Quantiphi vs Addepto: full comparison for 2026
Quick verdict
Quantiphi (4.3/5) edges ahead of Addepto (4.0/5) overall. Quantiphi is the better choice for enterprises that need many AI roles filled at once by one AI-only supplier. Addepto is the stronger option for industrial and automotive companies that need data engineers who know factory data. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs Addepto: head-to-head summary
| Criterion | Quantiphi | Addepto |
|---|---|---|
| Founded | 2013 | 2017 |
| HQ | Marlborough, Massachusetts, USA | Warsaw, Poland |
| Team size | 3,000–4,000+ | 50–249 |
| Rating | 4.3 / 5 | 4.0 / 5 |
| Primary differentiator | The biggest AI-only bench here, sold through a named staffing program with AWS | Data and ML engineers with industrial and automotive client history |
| Pricing model | Elastic Staffing billed per specialist; consulting quoted separately; rates on request | Monthly per engineer or project fee; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Databricks, Spark |
| Industries served | Healthcare, Financial services, Energy, Retail, Media | Manufacturing, Automotive, Aviation, Retail, Logistics |
Quantiphi vs Addepto: overview
Quantiphi
Quantiphi, based in Marlborough, Massachusetts and founded in 2013, is the largest company on this page that works only on AI and data, with directory estimates between 3,000 and more than 4,000 people. Its Elastic Staffing program, built with AWS, places generative AI and ML specialists into client teams. That scale is the reason it ranks here: no other AI-only supplier can staff ML, MLOps, data and LLM roles in parallel. Google Cloud named it 2025 AI Partner of the Year for North America. The cost is attention, since staffing is one product inside a large consulting business.
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.
Services and capabilities: Quantiphi vs Addepto
| Capability | Quantiphi | Addepto |
|---|---|---|
| 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: Quantiphi vs Addepto
| Framework / platform | Quantiphi | Addepto |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | ✓ | N/A |
| Databricks | ✓ | ✓ |
| Kubernetes | ✓ | N/A |
Pricing comparison: Quantiphi vs Addepto
| Criterion | Quantiphi | Addepto |
|---|---|---|
| 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: Quantiphi vs Addepto
| Dimension | Quantiphi | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Financial services, Energy | Manufacturing, Automotive, Aviation |
| Best use cases | Staffing eight GenAI specialists into an enterprise program, Adding Vertex AI or SageMaker engineers for a cloud ML migration | Adding a data engineer to an automotive analytics platform, Building a predictive maintenance model with a two-person team |
| Typical project type | Dedicated engineer | Dedicated engineer |
Quantiphi vs Addepto: pros and cons
| Quantiphi | |
|---|---|
| + | Can staff several AI specialties in parallel, which no other AI-only firm here can |
| + | Top partner tiers with Google Cloud and AWS help on cloud-specific ML roles |
| + | A named staffing product makes procurement simpler |
| - | Requests for one or two engineers compete with large consulting programs |
| - | Rates appear only after scoping |
| - | Headcount estimates vary widely between sources |
| 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 |
Who should choose Quantiphi?
A typical fit: staffing eight GenAI specialists into an enterprise program.
The biggest AI-only bench here, sold through a named staffing program with AWS. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Energy, Retail, Media.
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.
Decision matrix: Quantiphi vs Addepto
| 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; Quantiphi 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: Quantiphi (Not published) vs Addepto (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: Quantiphi vs Addepto
| Use case | Quantiphi fit | Addepto fit | Winner |
|---|---|---|---|
| Staffing eight GenAI specialists into an enterprise program | Strong | Strong | Both equally |
| Adding Vertex AI or SageMaker engineers for a cloud ML migration | Strong | Strong | Both equally |
| Adding a data engineer to an automotive analytics platform | Strong | Strong | Both equally |
| Building a predictive maintenance model with a two-person team | Limited | Strong | Addepto |
Verdict: Quantiphi vs Addepto
Quantiphi (4.3/5) is the stronger overall choice for most AI Engineer Staffing projects. The biggest AI-only bench here, sold through a named staffing program with AWS.
Addepto (4.0/5) is worth a look if you need building a predictive maintenance model with a two-person team. If your situation matches that, Addepto is a competitive option.
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Quantiphi vs Addepto FAQ
Is Quantiphi better than Addepto?
Quantiphi (4.3/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: can staff several AI specialties in parallel, which no other AI-only firm here can. Addepto's strongest advantage: strong data engineering on Databricks and Azure.
How do Quantiphi and Addepto differ in pricing?
Quantiphi uses elastic staffing billed per specialist; consulting quoted separately; rates on request pricing. Addepto uses monthly per engineer or project 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: Quantiphi or Addepto?
Quantiphi 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 Quantiphi and Addepto?
Quantiphi's primary differentiator is: the biggest AI-only bench here, sold through a named staffing program with AWS. Addepto's primary differentiator is: data and ML engineers with industrial and automotive client history. They also differ in team size (3,000–4,000+ vs 50–249), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Financial services vs Manufacturing, Automotive).
Verify all details directly with each company before making a decision.