Addepto vs Softeq: full comparison for 2026
Last updated: July 2026
Quick verdict
Addepto (3.9/5) edges ahead of Softeq (3.8/5) overall. Addepto is the better choice for manufacturing, logistics, and retail SMEs needing a focused ML boutique with direct senior access and vertical-specific delivery experience. Softeq is the stronger option for manufacturers, robotics companies, and IoT product builders needing ML integrated with embedded hardware and connected device firmware. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Softeq: head-to-head summary
| Criterion | Addepto | Softeq |
|---|---|---|
| Founded | 2017 | 1997 |
| HQ | Warsaw, Poland | Houston, TX, USA |
| Team size | 50–100 | 400+ |
| Rating | 3.9 / 5 | 3.8 / 5 |
| Best for | Manufacturing, logistics, and retail SMEs needing a focused ML boutique with direct senior access and vertical-specific delivery experience | Manufacturers, robotics companies, and IoT product builders needing ML integrated with embedded hardware and connected device firmware |
| Pricing model | Fixed project, T&M | Fixed project, T&M, Dedicated team |
| Min. engagement | $15K | $25K |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, TensorFlow, AWS |
| Industries served | Manufacturing, Retail / E-commerce, Financial Services, Logistics | Manufacturing, Healthcare, Retail / E-commerce, Logistics, Technology / SaaS |
Addepto vs Softeq: overview
Addepto
Addepto is a machine learning and AI consultancy established in 2017 and headquartered in Warsaw, Poland, with approximately 52 employees. Despite its small size, Addepto has built a focused portfolio in manufacturing predictive maintenance, logistics AI, and retail recommendation engines, delivering scalable ML solutions that align with the specific data patterns and operational constraints of each vertical. The firm's notable projects include predictive maintenance implementations for manufacturing clients, logistics optimisation using AI-driven analysis, and recommendation engines for retail. Addepto is one of the more accessible boutiques by team size and minimum engagement, suitable for companies requiring a specialised ML partner without enterprise-level overhead.
Softeq
Softeq was founded by Christopher A. Howard in 1997 and is headquartered in Houston, Texas, with offices in Los Angeles, London, and Munich, and development centres in Vilnius, Lithuania, and Monterrey, Mexico. It employs 400+ professionals across software, firmware, hardware, IoT, AI/ML, and AR/VR capabilities. Softeq's distinguishing characteristic in the ML market is its hardware-to-cloud engineering breadth — clients whose ML challenge sits at the intersection of physical devices and data systems (robotics, smart manufacturing, connected hardware) benefit from Softeq's ability to deliver the full stack from embedded firmware through cloud ML without requiring separate hardware and software vendors.
Services and capabilities: Addepto vs Softeq
| Capability | Addepto | Softeq |
|---|---|---|
| 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: Addepto vs Softeq
| Framework / platform | Addepto | Softeq |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Kubernetes | ✓ | N/A |
| Databricks | N/A | N/A |
| MLflow | ✓ | N/A |
Pricing comparison: Addepto vs Softeq
| Criterion | Addepto | Softeq |
|---|---|---|
| Minimum engagement | $15K | $25K |
| Engagement models | Fixed project, Time & materials | Fixed project, Time & materials, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Addepto vs Softeq
| Dimension | Addepto | Softeq |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail / E-commerce, Financial Services | Manufacturing, Healthcare, Retail / E-commerce |
| Best use cases | Predictive maintenance ML for manufacturing equipment with IoT sensor data integration, Recommendation engine development for e-commerce and retail personalisation platforms | Computer vision quality inspection embedded in smart manufacturing equipment with on-device inference, IoT sensor data ML for predictive maintenance with edge AI processing on connected hardware |
| Typical project type | Fixed project | Fixed project |
Addepto vs Softeq: pros and cons
| Addepto | |
|---|---|
| + | Focused manufacturing and retail portfolio reduces onboarding time on predictive maintenance and recommendation system projects |
| + | Small team ensures senior practitioner involvement throughout the engagement rather than junior staffing after kickoff |
| + | Competitive Warsaw-based rates are well below US boutiques of equivalent vertical ML depth |
| + | Accessible $15K minimum allows SMEs to engage professional ML delivery without enterprise investment levels |
| - | Team of ~52 strictly limits concurrent capacity — unsuitable for clients needing multiple simultaneous ML tracks |
| - | Founded 2017 — shorter track record than established competitors for high-stakes procurement decisions |
| - | Narrow vertical focus means less applicable experience for clients in healthcare, financial services, or media |
| - | Less infrastructure in generative AI, agentic systems, or large-scale MLOps compared to larger firms |
| Softeq | |
|---|---|
| + | Only firm in this review offering ML development combined with hardware engineering, firmware, and IoT connectivity |
| + | 25+ years of operation and inclusion in Inc. 5000 validate sustained delivery quality |
| + | Houston HQ provides US-based relationship management with competitive blended rates from Lithuania and Mexico delivery |
| + | AR/VR capability alongside ML creates unique edge for industrial training and visualisation applications |
| - | ML is one component of a very broad portfolio — specialist deep learning or advanced NLP depth is thinner than ML-native boutiques |
| - | Less suitable for pure cloud ML or data analytics engagements with no hardware component |
| - | Less established in generative AI and LLM integration compared to newer AI-native competitors |
Who should choose Addepto?
