Addepto vs Harnham: full comparison for 2026
Quick verdict
Addepto (4.0/5) edges ahead of Harnham (3.7/5) overall. Addepto is the better choice for industrial and automotive companies that need data engineers who know factory data. Harnham is the stronger option for companies hiring permanent data or ML staff in the UK or U.S. through a specialist agency. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Harnham: head-to-head summary
| Criterion | Addepto | Harnham |
|---|---|---|
| Founded | 2017 | 2006 |
| HQ | Warsaw, Poland | London, United Kingdom |
| Team size | 50–249 | 100–500 |
| Rating | 4.0 / 5 | 3.7 / 5 |
| Primary differentiator | Data and ML engineers with industrial and automotive client history | Twenty years of recruiting only in data and analytics |
| Pricing model | Monthly per engineer or project fee; rates on request | Placement fee for permanent hires; contractor day or hourly rates; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Databricks, Spark | Python, SQL, Spark |
| Industries served | Manufacturing, Automotive, Aviation, Retail, Logistics | Financial services, Retail, Healthcare, Media, Technology |
Addepto vs Harnham: 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.
Harnham
Harnham has recruited for data and analytics roles since 2006 from London, with offices in the U.S. including New York and San Francisco. It places data engineers, data scientists and ML engineers on contract or permanent terms and runs a graduate training arm, Rockborne. As a recruitment agency, it screens through consultants who specialise in data hiring rather than through practising engineers, and contractors are not managed after placement the way a staffing firm's employees are. That makes it better for permanent hires than for managed augmentation.
Services and capabilities: Addepto vs Harnham
| Capability | Addepto | Harnham |
|---|---|---|
| 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 Harnham
| Framework / platform | Addepto | Harnham |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | N/A | 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 | ✓ | ✓ |
| Kubernetes | N/A | N/A |
Pricing comparison: Addepto vs Harnham
| Criterion | Addepto | Harnham |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineer, Dedicated team, Project delivery | Direct hire, Contract-to-hire, Freelance contract |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Addepto vs Harnham
| Dimension | Addepto | Harnham |
|---|---|---|
| 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 | Hiring a permanent head of data science in London, Placing a contract data engineer for six months |
| Typical project type | Dedicated engineer | Direct hire |
Addepto vs Harnham: 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 |
| Harnham | |
|---|---|
| + | Long specialist history in data recruiting |
| + | Offices in the UK and several U.S. cities |
| + | Both contract and permanent hiring |
| - | Screening by recruitment consultants, not engineers |
| - | Contractors are not managed after placement |
| - | Headcount estimates vary |
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 Harnham?
A typical fit: hiring a permanent head of data science in London.
Twenty years of recruiting only in data and analytics. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Retail, Healthcare, Media, Technology.
Decision matrix: Addepto vs Harnham
| 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 | Addepto |
| 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 Harnham (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 | Harnham |
Use case fit: Addepto vs Harnham
| Use case | Addepto fit | Harnham fit | Winner |
|---|---|---|---|
| Adding a data engineer to an automotive analytics platform | Strong | Limited | Addepto |
| Building a predictive maintenance model with a two-person team | Strong | Limited | Addepto |
| Hiring a permanent head of data science in London | Limited | Strong | Harnham |
| Placing a contract data engineer for six months | Limited | Strong | Harnham |
Verdict: Addepto vs Harnham
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.
Harnham (3.7/5) is worth a look if you need placing a contract data engineer for six months. If your situation matches that, Harnham is a competitive option.
Related comparisons
Addepto vs Harnham FAQ
Is Addepto better than Harnham?
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. Harnham's strongest advantage: long specialist history in data recruiting.
How do Addepto and Harnham differ in pricing?
Addepto uses monthly per engineer or project fee; rates on request pricing. Harnham uses placement fee for permanent hires; contractor day or hourly rates; 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 Harnham?
Harnham 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 Harnham?
Addepto's primary differentiator is: data and ML engineers with industrial and automotive client history. Harnham's primary differentiator is: twenty years of recruiting only in data and analytics. They also differ in team size (50–249 vs 100–500), 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.