Oxagile vs Softeq: full comparison for 2026
Last updated: July 2026
Quick verdict
Oxagile (3.8/5) edges ahead of Softeq (3.7/5) overall. Oxagile is the better choice for media, sports, and AdTech companies needing AI and ML capabilities integrated into video platforms and content analytics systems. Softeq is the stronger option for enterprise companies with hardware, IoT, or embedded systems context needing ML integrated into connected platform programmes. The right choice depends on your project size, budget, and required tech stack.
Oxagile vs Softeq: head-to-head summary
| Criterion | Oxagile | Softeq |
|---|---|---|
| Founded | 2005 | 1997 |
| HQ | New York, NY, USA / Minsk, Belarus | Houston, TX, USA |
| Team size | 400–600 | 700–1,000 |
| Rating | 3.8 / 5 | 3.7 / 5 |
| Best for | Media, sports, and AdTech companies needing AI and ML capabilities integrated into video platforms and content analytics systems | Enterprise companies with hardware, IoT, or embedded systems context needing ML integrated into connected platform programmes |
| Pricing model | Fixed project, dedicated team, T&M | Fixed project, dedicated team, T&M |
| Min. engagement | $25K | $50K |
| Primary tech stack | Python, TensorFlow, OpenCV | Python, TensorFlow, PyTorch |
| Industries served | E-commerce, Healthcare, Manufacturing, Logistics, SaaS | Manufacturing, Healthcare, Logistics, SaaS, Fintech |
Oxagile vs Softeq: overview
Oxagile
Oxagile is a custom software development firm founded in 2005 with offices in New York and Minsk, Belarus, specialising in video domain AI, AdTech, business intelligence, and educational technology. The firm's machine learning practice focuses on object recognition, video analytics, and AI-powered media solutions, drawing on over 20 years of video technology delivery. Oxagile's ML engineering team works with clients in sports, media, advertising, and education to deliver production-grade AI features integrated into video platforms. The firm employs 400+ engineers.
Softeq
Softeq is a technology services company founded in 1997 and headquartered in Houston, Texas, with 700+ professionals delivering AI and machine learning solutions as part of broader digital transformation programmes. The firm has unique strength in projects involving hardware connectivity, embedded systems, and IoT integration alongside ML. Softeq's ML practice covers predictive analytics, computer vision, and NLP, positioned as capability extensions within enterprise platform modernisation engagements. The company holds technology partnerships with Microsoft and AWS.
Services and capabilities: Oxagile vs Softeq
| Capability | Oxagile | Softeq |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✓ | ✓ |
| NLP & text analytics | ✓ | ✗ |
| MLOps & deployment | ✗ | ✓ |
| Generative AI | ✗ | ✗ |
| ML consulting & strategy | ✗ | ✗ |
| Staff augmentation | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: Oxagile vs Softeq
| Framework / platform | Oxagile | Softeq |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | N/A | ✓ |
| TensorFlow | ✓ | ✓ |
| Scikit-learn | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| MLflow | N/A | N/A |
| Hugging Face | N/A | N/A |
| LangChain | N/A | N/A |
| Docker/Kubernetes | N/A | N/A |
| Databricks | N/A | N/A |
Pricing comparison: Oxagile vs Softeq
| Criterion | Oxagile | Softeq |
|---|---|---|
| Minimum engagement | $25K | $50K |
| Engagement models | Fixed project, Dedicated team, Time & materials | Fixed project, Dedicated team, Time & materials |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Oxagile vs Softeq
| Dimension | Oxagile | Softeq |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | E-commerce, Healthcare, Manufacturing | Manufacturing, Healthcare, Logistics |
| Best use cases | Object recognition systems for sports highlight clip generation, Video analytics for media consumption behaviour and content performance | Predictive maintenance for IoT-connected manufacturing equipment and sensors, Computer vision for smart factory quality inspection with camera hardware |
| Typical project type | Fixed project | Fixed project |
Oxagile vs Softeq: pros and cons
| Oxagile | |
|---|---|
| + | 20+ years of video technology expertise — stronger than most for video-domain ML use cases |
| + | Strong computer vision and object recognition delivery across named media and sports clients |
| + | 400+ engineers provide staffing capacity for medium-to-large concurrent projects |
| + | US-based New York presence for North American client engagement in business hours |
| + | Documented AdTech ML applications including ad relevance and fraud detection models |
| - | Primary strength is video and media ML — less suited to non-video ML use cases |
| - | Belarus-based delivery requires business continuity planning for long-term engagements |
| - | Less documented coverage of modern LLM and generative AI than newer competitors |
| Softeq | |
|---|---|
| + | Unique strength in ML for IoT and hardware-connected enterprise systems |
| + | 700+ engineers provide delivery capacity for large enterprise programmes |
| + | Microsoft and AWS partnerships verify cloud ML deployment credentials |
| + | 28-year enterprise technology delivery track record provides procurement confidence |
| + | US Texas HQ for North American enterprise client engagement and account management |
| - | ML is a practice within a broader IT services firm — not an AI-first company |
| - | Less suited to pure ML research or standalone AI product development without hardware context |
| - | $50K minimum may be too high for smaller or startup-stage ML exploration |
Who should choose Oxagile?
