Quantiphi vs Softweb Solutions: full comparison for 2026
Last updated: July 2026
Quick verdict
Quantiphi (4.4/5) edges ahead of Softweb Solutions (3.9/5) overall. Quantiphi is the better choice for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering.. Softweb Solutions is the stronger option for companies needing AI/ML specifically paired with IoT sensor data and device deployment, backed by Avnet's hardware supply chain.. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs Softweb Solutions: head-to-head summary
| Criterion | Quantiphi | Softweb Solutions |
|---|---|---|
| Founded | 2013 | 2006 |
| HQ | Marlborough, Massachusetts, USA | Plano, Texas, USA |
| Team size | 1,001–5,000 | 201–500 |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Best for | Enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering. | Companies needing AI/ML specifically paired with IoT sensor data and device deployment, backed by Avnet's hardware supply chain. |
| Pricing model | Fixed project and managed AI services | Fixed project and managed services |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, Google Cloud Vertex AI | Python, TensorFlow, Azure IoT |
| Industries served | Financial Services, Healthcare, Media, Technology/SaaS | Manufacturing, Retail, Logistics |
Quantiphi vs Softweb Solutions: overview
Quantiphi
Quantiphi is an AI-first digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, headquartered in Marlborough, Massachusetts. Reported headcount is roughly 2,670–3,927 employees depending on source, making it one of the larger, more established AI-native firms on this list, with strong focus on financial services and cloud-native ML platform engineering.
Softweb Solutions
Softweb Solutions is an AI and IoT-focused digital transformation company founded in 2006 (some sources cite 2004), headquartered in Plano, Texas, with additional offices in Chicago, Dallas, and Ahmedabad, India. The company was acquired by global electronics distributor Avnet in December 2018 and now operates as 'Softweb Solutions — An Avnet Company,' building AI models for image classification, intelligent forecasting, and IoT scenario detection alongside broader data and digital transformation services.
Services and capabilities: Quantiphi vs Softweb Solutions
| Capability | Quantiphi | Softweb Solutions |
|---|---|---|
| Custom ML model development | ✓ | ✓ |
| Deep learning & computer vision | ✗ | ✓ |
| NLP & LLM / Generative AI | ✗ | ✗ |
| MLOps & production deployment | ✓ | ✗ |
| Data engineering | ✓ | ✗ |
| AI strategy consulting | ✓ | ✗ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Quantiphi vs Softweb Solutions
| Framework / platform | Quantiphi | Softweb Solutions |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: Quantiphi vs Softweb Solutions
| Criterion | Quantiphi | Softweb Solutions |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Managed services | Fixed project, Managed services |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / not published | Enterprise / not published |
Target audience comparison: Quantiphi vs Softweb Solutions
| Dimension | Quantiphi | Softweb Solutions |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial Services, Healthcare, Media | Manufacturing, Retail, Logistics |
| Best use cases | Enterprise financial-services AI programs requiring both scale and deep ML expertise, Cloud-native ML platform builds on GCP, AWS, or Azure at production scale | Manufacturing or logistics clients needing AI models tied to IoT sensor data, Retail computer-vision projects such as image classification or shelf monitoring |
| Typical project type | Fixed project | Fixed project |
Quantiphi vs Softweb Solutions: pros and cons
| Quantiphi | |
|---|---|
| + | Founded as an AI-first company rather than a generalist IT firm that later added an AI practice |
| + | Enterprise-scale headcount (2,600+) supports large, multi-region programs |
| + | Strong cloud-native ML platform engineering, reducing gaps between model development and production deployment |
| + | 13 years of continuous focus on applied AI and analytics |
| - | Scale and enterprise sales process may be slower and less accessible for small pilot projects than boutique competitors |
| - | Recent employee counts show a reported year-over-year headcount decline (~4% per one source), worth asking about directly |
| - | Minimum engagement size and standard pricing are not publicly disclosed |
| Softweb Solutions | |
|---|---|
| + | Ownership by Avnet (since December 2018) gives direct proximity to hardware and electronics supply chains for IoT-linked AI projects |
| + | Nearly two decades of operating history combining AI and IoT delivery |
| + | Named use cases in image classification and intelligent forecasting show concrete applied AI/ML work |
| + | Multi-country office presence (US, India) supports cost-flexible delivery |
| - | Acquired by Avnet in 2018 — the company now operates as a subsidiary rather than an independent AI consultancy, which can affect contracting flexibility |
| - | Public sources disagree on both founding year (2004 vs. 2006) and employee count (201–500 vs. 501–1,000) |
| - | IoT-centric positioning may be less suited to buyers wanting purely software-based ML with no hardware/device component |
Who should choose Quantiphi?
Quantiphi is the right choice for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering..
AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing.. Minimum engagement starts at Not published. Works best with clients in Financial Services, Healthcare, Media, Technology/SaaS.
Who should choose Softweb Solutions?
Softweb Solutions is the right choice for companies needing AI/ML specifically paired with IoT sensor data and device deployment, backed by Avnet's hardware supply chain..
Backed by Avnet, a global electronics distributor, giving unusual hardware/IoT supply-chain proximity for AI-on-device projects.. Minimum engagement starts at Not published. Works best with clients in Manufacturing, Retail, Logistics.
Decision matrix: Quantiphi vs Softweb Solutions
| 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 | Check each company's engagement model |
| Your budget is at the lower end | Compare: Quantiphi (Not published) vs Softweb Solutions (Not published) |
| 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 | Quantiphi |
Use case fit: Quantiphi vs Softweb Solutions
| Use case | Quantiphi fit | Softweb Solutions fit | Winner |
|---|---|---|---|
| Enterprise financial-services AI programs requiring both scale and deep ML expertise | Strong | Limited | Quantiphi |
| Cloud-native ML platform builds on GCP, AWS, or Azure at production scale | Strong | Limited | Quantiphi |
| Manufacturing or logistics clients needing AI models tied to IoT sensor data | Limited | Strong | Softweb Solutions |
| Retail computer-vision projects such as image classification or shelf monitoring | Limited | Strong | Softweb Solutions |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Quantiphi vs Softweb Solutions
Quantiphi (4.4/5) is the stronger overall choice for most Machine Learning Development projects. AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing.. It is best for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering..
Softweb Solutions (3.9/5) is the better choice when companies needing AI/ML specifically paired with IoT sensor data and device deployment, backed by Avnet's hardware supply chain.. If your situation matches those criteria, Softweb Solutions is a competitive option.
Related comparisons
Quantiphi vs Softweb Solutions FAQ
Is Quantiphi better than Softweb Solutions?
Quantiphi (4.4/5) scores higher overall, but "better" depends on your use case. Quantiphi is better for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering.. Softweb Solutions is better for companies needing AI/ML specifically paired with IoT sensor data and device deployment, backed by Avnet's hardware supply chain..
How do Quantiphi and Softweb Solutions differ in pricing?
Quantiphi uses fixed project and managed ai services pricing with a minimum engagement of Not published. Softweb Solutions uses fixed project and managed services pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Quantiphi or Softweb Solutions?
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 Softweb Solutions?
Quantiphi's primary differentiator is: ai-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist it outsourcing.. Softweb Solutions's primary differentiator is: backed by avnet, a global electronics distributor, giving unusual hardware/iot supply-chain proximity for ai-on-device projects.. They also differ in team size (1,001–5,000 vs 201–500), minimum engagement (Not published vs Not published), and primary industries served (Financial Services, Healthcare vs Manufacturing, Retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.