Addepto is the right choice for manufacturing, logistics, and retail SMEs needing a focused ML boutique with direct senior access and vertical-specific delivery experience.
Focused vertical expertise in manufacturing predictive maintenance and retail AI at boutique scale — avoids the generalist overhead of larger firms for targeted use cases. Minimum engagement starts at $15K. Works best with clients in Manufacturing, Retail / E-commerce, Financial Services, Logistics.
Who should choose Softeq?
Softeq is the right choice for manufacturers, robotics companies, and IoT product builders needing ML integrated with embedded hardware and connected device firmware.
Unique full-stack hardware-to-cloud capability — ML embedded into firmware and device systems without requiring a separate hardware engineering partner. Minimum engagement starts at $25K. Works best with clients in Manufacturing, Healthcare, Retail / E-commerce, Logistics, Technology / SaaS.
Decision matrix: Addepto vs Softeq
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Addepto |
| You need a large dedicated team for an ongoing programme | Softeq |
| Your budget is at the lower end | Addepto |
| You need specialist depth in a specific vertical | Softeq |
| 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: Addepto vs Softeq
| Use case | Addepto fit | Softeq fit | Winner |
|---|---|---|---|
| Predictive maintenance ML for manufacturing equipment with IoT sensor data integration | Strong | Strong | Both equally |
| Recommendation engine development for e-commerce and retail personalisation platforms | Strong | Limited | Addepto |
| Computer vision quality inspection embedded in smart manufacturing equipment with on-device inference | Limited | Strong | Softeq |
| IoT sensor data ML for predictive maintenance with edge AI processing on connected hardware | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Addepto vs Softeq
Addepto (3.9/5) is the stronger overall choice for most Machine Learning projects. Focused vertical expertise in manufacturing predictive maintenance and retail AI at boutique scale — avoids the generalist overhead of larger firms for targeted use cases. It is best for manufacturing, logistics, and retail SMEs needing a focused ML boutique with direct senior access and vertical-specific delivery experience.
Softeq (3.8/5) is the better choice when manufacturers, robotics companies, and IoT product builders needing ML integrated with embedded hardware and connected device firmware. If your situation matches those criteria, Softeq is a competitive option.
Related comparisons
Addepto vs Softeq FAQ
Is Addepto better than Softeq?
Addepto (3.9/5) scores higher overall, but "better" depends on your use case. Addepto is better for manufacturing, logistics, and retail SMEs needing a focused ML boutique with direct senior access and vertical-specific delivery experience. Softeq is better for manufacturers, robotics companies, and IoT product builders needing ML integrated with embedded hardware and connected device firmware.
How do Addepto and Softeq differ in pricing?
Addepto uses fixed project, t&m pricing with a minimum engagement of $15K. Softeq 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: Addepto or Softeq?
Addepto 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 Addepto and Softeq?
Addepto's primary differentiator is: focused vertical expertise in manufacturing predictive maintenance and retail ai at boutique scale — avoids the generalist overhead of larger firms for targeted use cases. Softeq's primary differentiator is: unique full-stack hardware-to-cloud capability — ml embedded into firmware and device systems without requiring a separate hardware engineering partner. They also differ in team size (50–100 vs 400+), minimum engagement ($15K vs $25K), and primary industries served (Manufacturing, Retail / E-commerce vs Manufacturing, Healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.