Addepto vs Azumo: full comparison for 2026
Quick verdict
Addepto (4.0/5) edges ahead of Azumo (3.9/5) overall. Addepto is the better choice for industrial and automotive companies that need data engineers who know factory data. Azumo is the stronger option for U.S. teams on a tight budget that need ML engineers who work their hours. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Azumo: head-to-head summary
| Criterion | Addepto | Azumo |
|---|---|---|
| Founded | 2017 | 2016 |
| HQ | Warsaw, Poland | San Francisco, California, USA |
| Team size | 50–249 | 50–249 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Data and ML engineers with industrial and automotive client history | The lowest published hourly band on this page with full U.S. time-zone overlap |
| Pricing model | Monthly per engineer or project fee; rates on request | $25–$49/hr (Clutch band); monthly staff augmentation or dedicated team |
| Min. engagement | Not published | $10,000+ |
| Primary tech stack | Python, Databricks, Spark | Python, PyTorch, TensorFlow |
| Industries served | Manufacturing, Automotive, Aviation, Retail, Logistics | SaaS, Fintech, Healthcare, Retail, Media |
Addepto vs Azumo: 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.
Azumo
Azumo was founded in 2016, is headquartered in San Francisco and delivers mostly from Argentina, with an office in Rosario. Clutch lists an hourly band of $25 to $49 and a $10,000 minimum project, the lowest published rate on this page. It offers staff augmentation, dedicated nearshore teams and virtual CTO services, and it won a Clutch award as a top AI developer in 2023. Its engineers keep U.S. hours, so daily stand-ups are easy. The catch is depth. AI is a strong practice but one of several, and specialist experience varies by role.
Services and capabilities: Addepto vs Azumo
| Capability | Addepto | Azumo |
|---|---|---|
| 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 Azumo
| Framework / platform | Addepto | Azumo |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | ✓ |
| Databricks | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Addepto vs Azumo
| Criterion | Addepto | Azumo |
|---|---|---|
| Minimum engagement | Not published | $10,000+ |
| Engagement models | Dedicated engineer, Dedicated team, Project delivery | Dedicated engineer, Dedicated team, Project delivery |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: Addepto vs Azumo
| Dimension | Addepto | Azumo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Aviation | SaaS, Fintech, Healthcare |
| Best use cases | Adding a data engineer to an automotive analytics platform, Building a predictive maintenance model with a two-person team | Adding a nearshore LLM engineer to a U.S. SaaS team, Building a data engineering squad on a startup budget |
| Typical project type | Dedicated engineer | Dedicated engineer |
Addepto vs Azumo: 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 |
| Azumo | |
|---|---|
| + | Published hourly band is the lowest on this list |
| + | Argentina shares working hours with U.S. teams |
| + | Clutch cost rating of 4.8 |
| - | AI is one of several practices, not the whole company |
| - | Fewer research-grade ML specialists than AI-only firms |
| - | Team size reported between 50 and 500 depending on the source |
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 Azumo?
A typical fit: adding a nearshore LLM engineer to a U.S. SaaS team.
The lowest published hourly band on this page with full U.S. time-zone overlap. Minimum engagement starts at $10,000+. Works best with clients in SaaS, Fintech, Healthcare, Retail, Media.
Decision matrix: Addepto vs Azumo
| 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 Azumo ($10,000+) |
| Your team works U.S. hours | Azumo |
| You may want to hire the engineer permanently later | Neither lists direct hire; agree conversion terms up front |
Use case fit: Addepto vs Azumo
| Use case | Addepto fit | Azumo 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 |
| Adding a nearshore LLM engineer to a U.S. SaaS team | Strong | Strong | Both equally |
| Building a data engineering squad on a startup budget | Strong | Strong | Both equally |
Verdict: Addepto vs Azumo
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.
Azumo (3.9/5) is worth a look if you need building a data engineering squad on a startup budget. If your situation matches that, Azumo is a competitive option.
Related comparisons
Addepto vs Azumo FAQ
Is Addepto better than Azumo?
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. Azumo's strongest advantage: published hourly band is the lowest on this list.
How do Addepto and Azumo differ in pricing?
Addepto uses monthly per engineer or project fee; rates on request pricing. Azumo uses $25–$49/hr (clutch band); monthly staff augmentation or dedicated team pricing with a minimum engagement of $10,000+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Azumo?
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 Azumo?
Addepto's primary differentiator is: data and ML engineers with industrial and automotive client history. Azumo's primary differentiator is: the lowest published hourly band on this page with full U.S. time-zone overlap. They also differ in team size (50–249 vs 50–249), minimum engagement (Not published vs $10,000+), and primary industries served (Manufacturing, Automotive vs SaaS, Fintech).
Verify all details directly with each company before making a decision.