InData Labs vs STX Next: full comparison for 2026
Quick verdict
InData Labs (4.1/5) edges ahead of STX Next (3.9/5) overall. InData Labs is the better choice for mid-sized companies adding data scientists to product teams. STX Next is the stronger option for python codebases adding LLM and data engineers. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs STX Next: head-to-head summary
| Criterion | InData Labs | STX Next |
|---|---|---|
| Founded | 2014 | 2005 |
| HQ | Nicosia, Cyprus | Poznań, Poland |
| Team size | 50–249 | 250–500 |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | A data-science-only firm small enough that senior staff stay involved | Python specialization applied to data and AI delivery |
| Pricing model | Time and materials; dedicated engineers; rates on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Django, FastAPI |
| Industries served | Retail & e-commerce, Healthcare & life sciences, Financial services, Media | Financial services, Software & SaaS, Media, Healthcare & life sciences |
InData Labs vs STX Next: overview
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with additional locations including Vilnius and Miami. Most directories put its headcount below 250 people. The company works only on data science and AI, covering predictive analytics, NLP, computer vision and generative AI, and it supplies engineers to client teams as well as delivering projects.
STX Next
STX Next was founded in Poznań in March 2005 and built its reputation as one of Europe's largest Python software houses. Its 2025 anniversary release cites about 500 staff and more than 1,000 delivered projects, with delivery centers in Poland and Mexico. The firm now presents itself as a data and AI consultancy, and Python's dominance in ML makes its bench a natural fit for model and data work.
Services and capabilities: InData Labs vs STX Next
| Capability | InData Labs | 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: InData Labs vs STX Next
| Framework / platform | InData Labs | STX Next |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: InData Labs vs STX Next
| Criterion | InData Labs | STX Next |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Managed delivery | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs STX Next
| Dimension | InData Labs | STX Next |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail & e-commerce, Healthcare & life sciences, Financial services | Financial services, Software & SaaS, Media |
| Best use cases | Adding a computer-vision engineer to a retail analytics team, Building churn and demand models with in-house analysts | Adding LLM features to a Django product, Building data jobs in Python for analytics |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
InData Labs vs STX Next: pros and cons
| InData Labs | |
|---|---|
| + | Data science and AI are its only line of work |
| + | Experience across vision, language and predictive models |
| + | Clients deal with a small firm where senior staff stay close to the work |
| - | Headcount estimates vary widely, so confirm bench depth for your role |
| - | Limited capacity for large multi-team programs |
| - | Less visible LLM-agent work than some newer specialists |
| STX Next | |
|---|---|
| + | Python depth fits most AI codebases |
| + | Delivery from both Poland and Mexico |
| + | Long history of extending client teams |
| - | AI positioning is recent compared with its Python history |
| - | Polish rates are above Ukrainian and Latin American options |
| - | Fewer specialist roles such as computer vision |
Who should choose InData Labs?
A typical fit: adding a computer-vision engineer to a retail analytics team.
A data-science-only firm small enough that senior staff stay involved. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare & life sciences, Financial services, Media.
Who should choose STX Next?
A typical fit: adding LLM features to a Django product.
Python specialization applied to data and AI delivery. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Software & SaaS, Media, Healthcare & life sciences.
Decision matrix: InData Labs vs STX Next
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | STX Next |
| You want the supplier to own delivery as well as staffing | Both; InData Labs rates higher overall |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: InData Labs (Not published) vs STX Next (Not published) |
| You need overlap with U.S. working hours | Neither is nearshore; agree overlap hours up front |
| You need specialist depth in a specific vertical | InData Labs |
Use case fit: InData Labs vs STX Next
| Use case | InData Labs fit | STX Next fit | Winner |
|---|---|---|---|
| Adding a computer-vision engineer to a retail analytics team | Strong | Strong | Both equally |
| Building churn and demand models with in-house analysts | Strong | Strong | Both equally |
| Adding LLM features to a Django product | Strong | Strong | Both equally |
| Building data jobs in Python for analytics | Strong | Strong | Both equally |
Verdict: InData Labs vs STX Next
InData Labs (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. A data-science-only firm small enough that senior staff stay involved.
STX Next (3.9/5) is worth a look if you need building data jobs in Python for analytics. If your situation matches that, STX Next is a competitive option.
Related comparisons
InData Labs vs STX Next FAQ
Is InData Labs better than STX Next?
InData Labs (4.1/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: data science and AI are its only line of work. STX Next's strongest advantage: python depth fits most AI codebases.
How do InData Labs and STX Next differ in pricing?
InData Labs uses time and materials; dedicated engineers; rates on request pricing. STX Next uses time and materials; dedicated teams; 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: InData Labs 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 company before shortlisting.
What are the main differences between InData Labs and STX Next?
InData Labs's primary differentiator is: a data-science-only firm small enough that senior staff stay involved. STX Next's primary differentiator is: python specialization applied to data and AI delivery. They also differ in team size (50–249 vs 250–500), minimum engagement (Not published vs Not published), and primary industries served (Retail & e-commerce, Healthcare & life sciences vs Financial services, Software & SaaS).
Verify all details directly with each company before making a decision.