micro1 vs Harnham: full comparison for 2026
Quick verdict
micro1 (3.7/5) edges ahead of Harnham (3.7/5) overall. micro1 is the better choice for AI teams that need many vetted contributors quickly for evaluation or data work. 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.
micro1 vs Harnham: head-to-head summary
| Criterion | micro1 | Harnham |
|---|---|---|
| Founded | 2022 | 2006 |
| HQ | California, USA | London, United Kingdom |
| Team size | Estimates range from 11–50 staff to thousands including contractors | 100–500 |
| Rating | 3.7 / 5 | 3.7 / 5 |
| Primary differentiator | Automated AI interviews that screen candidates in high volume | Twenty years of recruiting only in data and analytics |
| Pricing model | Hourly or monthly per contractor; one-week test option; 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, PyTorch, OpenAI | Python, SQL, Spark |
| Industries served | AI labs, Technology, SaaS, Finance, Healthcare | Financial services, Retail, Healthcare, Media, Technology |
micro1 vs Harnham: overview
micro1
micro1 was founded in 2022 by Ali Ansari and is based in California. Its AI recruiter, Zara, runs a structured interview of 20 to 40 minutes with every applicant. The company raised a Series A at a $500 million valuation in September 2025 and now earns most of its revenue supplying vetted experts to AI labs for model training. Engineering teams can still hire through it, but an automated interview is a different thing from a senior engineer reviewing code, and its focus has moved toward lab work.
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: micro1 vs Harnham
| Capability | micro1 | 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: micro1 vs Harnham
| Framework / platform | micro1 | Harnham |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | N/A | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | N/A |
| Databricks | N/A | ✓ |
| Kubernetes | N/A | N/A |
Pricing comparison: micro1 vs Harnham
| Criterion | micro1 | Harnham |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Freelance contract, Trial period | Direct hire, Contract-to-hire, Freelance contract |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: micro1 vs Harnham
| Dimension | micro1 | Harnham |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | AI labs, Technology, SaaS | Financial services, Retail, Healthcare |
| Best use cases | Hiring twenty LLM evaluators for a model release, Adding a contract ML engineer for a short project | Hiring a permanent head of data science in London, Placing a contract data engineer for six months |
| Typical project type | Freelance contract | Direct hire |
micro1 vs Harnham: pros and cons
| micro1 | |
|---|---|
| + | Can screen a very large number of candidates fast |
| + | Experience with AI-lab evaluation work |
| + | A short trial before a longer contract |
| - | AI interviews replace engineer judgment in the screen |
| - | Most revenue now comes from AI labs, not product teams |
| - | Headquarters is listed differently across sources |
| 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 micro1?
A typical fit: hiring twenty LLM evaluators for a model release.
Automated AI interviews that screen candidates in high volume. Minimum engagement is not publicly disclosed. Works best with clients in AI labs, Technology, SaaS, Finance, Healthcare.
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: micro1 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 | Neither lists dedicated teams; check team size before signing |
| You want to test an engineer before committing | micro1 |
| Your budget is at the lower end | Compare: micro1 (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: micro1 vs Harnham
| Use case | micro1 fit | Harnham fit | Winner |
|---|---|---|---|
| Hiring twenty LLM evaluators for a model release | Strong | Strong | Both equally |
| Adding a contract ML engineer for a short project | Strong | Limited | micro1 |
| Hiring a permanent head of data science in London | Strong | Strong | Both equally |
| Placing a contract data engineer for six months | Limited | Strong | Harnham |
Verdict: micro1 vs Harnham
micro1 (3.7/5) is the stronger overall choice for most AI Engineer Staffing projects. Automated AI interviews that screen candidates in high volume.
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
micro1 vs Harnham FAQ
Is micro1 better than Harnham?
micro1 (3.7/5) scores higher overall, but "better" depends on your use case. micro1's strongest advantage: can screen a very large number of candidates fast. Harnham's strongest advantage: long specialist history in data recruiting.
How do micro1 and Harnham differ in pricing?
micro1 uses hourly or monthly per contractor; one-week test option; 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: micro1 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 micro1 and Harnham?
micro1's primary differentiator is: automated AI interviews that screen candidates in high volume. Harnham's primary differentiator is: twenty years of recruiting only in data and analytics. They also differ in team size (Estimates range from 11–50 staff to thousands including contractors vs 100–500), minimum engagement (Not published vs Not published), and primary industries served (AI labs, Technology vs Financial services, Retail).
Verify all details directly with each company before making a decision.