Quantiphi vs Oxagile: full comparison for 2026
Last updated: July 2026
Quick verdict
Quantiphi (4.4/5) edges ahead of Oxagile (4.2/5) overall. Quantiphi is the better choice for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials. Oxagile is the stronger option for enterprises in healthcare, media, or retail seeking cost-effective ML development with Eastern European engineering quality. 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 | Minsk, Belarus |
| Team size | 1,000–5,000 | 250–999 |
| Rating | 4.4 / 5 | 4.2 / 5 |
| Best for | Enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials | Enterprises in healthcare, media, or retail seeking cost-effective ML development with Eastern European engineering quality |
| Pricing model | Fixed project, T&M, dedicated team | Fixed project, T&M, dedicated team |
| Min. engagement | $75K | $20K |
| Primary tech stack | TensorFlow, PyTorch, AWS SageMaker | Python, TensorFlow, OpenCV |
| Industries served | Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce | Healthcare & Life Sciences, Media & Entertainment, Financial Services, Manufacturing & Industrial, Retail & E-commerce |
Quantiphi vs Oxagile: overview
Quantiphi
Quantiphi is an AI-first digital engineering company founded in 2013 and headquartered in Marlborough, MA, with 1,001–5,000 employees. The firm holds AWS Premier Global Consulting Partner status and was named a Google Cloud Partner of the Year across four categories in 2026. Quantiphi's ML practice spans cloud-native model development, MLOps, computer vision, NLP, and generative AI, with a strong track record in healthcare, financial services, media, and retail.
Oxagile
Oxagile is a software and AI development company founded in 2005 and headquartered in Minsk, Belarus, with 250–999 employees. The firm offers AI software development services with a focus on data-driven solutions for digital transformation. Oxagile is recognised for connected care AI in healthcare, computer vision in media and retail, and custom ML systems for enterprise clients across multiple verticals.
Services and capabilities: Quantiphi vs Oxagile
| Capability | Quantiphi | Oxagile |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✓ | ✓ |
| NLP & LLMs | ✓ | ✓ |
| MLOps & deployment | ✓ | ✓ |
| Generative AI | ✓ | ✗ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Quantiphi vs Oxagile
| Framework / platform | Quantiphi | Oxagile |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS SageMaker | ✓ | N/A |
| Azure ML | N/A | N/A |
| Vertex AI | ✓ | N/A |
| Scikit-learn | N/A | ✓ |
| Hugging Face | N/A | N/A |
| Apache Spark | ✓ | N/A |
| Kubernetes | N/A | N/A |
| MLflow | N/A | N/A |
Pricing comparison: Quantiphi vs Oxagile
| Criterion | Quantiphi | Oxagile |
|---|---|---|
| Minimum engagement | $75K | $20K |
| Engagement models | Fixed project, Time & materials, Dedicated team | 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 | Mid-market to enterprise | Startup to mid-market |
| Best industries | Healthcare & Life Sciences, Financial Services, Media & Entertainment | Healthcare & Life Sciences, Media & Entertainment, Financial Services |
| Best use cases | Enterprise ML platform build on AWS SageMaker with MLOps pipeline and model governance, Healthcare computer vision system for radiology and pathology AI on Google Cloud | Connected care AI for remote patient monitoring and telemedicine platform, Computer vision content moderation system for media streaming service |
| Typical project type | Fixed project | Fixed project |
Quantiphi vs Oxagile: pros and cons
| Quantiphi | |
|---|---|
| + | AWS Premier + Google Cloud four-time Partner of the Year — independently verified at the highest cloud tier |
| + | Named first Preferred Amazon Quick Global SI Partner by the AWS GenAI Innovation Center |
| + | Deep healthcare ML practice with imaging AI and clinical NLP deployments |
| + | Large team (1,000–5,000) supports enterprise-scale parallel programmes across multiple verticals |
| + | Covers both cloud-native SageMaker/Vertex AI and on-premise ML infrastructure |
| - | $75K+ minimum engagement excludes SMB and startup budgets |
| - | Large-firm delivery cadence can feel slower than agile boutiques for fast-moving projects |
| - | Strong AWS and GCP depth; less Azure-native capability compared to Microsoft-aligned firms |
| Oxagile | |
|---|---|
| + | Competitive rates — 40–60% lower than US equivalents at comparable engineering quality |
| + | Connected care and healthcare imaging AI track record with PACS integration experience |
| + | Lower $20K minimum makes specialist ML accessible for budget-conscious projects |
| + | Computer vision depth in both media and industrial inspection use cases |
| + | Flexible three-model engagement covers fixed scope through long-term dedicated teams |
| - | Belarus-based delivery carries geopolitical risk and potential regulatory complications for some enterprises |
| - | Less generative AI and LLM depth than firms with more recent AI-native practices |
| - | Brand visibility lower than US-headquartered peers in North American procurement processes |
Who should choose Quantiphi?
