MobiDev vs STX Next: full comparison for 2026
Quick verdict
MobiDev (4.4/5) edges ahead of STX Next (4.2/5) overall. MobiDev is the better choice for retail and fitness products, one AI engineer to start. STX Next is the stronger option for python product teams adding ML capacity. The right choice depends on your project size, budget, and required tech stack.
MobiDev vs STX Next: head-to-head summary
| Criterion | MobiDev | STX Next |
|---|---|---|
| Founded | 2009 | 2005 |
| HQ | Atlanta, USA (R&D in Ukraine and Poland) | Poznań, Poland |
| Team size | 201–500 | 250–999 |
| Rating | 4.4 / 5 | 4.2 / 5 |
| Primary differentiator | Long AI product record in retail, hospitality and fitness | Large Python bench with documented ML staff-augmentation work |
| Pricing model | Monthly per engineer; dedicated team; rates on request | Time and materials; team extension; rates on request |
| Min. engagement | 1 full-time engineer | Not disclosed |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Django, PyTorch |
| Industries served | Retail and e-commerce, Hospitality, Fitness and wellness, Healthcare | Real estate tech, Healthcare, Fintech, SaaS |
MobiDev vs STX Next: overview
MobiDev
MobiDev was founded in 2009 in Kharkiv, Ukraine, opened its first U.S. office in Atlanta in 2011, and now runs R&D centers in Ukraine and Łódź, Poland. Its AI team-augmentation offer starts at a single full-time engineer and quotes up to two weeks to allocate someone (per company website; independently unverifiable). The company says 89% of its engineers are middle or senior level and reports more than 65 AI and ML products built, mainly for retail, hospitality, fitness and health clients. Headcount figures range from 201–500 on aggregators to 400+ on a regional IT directory.
STX Next
STX Next was founded in 2005 in Poznań, Poland, and runs delivery centers in Poland and Mexico. It describes itself as Europe's largest Python-focused engineering partner for data, AI and cloud (per company website; independently unverifiable), and Clutch places it in the 250–999 employee band. A Clutch review covers a 2023–2024 staff-augmentation engagement for a real-estate technology client involving machine learning, computer vision and recommendation systems. Other reviews describe multi-year Python team extensions.
Services and capabilities: MobiDev vs STX Next
| Capability | MobiDev | STX Next |
|---|---|---|
| LLM / GenAI engineers | ✗ | ✗ |
| MLOps & deployment | ✗ | ✗ |
| Computer vision | ✓ | ✓ |
| Data engineering | ✗ | ✓ |
| AI agent development | ✓ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: MobiDev vs STX Next
| Framework / platform | MobiDev | STX Next |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: MobiDev vs STX Next
| Criterion | MobiDev | STX Next |
|---|---|---|
| Minimum engagement | 1 full-time engineer | Not disclosed |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: MobiDev vs STX Next
| Dimension | MobiDev | STX Next |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail and e-commerce, Hospitality, Fitness and wellness | Real estate tech, Healthcare, Fintech |
| Best use cases | Adding a pose-estimation engineer to a fitness app, Placing an AI engineer to build product recommendations for a retailer | Adding a recommendation-systems engineer to a marketplace product, Extending a Python team with a computer-vision specialist |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
MobiDev vs STX Next: pros and cons
| MobiDev | |
|---|---|
| + | You can start with a single engineer instead of a whole squad |
| + | Senior-weighted bench, with a stated six-year average experience among lead AI engineers |
| + | Strong record in computer vision for fitness and sports products |
| + | U.S. and UK incorporation makes contracting straightforward |
| - | Much of the delivery team is in Ukraine, so some buyers will want to discuss continuity planning |
| - | Industry focus is narrower than the large generalists |
| - | No published rates |
| STX Next | |
|---|---|
| + | Python depth means ML and backend roles come from one bench |
| + | Documented multi-year team extensions |
| + | Mexico center adds U.S. time-zone coverage |
| - | AI is a practice within a broader Python services company |
| - | Largest-in-Europe positioning is the company's own claim |
| - | No public rates |
Who should choose MobiDev?
A typical fit: adding a pose-estimation engineer to a fitness app.
Long AI product record in retail, hospitality and fitness. Minimum engagement starts at 1 full-time engineer. Works best with clients in Retail and e-commerce, Hospitality, Fitness and wellness, Healthcare.
Who should choose STX Next?
A typical fit: adding a recommendation-systems engineer to a marketplace product.
Large Python bench with documented ML staff-augmentation work. Minimum engagement is not publicly disclosed. Works best with clients in Real estate tech, Healthcare, Fintech, SaaS.
Decision matrix: MobiDev vs STX Next
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | MobiDev |
| Your budget is at the lower end | Compare: MobiDev (1 full-time engineer) vs STX Next (Not disclosed) |
| You need specialist depth in a specific vertical | MobiDev |
| 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: MobiDev vs STX Next
| Use case | MobiDev fit | STX Next fit | Winner |
|---|---|---|---|
| Adding a pose-estimation engineer to a fitness app | Strong | Strong | Both equally |
| Placing an AI engineer to build product recommendations for a retailer | Strong | Limited | MobiDev |
| Adding a recommendation-systems engineer to a marketplace product | Strong | Strong | Both equally |
| Extending a Python team with a computer-vision specialist | Limited | Strong | STX Next |
Verdict: MobiDev vs STX Next
MobiDev (4.4/5) is the stronger overall choice for most AI Staffing projects. Long AI product record in retail, hospitality and fitness.
STX Next (4.2/5) is worth a look if you need extending a Python team with a computer-vision specialist. If your situation matches that, STX Next is a competitive option.
Related comparisons
MobiDev vs STX Next FAQ
Is MobiDev better than STX Next?
MobiDev (4.4/5) scores higher overall, but "better" depends on your use case. MobiDev's strongest advantage: you can start with a single engineer instead of a whole squad. STX Next's strongest advantage: python depth means ML and backend roles come from one bench.
How do MobiDev and STX Next differ in pricing?
MobiDev uses monthly per engineer; dedicated team; rates on request pricing with a minimum engagement of 1 full-time engineer. STX Next uses time and materials; team extension; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: MobiDev or STX Next?
STX Next 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 MobiDev and STX Next?
MobiDev's primary differentiator is: long AI product record in retail, hospitality and fitness. STX Next's primary differentiator is: large Python bench with documented ML staff-augmentation work. They also differ in team size (201–500 vs 250–999), minimum engagement (1 full-time engineer vs Not disclosed), and primary industries served (Retail and e-commerce, Hospitality vs Real estate tech, Healthcare).
Verify all details directly with each agency before making a decision.