Oxagile is the right choice for media, sports, and AdTech companies needing AI and ML capabilities integrated into video platforms and content analytics systems.
20-year video technology specialist with strong computer vision and video analytics ML capability for media, sports, and AdTech clients. Minimum engagement starts at $25K. Works best with clients in E-commerce, Healthcare, Manufacturing, Logistics, SaaS.
Who should choose Softeq?
Softeq is the right choice for enterprise companies with hardware, IoT, or embedded systems context needing ML integrated into connected platform programmes.
Houston-based enterprise firm with unique strength in ML for IoT and hardware-connected AI applications alongside Microsoft and AWS partnerships. Minimum engagement starts at $50K. Works best with clients in Manufacturing, Healthcare, Logistics, SaaS, Fintech.
Decision matrix: Oxagile vs Softeq
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Oxagile |
| You need a large dedicated team for an ongoing programme | Oxagile |
| Your budget is at the lower end | Oxagile |
| You need specialist depth in a specific vertical | Oxagile |
| 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: Oxagile vs Softeq
| Use case | Oxagile fit | Softeq fit | Winner |
|---|---|---|---|
| Object recognition systems for sports highlight clip generation | Strong | Limited | Oxagile |
| Video analytics for media consumption behaviour and content performance | Strong | Limited | Oxagile |
| Predictive maintenance for IoT-connected manufacturing equipment and sensors | Limited | Strong | Softeq |
| Computer vision for smart factory quality inspection with camera hardware | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Oxagile vs Softeq
Oxagile (3.8/5) is the stronger overall choice for most Machine Learning Development projects. 20-year video technology specialist with strong computer vision and video analytics ML capability for media, sports, and AdTech clients. It is best for media, sports, and AdTech companies needing AI and ML capabilities integrated into video platforms and content analytics systems.
Softeq (3.7/5) is the better choice when enterprise companies with hardware, IoT, or embedded systems context needing ML integrated into connected platform programmes. If your situation matches those criteria, Softeq is a competitive option.
Related comparisons
Oxagile vs Softeq FAQ
Is Oxagile better than Softeq?
Oxagile (3.8/5) scores higher overall, but "better" depends on your use case. Oxagile is better for media, sports, and AdTech companies needing AI and ML capabilities integrated into video platforms and content analytics systems. Softeq is better for enterprise companies with hardware, IoT, or embedded systems context needing ML integrated into connected platform programmes.
How do Oxagile and Softeq differ in pricing?
Oxagile uses fixed project, dedicated team, t&m pricing with a minimum engagement of $25K. Softeq uses fixed project, dedicated team, t&m pricing with a minimum engagement of $50K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Oxagile or Softeq?
Softeq 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 Oxagile and Softeq?
Oxagile's primary differentiator is: 20-year video technology specialist with strong computer vision and video analytics ml capability for media, sports, and adtech clients. Softeq's primary differentiator is: houston-based enterprise firm with unique strength in ml for iot and hardware-connected ai applications alongside microsoft and aws partnerships. They also differ in team size (400–600 vs 700–1,000), minimum engagement ($25K vs $50K), and primary industries served (E-commerce, Healthcare vs Manufacturing, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.