Addepto vs N-iX: full comparison for 2026
Quick verdict
Addepto (4.0/5) edges ahead of N-iX (3.9/5) overall. Addepto is the better choice for industrial and automotive companies that need data engineers who know factory data. N-iX is the stronger option for large companies that want ML and data engineers from an established Central European supplier. The right choice depends on your project size, budget, and required tech stack.
Addepto vs N-iX: head-to-head summary
| Criterion | Addepto | N-iX |
|---|---|---|
| Founded | 2017 | 2002 |
| HQ | Warsaw, Poland | Valletta, Malta (delivery mainly in Ukraine and Poland) |
| Team size | 50–249 | 2,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Data and ML engineers with industrial and automotive client history | Scale and two decades of history in Central European delivery |
| Pricing model | Monthly per engineer or project fee; rates on request | Monthly per engineer or managed team; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Databricks, Spark | Python, Spark, Databricks |
| Industries served | Manufacturing, Automotive, Aviation, Retail, Logistics | Financial services, Manufacturing, Retail, Telecom, Healthcare |
Addepto vs N-iX: overview
Addepto
Addepto was founded in Warsaw in 2017 and works on AI, ML and data engineering, mostly for industrial and automotive clients. KMS Technology acquired it in December 2025, so it now sits inside a larger U.S.-based IT group. Addepto supplies data and ML engineers for team extension as well as running projects. The acquisition may widen its bench over time, but buyers should expect changes to contracts and account management as the integration proceeds.
N-iX
N-iX has been in business since 2002, has its registered headquarters in Malta and does most of its delivery from Ukraine, Poland and other Central European countries. Company materials cite more than 2,400 engineers and staff augmentation as one of three engagement models. It is hiring ML engineers in 2026, and one listing seeks a lead computer-vision engineer for an external expert network that conducts technical interviews, which suggests specialists take part in its screening for senior roles. The firm is large and stable, but ML is a fraction of its work.
Services and capabilities: Addepto vs N-iX
| Capability | Addepto | N-iX |
|---|---|---|
| ML engineers | ✓ | ✓ |
| LLM / GenAI engineers | ✓ | ✗ |
| AI agent developers | ✗ | ✗ |
| MLOps engineers | ✗ | ✓ |
| Computer vision engineers | ✗ | ✓ |
| NLP engineers | ✗ | ✗ |
| Data engineers | ✓ | ✓ |
| Engineer-led technical screen | ✗ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
| Direct hire option | ✗ | ✗ |
Tech stack comparison: Addepto vs N-iX
| Framework / platform | Addepto | N-iX |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | ✓ |
| Databricks | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: Addepto vs N-iX
| Criterion | Addepto | N-iX |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineer, Dedicated team, Project delivery | Dedicated engineer, Dedicated team, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Addepto vs N-iX
| Dimension | Addepto | N-iX |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Aviation | Financial services, Manufacturing, Retail |
| Best use cases | Adding a data engineer to an automotive analytics platform, Building a predictive maintenance model with a two-person team | Adding a data engineering team to an enterprise data platform, Staffing a computer-vision engineer for a manufacturing client |
| Typical project type | Dedicated engineer | Dedicated engineer |
Addepto vs N-iX: pros and cons
| Addepto | |
|---|---|
| + | Strong data engineering on Databricks and Azure |
| + | Industrial and automotive references |
| + | Backing from a larger group may add capacity |
| - | Acquired by KMS Technology in December 2025, so terms and contacts may change |
| - | Fewer computer-vision and NLP specialists than AI-research firms |
| - | Staffing evidence is thinner than its project work |
| N-iX | |
|---|---|
| + | Large bench across several Central European countries |
| + | Uses outside specialists to interview for senior technical roles |
| + | Long history with enterprise clients |
| - | ML is a small part of a general software business |
| - | Headquarters is listed differently across sources |
| - | No published rates |
Who should choose Addepto?
A typical fit: adding a data engineer to an automotive analytics platform.
Data and ML engineers with industrial and automotive client history. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Automotive, Aviation, Retail, Logistics.
Who should choose N-iX?
A typical fit: adding a data engineering team to an enterprise data platform.
Scale and two decades of history in Central European delivery. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Manufacturing, Retail, Telecom, Healthcare.
Decision matrix: Addepto vs N-iX
| Your situation | Recommended choice |
|---|---|
| You want a working engineer, not a recruiter, to run the technical screen | Neither documents an engineer-led screen; run your own technical interview |
| You need one specialist for a few days a week | Neither advertises part-time experts; ask about reduced hours |
| You need several engineers working as one team | Both; Addepto rates higher overall |
| You want to test an engineer before committing | Neither publishes a trial; negotiate a short first term |
| Your budget is at the lower end | Compare: Addepto (Not published) vs N-iX (Not published) |
| Your team works U.S. hours | Neither lists Latin American engineers; confirm overlap hours in the contract |
| You may want to hire the engineer permanently later | Neither lists direct hire; agree conversion terms up front |
Use case fit: Addepto vs N-iX
| Use case | Addepto fit | N-iX fit | Winner |
|---|---|---|---|
| Adding a data engineer to an automotive analytics platform | Strong | Strong | Both equally |
| Building a predictive maintenance model with a two-person team | Strong | Limited | Addepto |
| Adding a data engineering team to an enterprise data platform | Strong | Strong | Both equally |
| Staffing a computer-vision engineer for a manufacturing client | Strong | Strong | Both equally |
Verdict: Addepto vs N-iX
Addepto (4.0/5) is the stronger overall choice for most AI Engineer Staffing projects. Data and ML engineers with industrial and automotive client history.
N-iX (3.9/5) is worth a look if you need staffing a computer-vision engineer for a manufacturing client. If your situation matches that, N-iX is a competitive option.
Related comparisons
Addepto vs N-iX FAQ
Is Addepto better than N-iX?
Addepto (4.0/5) scores higher overall, but "better" depends on your use case. Addepto's strongest advantage: strong data engineering on Databricks and Azure. N-iX's strongest advantage: large bench across several Central European countries.
How do Addepto and N-iX differ in pricing?
Addepto uses monthly per engineer or project fee; rates on request pricing. N-iX uses monthly per engineer or managed team; 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: Addepto or N-iX?
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 company before shortlisting.
What are the main differences between Addepto and N-iX?
Addepto's primary differentiator is: data and ML engineers with industrial and automotive client history. N-iX's primary differentiator is: scale and two decades of history in Central European delivery. They also differ in team size (50–249 vs 2,000+), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Automotive vs Financial services, Manufacturing).
Verify all details directly with each company before making a decision.