Quantiphi is the right choice for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials.
AWS Premier and four-time Google Cloud Partner of the Year — the highest independently verified cloud ML credentials in the market. Minimum engagement starts at $75K. Works best with clients in Healthcare & Life Sciences, Financial Services, Media & Entertainment, Manufacturing & Industrial, Retail & E-commerce.
Who should choose Oxagile?
Oxagile is the right choice for enterprises in healthcare, media, or retail seeking cost-effective ML development with Eastern European engineering quality.
Strong connected-care and healthcare AI track record combined with 40–60% cost advantage versus US equivalents. Minimum engagement starts at $20K. Works best with clients in Healthcare & Life Sciences, Media & Entertainment, Financial Services, Manufacturing & Industrial, Retail & E-commerce.
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 |
|---|---|---|---|
| Enterprise ML platform build on AWS SageMaker with MLOps pipeline and model governance | Strong | Limited | Quantiphi |
| Healthcare computer vision system for radiology and pathology AI on Google Cloud | Strong | Strong | Both equally |
| Connected care AI for remote patient monitoring and telemedicine platform | Limited | Strong | Oxagile |
| Computer vision content moderation system for media streaming service | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Quantiphi vs Oxagile
Quantiphi (4.4/5) is the stronger overall choice for most Machine Learning Development projects. AWS Premier and four-time Google Cloud Partner of the Year — the highest independently verified cloud ML credentials in the market. It is best for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials.
Oxagile (4.2/5) is the better choice when enterprises in healthcare, media, or retail seeking cost-effective ML development with Eastern European engineering quality. If your situation matches those criteria, Oxagile is a competitive option.
Related comparisons
Quantiphi vs Oxagile FAQ
Is Quantiphi better than Oxagile?
Quantiphi (4.4/5) scores higher overall, but "better" depends on your use case. Quantiphi is better for enterprises that need cloud-native ML at scale on AWS or Google Cloud with top-tier partnership credentials. Oxagile is better for enterprises in healthcare, media, or retail seeking cost-effective ML development with Eastern European engineering quality.
How do Quantiphi and Oxagile differ in pricing?
Quantiphi uses fixed project, t&m, dedicated team pricing with a minimum engagement of $75K. Oxagile uses fixed project, t&m, dedicated team pricing with a minimum engagement of $20K. 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 company before shortlisting.
What are the main differences between Quantiphi and Oxagile?
Quantiphi's primary differentiator is: aws premier and four-time google cloud partner of the year — the highest independently verified cloud ml credentials in the market. Oxagile's primary differentiator is: strong connected-care and healthcare ai track record combined with 40–60% cost advantage versus us equivalents. They also differ in team size (1,000–5,000 vs 250–999), minimum engagement ($75K vs $20K), and primary industries served (Healthcare & Life Sciences, Financial Services vs Healthcare & Life Sciences, Media & Entertainment).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.