quantiphi | Oxagile |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Kubernetes | ✓ | ✓ |
| Databricks | ✓ | N/A |
| MLflow | ✓ | N/A |
Pricing comparison: Quantiphi vs Oxagile
| Criterion | Quantiphi | Oxagile |
|---|---|---|
| Minimum engagement | $50K | $25K |
| Engagement models | Fixed project, Dedicated team, Time & materials | Fixed project, Time & materials, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Quantiphi vs Oxagile
| Dimension | Quantiphi | Oxagile |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Financial Services, Retail / E-commerce | Media / Entertainment, Healthcare, Manufacturing |
| Best use cases | Intelligent document processing and extraction for insurance, banking, and healthcare claims workflows, Contact centre AI with sentiment analysis and real-time agent assist on AWS infrastructure | Automated video content moderation and compliance tagging for OTT and broadcast platforms, Computer vision quality inspection systems for manufacturing production lines |
| Typical project type | Fixed project | Fixed project |
Quantiphi vs Oxagile: pros and cons
| Quantiphi | |
|---|---|
| + | AWS Premier ML Consulting Competency confirms validated production ML delivery on AWS infrastructure |
| + | Proprietary NeuralOps framework demonstrably reduces ML deployment overhead for enterprise clients |
| + | 2,600+ practitioners provide enough depth for complex concurrent programmes without thin staffing |
| + | Strong intelligent document processing and contact centre AI track record across healthcare and BFSI |
| + | Competitive pricing relative to similarly sized firms, enabled by blended India-US delivery rates |
| - | Strongest on AWS — Azure and GCP engagements involve more third-party tooling rather than native Quantiphi frameworks |
| - | Less brand recognition than Tiger Analytics or Fractal for CPG and BFSI decision-makers |
| - | Partner involvement varies; some clients note engagement quality depends on assigned team seniority |
| Oxagile | |
|---|---|
| + | 20-year computer vision heritage provides production-grade depth in a capability most generalists offer only superficially |
| + | Video AI and content analysis capability is particularly strong — directly transferable to media and broadcast clients |
| + | Dual delivery centre model (Minsk + Warsaw) provides redundancy and EU data processing alignment via Warsaw |
| + | Full project lifecycle from CV prototype through production deployment and monitoring |
| + | Competitive rates relative to Western European firms of equivalent computer vision depth |
| - | Minsk-based delivery introduces political and banking risk for some Western European and North American clients |
| - | Core strength is computer vision and media AI; pure NLP or tabular ML projects may receive less specialised teams |
| - | Less established for cloud-native MLOps and generative AI relative to newer AI-native firms |
Who should choose Quantiphi?
Quantiphi is the right choice for enterprises needing production ML on AWS with strong MLOps infrastructure and intelligent document processing.
AWS Premier ML Consulting Partner with proprietary NeuralOps framework that accelerates time from training to production deployment. Minimum engagement starts at $50K. Works best with clients in Healthcare, Financial Services, Retail / E-commerce, Manufacturing, Technology / SaaS.
Who should choose Oxagile?
Oxagile is the right choice for media, healthcare, and manufacturing enterprises needing production computer vision or video AI systems.
20-year heritage in video technology and media AI translates directly into best-in-class computer vision delivery for media, broadcast, and content platforms. Minimum engagement starts at $25K. Works best with clients in Media / Entertainment, Healthcare, Manufacturing, Technology / SaaS, Logistics.
Decision matrix: Quantiphi vs Oxagile
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Quantiphi |
| You need a large dedicated team for an ongoing programme | Quantiphi |
| Your budget is at the lower end | Oxagile |
| You need specialist depth in a specific vertical | Quantiphi |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Quantiphi vs Oxagile
| Use case | Quantiphi fit | Oxagile fit | Winner |
|---|---|---|---|
| Intelligent document processing and extraction for insurance, banking, and healthcare claims workflows | Strong | Limited | Quantiphi |
| Contact centre AI with sentiment analysis and real-time agent assist on AWS infrastructure | Strong | Limited | Quantiphi |
| Automated video content moderation and compliance tagging for OTT and broadcast platforms | Limited | Strong | Oxagile |
| Computer vision quality inspection systems for manufacturing production lines | Limited | Strong | Oxagile |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Quantiphi vs Oxagile
Quantiphi (4.3/5) is the stronger overall choice for most Machine Learning projects. AWS Premier ML Consulting Partner with proprietary NeuralOps framework that accelerates time from training to production deployment. It is best for enterprises needing production ML on AWS with strong MLOps infrastructure and intelligent document processing.
Oxagile (4.0/5) is the better choice when media, healthcare, and manufacturing enterprises needing production computer vision or video AI systems. If your situation matches those criteria, Oxagile is a competitive option.
Related comparisons
Quantiphi vs Oxagile FAQ
Is Quantiphi better than Oxagile?
Quantiphi (4.3/5) scores higher overall, but "better" depends on your use case. Quantiphi is better for enterprises needing production ML on AWS with strong MLOps infrastructure and intelligent document processing. Oxagile is better for media, healthcare, and manufacturing enterprises needing production computer vision or video AI systems.
How do Quantiphi and Oxagile differ in pricing?
Quantiphi uses fixed project, t&m pricing with a minimum engagement of $50K. Oxagile uses fixed project, t&m, dedicated team pricing with a minimum engagement of $25K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Quantiphi or Oxagile?
Quantiphi is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between Quantiphi and Oxagile?
Quantiphi's primary differentiator is: aws premier ml consulting partner with proprietary neuralops framework that accelerates time from training to production deployment. Oxagile's primary differentiator is: 20-year heritage in video technology and media ai translates directly into best-in-class computer vision delivery for media, broadcast, and content platforms. They also differ in team size (2,670 vs 400+), minimum engagement ($50K vs $25K), and primary industries served (Healthcare, Financial Services vs Media / Entertainment, Healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.