InData Labs vs Fuzzy Labs: full comparison for 2026
Quick verdict
InData Labs (4.2/5) edges ahead of Fuzzy Labs (4.0/5) overall. InData Labs is the better choice for buyers who want an R&D-minded data science team without paying Western European rates. Fuzzy Labs is the stronger option for UK data science teams, including public sector, that need MLOps engineers working alongside them. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Fuzzy Labs: head-to-head summary
| Criterion | InData Labs | Fuzzy Labs |
|---|---|---|
| Founded | 2014 | 2019 |
| HQ | Nicosia, Cyprus | Manchester, UK |
| Team size | 50–99 (directory estimates range up to 201–500) | Under 50 (registry filing lists a micro company) |
| Rating | 4.2 / 5 | 4.0 / 5 |
| Primary differentiator | Research-led data science with a dedicated-team option | Open-source MLOps specialists with security-cleared engineers for government work |
| Pricing model | Dedicated team or project pricing; Clutch shows projects from under $50,000 to over $100,000; rates on request | Day-rate or retainer per engineer; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Kubernetes, MLflow |
| Industries served | Healthcare, Fintech, Retail, Media | Public sector & policing, Startups, Enterprise |
InData Labs vs Fuzzy Labs: overview
InData Labs
Since 2014, InData Labs has done nothing but data science and AI, and it says it has completed more than 150 projects across healthcare, fintech and retail. The company is registered in Nicosia, Cyprus, with a second office in Singapore and delivery staff in Lithuania and Poland. Dedicated teams and staff augmentation appear in its service list next to generative AI, predictive analytics and computer vision, though the firm publishes little about how those engagements are structured. Clutch reviewers praise value for money and flexibility.
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.
Services and capabilities: InData Labs vs Fuzzy Labs
| Capability | InData Labs | Fuzzy Labs |
|---|---|---|
| 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: InData Labs vs Fuzzy Labs
| Framework / platform | InData Labs | Fuzzy Labs |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | ✓ |
| Databricks | N/A | N/A |
| MLflow | N/A | ✓ |
Pricing comparison: InData Labs vs Fuzzy Labs
| Criterion | InData Labs | Fuzzy Labs |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineers, Project delivery | Embedded team, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs Fuzzy Labs
| Dimension | InData Labs | Fuzzy Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Fintech, Retail | Public sector & policing, Startups, Enterprise |
| Best use cases | Staffing a computer-vision R&D effort for a health-tech product, Adding NLP engineers to a fintech document workflow | Getting a police force's ML models into production, Adding an MLOps engineer to a startup's data science team |
| Typical project type | Dedicated engineers | Embedded team |
InData Labs vs Fuzzy Labs: pros and cons
| InData Labs | |
|---|---|
| + | 150+ completed AI projects (per company website; independently unverifiable) |
| + | Computer vision and NLP are long-standing specialties |
| + | Clutch reviewers mention flexibility when scope changes |
| - | Very little public detail on augmentation terms, team size or billing |
| - | Headcount estimates vary from about 50 to 500, so bench depth is unclear |
| - | One reviewer asked for better-prepared planning sessions |
| 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 |
Who should choose InData Labs?
A typical fit: staffing a computer-vision R&D effort for a health-tech product.
Research-led data science with a dedicated-team option. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Fintech, Retail, Media.
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.
Decision matrix: InData Labs vs Fuzzy Labs
| 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; InData 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: InData Labs (Not published) vs Fuzzy Labs (Not published) |
| You need engineers deployed inside your organization | Fuzzy Labs |
| You need specialist depth in a specific vertical | InData Labs |
Use case fit: InData Labs vs Fuzzy Labs
| Use case | InData Labs fit | Fuzzy Labs fit | Winner |
|---|---|---|---|
| Staffing a computer-vision R&D effort for a health-tech product | Strong | Limited | InData Labs |
| Adding NLP engineers to a fintech document workflow | Strong | Strong | Both equally |
| Getting a police force's ML models into production | Limited | Strong | Fuzzy Labs |
| Adding an MLOps engineer to a startup's data science team | Strong | Strong | Both equally |
Verdict: InData Labs vs Fuzzy Labs
InData Labs (4.2/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Research-led data science with a dedicated-team option.
Fuzzy Labs (4.0/5) is worth a look if you need adding an MLOps engineer to a startup's data science team. If your situation matches that, Fuzzy Labs is a competitive option.
Related comparisons
InData Labs vs Fuzzy Labs FAQ
Is InData Labs better than Fuzzy Labs?
InData Labs (4.2/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: 150+ completed AI projects (per company website; independently unverifiable). Fuzzy Labs's strongest advantage: security-cleared engineers can work in sensitive UK environments.
How do InData Labs and Fuzzy Labs differ in pricing?
InData Labs uses dedicated team or project pricing; clutch shows projects from under $50,000 to over $100,000; rates on request pricing. Fuzzy Labs uses day-rate or retainer per engineer; 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: InData Labs or Fuzzy Labs?
InData Labs 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 InData Labs and Fuzzy Labs?
InData Labs's primary differentiator is: research-led data science with a dedicated-team option. Fuzzy Labs's primary differentiator is: open-source MLOps specialists with security-cleared engineers for government work. They also differ in team size (50–99 (directory estimates range up to 201–500) vs Under 50 (registry filing lists a micro company)), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Fintech vs Public sector & policing, Startups).
Verify all details directly with each company before making a decision.