Addepto vs BairesDev: full comparison for 2026
Quick verdict
Addepto (4.0/5) edges ahead of BairesDev (3.9/5) overall. Addepto is the better choice for industrial and automotive companies that need data engineers who know factory data. BairesDev is the stronger option for U.S. companies that need ML engineers alongside a larger nearshore software team. The right choice depends on your project size, budget, and required tech stack.
Addepto vs BairesDev: head-to-head summary
| Criterion | Addepto | BairesDev |
|---|---|---|
| Founded | 2017 | 2009 |
| HQ | Warsaw, Poland | San Francisco, California, USA |
| Team size | 50–249 | 4,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Data and ML engineers with industrial and automotive client history | Thousands of Latin American engineers available in U.S. time zones |
| Pricing model | Monthly per engineer or project fee; rates on request | Monthly per engineer or team; 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, Financial services, Healthcare, Retail, Media |
Addepto vs BairesDev: 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.
BairesDev
BairesDev was founded in Buenos Aires in 2009 and is now headquartered in San Francisco, with several thousand engineers across Latin America. It sells staff augmentation, dedicated teams and project delivery, and its AI practice covers ML, data engineering and generative AI. Its size means it can add many engineers quickly in U.S. time zones. AI is one practice inside a general software company, though, and its marketing volume is larger than the specialist evidence behind its AI work.
Services and capabilities: Addepto vs BairesDev
| Capability | Addepto | BairesDev |
|---|---|---|
| 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 BairesDev
| Framework / platform | Addepto | BairesDev |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | ✓ |
| Databricks | ✓ | ✓ |
| Kubernetes | N/A | N/A |
Pricing comparison: Addepto vs BairesDev
| Criterion | Addepto | BairesDev |
|---|---|---|
| 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 BairesDev
| Dimension | Addepto | BairesDev |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Aviation | Technology, Financial services, Healthcare |
| Best use cases | Adding a data engineer to an automotive analytics platform, Building a predictive maintenance model with a two-person team | Adding ML engineers to a nearshore product team, Staffing data engineers for a cloud data warehouse |
| Typical project type | Dedicated engineer | Dedicated engineer |
Addepto vs BairesDev: 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 |
| BairesDev | |
|---|---|
| + | Can staff large mixed teams of ML and software engineers |
| + | Latin American engineers work U.S. hours |
| + | Mature contracting and onboarding process |
| - | AI is one practice among many, so specialist depth varies |
| - | Screening is run at volume and not described as engineer-led for ML roles |
| - | No public 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 BairesDev?
A typical fit: adding ML engineers to a nearshore product team.
Thousands of Latin American engineers available in U.S. time zones. Minimum engagement is not publicly disclosed. Works best with clients in Technology, Financial services, Healthcare, Retail, Media.
Decision matrix: Addepto vs BairesDev
| 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 BairesDev (Not published) |
| Your team works U.S. hours | BairesDev |
| You may want to hire the engineer permanently later | Neither lists direct hire; agree conversion terms up front |
Use case fit: Addepto vs BairesDev
| Use case | Addepto fit | BairesDev 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 ML engineers to a nearshore product team | Strong | Strong | Both equally |
| Staffing data engineers for a cloud data warehouse | Strong | Strong | Both equally |
Verdict: Addepto vs BairesDev
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.
BairesDev (3.9/5) is worth a look if you need staffing data engineers for a cloud data warehouse. If your situation matches that, BairesDev is a competitive option.
Related comparisons
Addepto vs BairesDev FAQ
Is Addepto better than BairesDev?
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. BairesDev's strongest advantage: can staff large mixed teams of ML and software engineers.
How do Addepto and BairesDev differ in pricing?
Addepto uses monthly per engineer or project fee; rates on request pricing. BairesDev uses monthly per engineer or 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 BairesDev?
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 BairesDev?
Addepto's primary differentiator is: data and ML engineers with industrial and automotive client history. BairesDev's primary differentiator is: thousands of Latin American engineers available in U.S. time zones. They also differ in team size (50–249 vs 4,000+), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Automotive vs Technology, Financial services).
Verify all details directly with each company before making a decision.