InData Labs vs STX Next: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.4/5) edges ahead of STX Next (4.0/5) overall. InData Labs is the better choice for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. STX Next is the stronger option for enterprises wanting Python-native ML and AI engineering from a vendor with two decades of Python specialization and certified partnerships across all three major clouds.. 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 (R&D and delivery centers in Lithuania and the US) | Poznań, Poland |
| Team size | 80+ | 330 |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Best for | Companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop. | Enterprises wanting Python-native ML and AI engineering from a vendor with two decades of Python specialization and certified partnerships across all three major clouds. |
| Pricing model | Fixed project, Time & Materials | Fixed project, dedicated team, staff augmentation |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Generative AI/GPT tooling, Computer vision frameworks | Python, AWS, Snowflake |
| Industries served | Cross-industry, Predictive Analytics | Financial Services, Manufacturing, Energy & Utilities, Healthcare, Retail/E-commerce |
InData Labs vs STX Next: overview
InData Labs
InData Labs is a data science and AI consultancy legally headquartered in Nicosia, Cyprus, founded in 2014 by video-gaming industry veteran Marat Karpeko, with R&D and delivery centers in Lithuania and the US. The 80+ person firm runs its own R&D center and covers a wide technical band from generative AI and GPT integration through predictive analytics, forecasting, and computer vision. Its Cyprus legal HQ gives clients an EU-entity contracting structure alongside nearshore delivery capacity.
STX Next
STX Next is a Poznań, Poland software company founded in 2005, describing itself as the largest Python-focused software development company in Europe with 330 employees operating a fully remote model across the US, UK, DACH region, and Poland. It holds simultaneous AWS Advanced Tier, Snowflake, Databricks, Microsoft Azure, and Amazon Bedrock partnerships, and built and open-sourced DeepNext, an autonomous AI developer agent, serving financial services, private equity, manufacturing, oil & gas, and healthcare clients.
Services and capabilities: InData Labs vs STX Next
| Capability | InData Labs | STX Next |
|---|---|---|
| ML Development | ✓ | ✓ |
| AI Consulting | ✓ | ✗ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| MLOps | ✗ | ✗ |
| Data Engineering | ✓ | ✓ |
| Staff Augmentation | ✗ | ✓ |
Tech stack comparison: InData Labs vs STX Next
| Framework / platform | InData Labs | STX Next |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | N/A | ✓ |
| Microsoft Azure | N/A | ✓ |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | N/A |
| PyTorch | N/A | N/A |
| LangChain | N/A | N/A |
| Databricks | N/A | ✓ |
Pricing comparison: InData Labs vs STX Next
| Criterion | InData Labs | STX Next |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Time & Materials | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: InData Labs vs STX Next
| Dimension | InData Labs | STX Next |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Cross-industry, Predictive Analytics | Financial Services, Manufacturing, Energy & Utilities |
| Best use cases | Generative AI and GPT integration projects, Predictive analytics and forecasting | Python-native ML pipeline development, Multi-cloud MLOps using Databricks, Snowflake, and Bedrock |
| Typical project type | Fixed project | Fixed project |
InData Labs vs STX Next: pros and cons
| InData Labs | |
|---|---|
| + | Founded 2014 — one of the longer-running boutique data science firms in this list |
| + | In-house R&D center is a differentiator versus pure staff-augmentation vendors |
| + | Cyprus legal HQ with Lithuania/US delivery centers gives EU-entity contracting plus nearshore delivery |
| + | Broad technical range from generative AI to classic forecasting and computer vision |
| - | 80+ employee band is imprecise — exact current headcount is not independently published |
| - | Legal HQ (Cyprus) is a smaller AI hub than its Lithuania delivery center, which may matter to buyers wanting an on-the-ground presence |
| - | Pricing model and minimum engagement are not published |
| STX Next | |
|---|---|
| + | Largest Python-focused software company in Europe (per company website), giving deep bench strength for Python-native ML engineering |
| + | Certified across AWS Advanced Tier, Snowflake, Databricks, Azure, and Amazon Bedrock simultaneously — an unusually broad multi-cloud partner portfolio |
| + | Open-sourced its own autonomous AI dev agent (DeepNext), demonstrating in-house AI R&D beyond client work |
| + | 330 employees and a fully remote model across the US, UK, DACH, and Poland gives wide delivery flexibility |
| - | AI and ML is one part of a much broader Python software-development practice, not the company's sole specialization |
| - | 330-person scale means less boutique-style founder involvement than smaller specialists on this list |
| - | Broad industry spread from banking to oil & gas trades vertical depth for breadth |
Who should choose InData Labs?
InData Labs is the right choice for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision.. Minimum engagement starts at Not published. Works best with clients in Cross-industry, Predictive Analytics.
Who should choose STX Next?
STX Next is the right choice for enterprises wanting Python-native ML and AI engineering from a vendor with two decades of Python specialization and certified partnerships across all three major clouds..
Built and open-sourced DeepNext, an autonomous AI developer agent, and holds AWS Advanced Tier, Snowflake, Databricks, Azure, and Amazon Bedrock partnerships simultaneously.. Minimum engagement starts at Not published. Works best with clients in Financial Services, Manufacturing, Energy & Utilities, Healthcare, Retail/E-commerce.
Decision matrix: InData Labs vs STX Next
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | STX Next |
| Your budget is at the lower end | Compare: InData Labs (Not published) vs STX Next (Not published) |
| You need specialist depth in a specific vertical | STX Next |
| You need staff augmentation or team extension | STX Next |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs STX Next
| Use case | InData Labs fit | STX Next fit | Winner |
|---|---|---|---|
| Generative AI and GPT integration projects | Strong | Limited | InData Labs |
| Predictive analytics and forecasting | Strong | Limited | InData Labs |
| Python-native ML pipeline development | Limited | Strong | STX Next |
| Multi-cloud MLOps using Databricks, Snowflake, and Bedrock | Limited | Strong | STX Next |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: InData Labs vs STX Next
InData Labs (4.4/5) is the stronger overall choice for most Machine Learning Development projects. Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision.. It is best for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
STX Next (4.0/5) is the better choice when enterprises wanting Python-native ML and AI engineering from a vendor with two decades of Python specialization and certified partnerships across all three major clouds.. If your situation matches those criteria, STX Next is a competitive option.
Related comparisons
InData Labs vs STX Next FAQ
Is InData Labs better than STX Next?
InData Labs (4.4/5) scores higher overall, but "better" depends on your use case. InData Labs is better for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. STX Next is better for enterprises wanting Python-native ML and AI engineering from a vendor with two decades of Python specialization and certified partnerships across all three major clouds..
How do InData Labs and STX Next differ in pricing?
InData Labs uses fixed project, time & materials pricing with a minimum engagement of Not published. STX Next uses fixed project, dedicated team, staff augmentation 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: 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: runs its own r&d center rather than purely project-based delivery, spanning generative ai/gpt integration through classic predictive analytics and computer vision.. STX Next's primary differentiator is: built and open-sourced deepnext, an autonomous ai developer agent, and holds aws advanced tier, snowflake, databricks, azure, and amazon bedrock partnerships simultaneously.. They also differ in team size (80+ vs 330), minimum engagement (Not published vs Not published), and primary industries served (Cross-industry, Predictive Analytics vs Financial Services, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.