InData Labs vs Addepto: full comparison for 2026
Quick verdict
InData Labs (4.2/5) edges ahead of Addepto (3.9/5) overall. InData Labs is the better choice for buyers who want an R&D-minded data science team without paying Western European rates. 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.
InData Labs vs Addepto: head-to-head summary
| Criterion | InData Labs | Addepto |
|---|---|---|
| Founded | 2014 | 2017 |
| HQ | Nicosia, Cyprus | Warsaw, Poland |
| Team size | 50–99 (directory estimates range up to 201–500) | 50–99 (directory estimate) |
| Rating | 4.2 / 5 | 3.9 / 5 |
| Primary differentiator | Research-led data science with a dedicated-team option | AI-heavy team with manufacturing domain experience, now backed by a larger group |
| Pricing model | Dedicated team or project pricing; Clutch shows projects from under $50,000 to over $100,000; rates on request | Collaborative team model or managed delivery; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Databricks, Spark |
| Industries served | Healthcare, Fintech, Retail, Media | Manufacturing, Automotive, Retail, Aviation |
InData Labs vs Addepto: 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.
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: InData Labs vs Addepto
| Capability | InData 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: InData Labs vs Addepto
| Framework / platform | InData Labs | Addepto |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | ✓ | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
Pricing comparison: InData Labs vs Addepto
| Criterion | InData Labs | Addepto |
|---|---|---|
| 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 Addepto
| Dimension | InData Labs | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Fintech, Retail | Manufacturing, Automotive, Retail |
| Best use cases | Staffing a computer-vision R&D effort for a health-tech product, Adding NLP engineers to a fintech document workflow | Adding Databricks engineers to a manufacturer's data team, Building a GenAI assistant for automotive service documents |
| Typical project type | Dedicated engineers | Embedded team |
InData Labs vs Addepto: 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 |
| 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 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 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: InData 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; 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 Addepto (Not published) |
| You need engineers deployed inside your organization | Addepto |
| You need specialist depth in a specific vertical | InData Labs |
Use case fit: InData Labs vs Addepto
| Use case | InData Labs fit | Addepto 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 |
| 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: InData Labs vs Addepto
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.
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.
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InData Labs vs Addepto FAQ
Is InData Labs better than Addepto?
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). Addepto's strongest advantage: nearly the whole team is AI engineers, according to its CEO.
How do InData Labs and Addepto 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. 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: InData Labs or Addepto?
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 Addepto?
InData Labs's primary differentiator is: research-led data science with a dedicated-team option. Addepto's primary differentiator is: AI-heavy team with manufacturing domain experience, now backed by a larger group. They also differ in team size (50–99 (directory estimates range up to 201–500) vs 50–99 (directory estimate)), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Fintech vs Manufacturing, Automotive).
Verify all details directly with each company before making a decision.