Fuzzy Labs vs Addepto: full comparison for 2026
Quick verdict
Fuzzy Labs (4.0/5) edges ahead of Addepto (3.9/5) overall. Fuzzy Labs is the better choice for UK data science teams, including public sector, that need MLOps engineers working alongside them. Addepto is the stronger option for industrial and automotive companies adding AI and data engineers to an internal team. The right choice depends on your project size, budget, and required tech stack.
Fuzzy Labs vs Addepto: head-to-head summary
| Criterion | Fuzzy Labs | Addepto |
|---|---|---|
| Founded | 2019 | 2017 |
| HQ | Manchester, UK | Warsaw, Poland |
| Team size | Under 50 (registry filing lists a micro company) | 50–99 (directory estimate) |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Open-source MLOps specialists with security-cleared engineers for government work | AI-heavy team with manufacturing domain experience, now backed by a larger group |
| Pricing model | Day-rate or retainer per engineer; rates on request | Collaborative team model or managed delivery; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Kubernetes, MLflow | Python, Databricks, Spark |
| Industries served | Public sector & policing, Startups, Enterprise | Manufacturing, Automotive, Retail, Aviation |
Fuzzy Labs vs Addepto: overview
Fuzzy Labs
Fuzzy Labs is a small MLOps consultancy incorporated in January 2019 and based at the GM Digital Security Hub in Manchester. It works side by side with data science teams to get models into production with less technical debt, describing itself as the client's in-house MLOps team and an extension of that team. Clients range from startups to policing and secure government work, and some roles require UK security clearance. The company says it doubled revenue in its most recent year and runs a fellowship to train new MLOps engineers.
Addepto
Addepto has worked on AI and data in Warsaw since 2017, with a strong client base in industrial and automotive companies. KMS Technology, an Atlanta engineering firm backed by Sunstone Partners, acquired it in December 2025. Its collaborative cooperation model puts Addepto engineers alongside the client's own team, and the company has said publicly it is not a body-leasing firm. After the deal, its CEO said 97% of the team are AI engineers.
Services and capabilities: Fuzzy Labs vs Addepto
| Capability | Fuzzy Labs | Addepto |
|---|---|---|
| LLM / GenAI engineers | ✗ | ✓ |
| AI agent development | ✗ | ✗ |
| MLOps & deployment | ✓ | ✗ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| Fractional / part-time experts | ✗ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Forward-deployed engineers | ✗ | ✗ |
| Access to a wider AI talent network | ✗ | ✗ |
Tech stack comparison: Fuzzy Labs vs Addepto
| Framework / platform | Fuzzy Labs | Addepto |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Databricks | N/A | ✓ |
| MLflow | ✓ | N/A |
Pricing comparison: Fuzzy Labs vs Addepto
| Criterion | Fuzzy Labs | Addepto |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Embedded team, Project delivery | Embedded team, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Fuzzy Labs vs Addepto
| Dimension | Fuzzy Labs | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Public sector & policing, Startups, Enterprise | Manufacturing, Automotive, Retail |
| Best use cases | Getting a police force's ML models into production, Adding an MLOps engineer to a startup's data science team | Adding Databricks engineers to a manufacturer's data team, Building a GenAI assistant for automotive service documents |
| Typical project type | Embedded team | Embedded team |
Fuzzy Labs vs Addepto: pros and cons
| Fuzzy Labs | |
|---|---|
| + | Security-cleared engineers can work in sensitive UK environments |
| + | Open-source tooling choices keep you free of vendor-specific platforms |
| + | Small team means you work directly with senior people |
| - | Very small; registry data lists eight employees, though the firm is hiring |
| - | MLOps only, so data scientists and LLM application developers come from elsewhere |
| - | UK-centric; limited overlap for U.S. or Asian teams |
| Addepto | |
|---|---|
| + | Nearly the whole team is AI engineers, according to its CEO |
| + | Industrial and automotive client experience |
| + | KMS ownership adds broader engineering capacity behind it |
| - | Acquired by KMS Technology in December 2025; ownership changes can bring new contract terms |
| - | Prefers joint delivery to straight staff placement |
| - | Team size estimates range from 8 to 99 |
Who should choose Fuzzy Labs?
A typical fit: getting a police force's ML models into production.
Open-source MLOps specialists with security-cleared engineers for government work. Minimum engagement is not publicly disclosed. Works best with clients in Public sector & policing, Startups, Enterprise.
Who should choose Addepto?
A typical fit: adding Databricks engineers to a manufacturer's data team.
AI-heavy team with manufacturing domain experience, now backed by a larger group. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Automotive, Retail, Aviation.
Decision matrix: Fuzzy Labs vs Addepto
| Your situation | Recommended choice |
|---|---|
| You need one AI specialist part-time | Neither advertises part-time experts; ask about reduced hours |
| You need several engineers working as one team | Both; Fuzzy Labs 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: Fuzzy Labs (Not published) vs Addepto (Not published) |
| You need engineers deployed inside your organization | Both; Fuzzy Labs rates higher overall |
| You need specialist depth in a specific vertical | Addepto |
Use case fit: Fuzzy Labs vs Addepto
| Use case | Fuzzy Labs fit | Addepto fit | Winner |
|---|---|---|---|
| Getting a police force's ML models into production | Strong | Limited | Fuzzy Labs |
| Adding an MLOps engineer to a startup's data science team | Strong | Strong | Both equally |
| Adding Databricks engineers to a manufacturer's data team | Strong | Strong | Both equally |
| Building a GenAI assistant for automotive service documents | Limited | Strong | Addepto |
Verdict: Fuzzy Labs vs Addepto
Fuzzy Labs (4.0/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Open-source MLOps specialists with security-cleared engineers for government work.
Addepto (3.9/5) is worth a look if you need building a GenAI assistant for automotive service documents. If your situation matches that, Addepto is a competitive option.
Related comparisons
Fuzzy Labs vs Addepto FAQ
Is Fuzzy Labs better than Addepto?
Fuzzy Labs (4.0/5) scores higher overall, but "better" depends on your use case. Fuzzy Labs's strongest advantage: security-cleared engineers can work in sensitive UK environments. Addepto's strongest advantage: nearly the whole team is AI engineers, according to its CEO.
How do Fuzzy Labs and Addepto differ in pricing?
Fuzzy Labs uses day-rate or retainer per engineer; rates on request pricing. Addepto uses collaborative team model or managed delivery; 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: Fuzzy Labs or Addepto?
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 Fuzzy Labs and Addepto?
Fuzzy Labs's primary differentiator is: open-source MLOps specialists with security-cleared engineers for government work. Addepto's primary differentiator is: AI-heavy team with manufacturing domain experience, now backed by a larger group. They also differ in team size (Under 50 (registry filing lists a micro company) vs 50–99 (directory estimate)), minimum engagement (Not published vs Not published), and primary industries served (Public sector & policing, Startups vs Manufacturing, Automotive).
Verify all details directly with each company before making a decision.