Addepto vs Coderio: full comparison for 2026
Quick verdict
Addepto (4.0/5) edges ahead of Coderio (3.8/5) overall. Addepto is the better choice for industrial and automotive companies that need data engineers who know factory data. Coderio is the stronger option for U.S. teams that need a managed nearshore squad with an ML engineer in it. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Coderio: head-to-head summary
| Criterion | Addepto | Coderio |
|---|---|---|
| Founded | 2017 | 2017 |
| HQ | Warsaw, Poland | Miami, Florida, USA |
| Team size | 50–249 | 200–250 |
| Rating | 4.0 / 5 | 3.8 / 5 |
| Primary differentiator | Data and ML engineers with industrial and automotive client history | Squads assembled within seven days, with managed delivery as an option |
| Pricing model | Monthly per engineer or project fee; rates on request | Monthly per engineer or squad; 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 | Financial services, Retail, Healthcare, Media, Technology |
Addepto vs Coderio: 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.
Coderio
Coderio was founded in 2017, is headquartered in Miami and employs around 220 people, mainly in Latin America. It supplies individual engineers or fully managed squads, which it says it can assemble within seven days, in time zones that match U.S. teams. Its AI/ML hiring page says its engineers have production experience rather than only notebook work. AI is one of several areas, and we found no detail on who runs its technical screens.
Services and capabilities: Addepto vs Coderio
| Capability | Addepto | Coderio |
|---|---|---|
| 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 Coderio
| Framework / platform | Addepto | Coderio |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | ✓ |
| Databricks | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Addepto vs Coderio
| Criterion | Addepto | Coderio |
|---|---|---|
| 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 Coderio
| Dimension | Addepto | Coderio |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Aviation | Financial services, Retail, Healthcare |
| Best use cases | Adding a data engineer to an automotive analytics platform, Building a predictive maintenance model with a two-person team | Building a nearshore squad with one ML engineer, Adding data engineers to a retail analytics team |
| Typical project type | Dedicated engineer | Dedicated engineer |
Addepto vs Coderio: 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 |
| Coderio | |
|---|---|
| + | Fast squad assembly |
| + | U.S. time-zone overlap |
| + | Can manage the squad if you lack a lead |
| - | General software firm with AI as one area |
| - | No published detail on technical screening |
| - | 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 Coderio?
A typical fit: building a nearshore squad with one ML engineer.
Squads assembled within seven days, with managed delivery as an option. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Retail, Healthcare, Media, Technology.
Decision matrix: Addepto vs Coderio
| 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 Coderio (Not published) |
| Your team works U.S. hours | Coderio |
| You may want to hire the engineer permanently later | Neither lists direct hire; agree conversion terms up front |
Use case fit: Addepto vs Coderio
| Use case | Addepto fit | Coderio 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 |
| Building a nearshore squad with one ML engineer | Strong | Strong | Both equally |
| Adding data engineers to a retail analytics team | Strong | Strong | Both equally |
Verdict: Addepto vs Coderio
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.
Coderio (3.8/5) is worth a look if you need adding data engineers to a retail analytics team. If your situation matches that, Coderio is a competitive option.
Related comparisons
Addepto vs Coderio FAQ
Is Addepto better than Coderio?
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. Coderio's strongest advantage: fast squad assembly.
How do Addepto and Coderio differ in pricing?
Addepto uses monthly per engineer or project fee; rates on request pricing. Coderio uses monthly per engineer or squad; 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 Coderio?
Coderio 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 Coderio?
Addepto's primary differentiator is: data and ML engineers with industrial and automotive client history. Coderio's primary differentiator is: squads assembled within seven days, with managed delivery as an option. They also differ in team size (50–249 vs 200–250), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Automotive vs Financial services, Retail).
Verify all details directly with each company before making a decision.