Addepto vs Qubit Labs: full comparison for 2026
Quick verdict
Addepto (4.0/5) edges ahead of Qubit Labs (3.7/5) overall. Addepto is the better choice for industrial and automotive companies that need data engineers who know factory data. Qubit Labs is the stronger option for cost-conscious teams that can write a precise brief for an Eastern European ML hire. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Qubit Labs: head-to-head summary
| Criterion | Addepto | Qubit Labs |
|---|---|---|
| Founded | 2017 | 2016 |
| HQ | Warsaw, Poland | Kyiv, Ukraine |
| Team size | 50–249 | 50–100 |
| Rating | 4.0 / 5 | 3.7 / 5 |
| Primary differentiator | Data and ML engineers with industrial and automotive client history | Recruiting across several lower-cost Eastern European countries |
| Pricing model | Monthly per engineer or project fee; rates on request | Monthly per engineer with a service fee; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Databricks, Spark | Python, TensorFlow, PyTorch |
| Industries served | Manufacturing, Automotive, Aviation, Retail, Logistics | Technology, Fintech, E-commerce, Gaming, Healthcare |
Addepto vs Qubit Labs: 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.
Qubit Labs
Qubit Labs launched in 2016 as a Ukrainian IT outstaffing company and is now listed with headquarters in Tallinn or Kyiv depending on the source. It builds remote dedicated teams in Ukraine, Poland, Moldova, Georgia, Romania and other countries, and in recent years it has added AI staff augmentation and deep tech recruiting. Screening is recruiter-led. The firm is a practical option for cost-conscious teams that know exactly what they want, but it has less proven ML depth than AI-only suppliers.
Services and capabilities: Addepto vs Qubit Labs
| Capability | Addepto | Qubit Labs |
|---|---|---|
| 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 Qubit Labs
| Framework / platform | Addepto | Qubit Labs |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Databricks | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Addepto vs Qubit Labs
| Criterion | Addepto | Qubit Labs |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineer, Dedicated team, Project delivery | Dedicated engineer, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Addepto vs Qubit Labs
| Dimension | Addepto | Qubit Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Aviation | Technology, Fintech, E-commerce |
| Best use cases | Adding a data engineer to an automotive analytics platform, Building a predictive maintenance model with a two-person team | Hiring a Python ML engineer in Poland or Romania, Building a remote data team outside Ukraine |
| Typical project type | Dedicated engineer | Dedicated engineer |
Addepto vs Qubit Labs: 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 |
| Qubit Labs | |
|---|---|
| + | Hires in several countries, not only Ukraine |
| + | Lower cost than Western European suppliers |
| + | Clients say shortlists arrive quickly |
| - | Recruiter-led screening for technical roles |
| - | AI staffing is a recent addition |
| - | Headquarters listed differently across sources |
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 Qubit Labs?
A typical fit: hiring a Python ML engineer in Poland or Romania.
Recruiting across several lower-cost Eastern European countries. Minimum engagement is not publicly disclosed. Works best with clients in Technology, Fintech, E-commerce, Gaming, Healthcare.
Decision matrix: Addepto vs Qubit Labs
| 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 Qubit Labs (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 Qubit Labs
| Use case | Addepto fit | Qubit Labs 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 | Strong | Both equally |
| Hiring a Python ML engineer in Poland or Romania | Limited | Strong | Qubit Labs |
| Building a remote data team outside Ukraine | Strong | Strong | Both equally |
Verdict: Addepto vs Qubit Labs
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.
Qubit Labs (3.7/5) is worth a look if you need building a remote data team outside Ukraine. If your situation matches that, Qubit Labs is a competitive option.
Related comparisons
Addepto vs Qubit Labs FAQ
Is Addepto better than Qubit Labs?
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. Qubit Labs's strongest advantage: hires in several countries, not only Ukraine.
How do Addepto and Qubit Labs differ in pricing?
Addepto uses monthly per engineer or project fee; rates on request pricing. Qubit Labs uses monthly per engineer with a service fee; 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 Qubit Labs?
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 Qubit Labs?
Addepto's primary differentiator is: data and ML engineers with industrial and automotive client history. Qubit Labs's primary differentiator is: recruiting across several lower-cost Eastern European countries. They also differ in team size (50–249 vs 50–100), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Automotive vs Technology, Fintech).
Verify all details directly with each company before making a decision.