Addepto vs Sciforce: full comparison for 2026
Quick verdict
Addepto (3.9/5) edges ahead of Sciforce (3.9/5) overall. Addepto is the better choice for industrial and automotive companies adding AI and data engineers to an internal team. Sciforce is the stronger option for healthcare and scientific data projects that need NLP or medical data skills. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Sciforce: head-to-head summary
| Criterion | Addepto | Sciforce |
|---|---|---|
| Founded | 2017 | 2015 |
| HQ | Warsaw, Poland | Lviv, Ukraine |
| Team size | 50–99 (directory estimate) | 40+ specialists (per company; may be dated) |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Primary differentiator | AI-heavy team with manufacturing domain experience, now backed by a larger group | Medical and scientific data experience in a small AI-first firm |
| Pricing model | Collaborative team model or managed delivery; rates on request | Monthly per engineer for augmentation; project pricing otherwise; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Databricks, Spark | Python, PyTorch, TensorFlow |
| Industries served | Manufacturing, Automotive, Retail, Aviation | Healthcare, Financial services, Logistics, Sports & media |
Addepto vs Sciforce: overview
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.
Sciforce
Sciforce was founded in 2015 with R&D offices in Lviv and Kharkiv and a representative office in Tallinn. Its teams cover AI and ML, NLP, computer vision and medical data science, and the company puts weight on ethical AI development. One Clutch reviewer, a Stockholm financial services firm, describes a staff augmentation engagement that ran from 2019 to 2023, with Sciforce recruiting and placing engineers for the client.
Services and capabilities: Addepto vs Sciforce
| Capability | Addepto | Sciforce |
|---|---|---|
| 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: Addepto vs Sciforce
| Framework / platform | Addepto | Sciforce |
|---|---|---|
| PyTorch | N/A | ✓ |
| TensorFlow | 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 |
| MLflow | N/A | N/A |
Pricing comparison: Addepto vs Sciforce
| Criterion | Addepto | Sciforce |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Embedded team, Project delivery | Dedicated engineers, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Addepto vs Sciforce
| Dimension | Addepto | Sciforce |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Retail | Healthcare, Financial services, Logistics |
| Best use cases | Adding Databricks engineers to a manufacturer's data team, Building a GenAI assistant for automotive service documents | Adding NLP engineers to a health-data platform, Placing ML engineers with a Nordic fintech for several years |
| Typical project type | Embedded team | Dedicated engineers |
Addepto vs Sciforce: pros and cons
| 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 |
| Sciforce | |
|---|---|
| + | Four-year augmentation engagement on record with a Swedish client |
| + | Medical data and NLP experience |
| + | Ukrainian rates for senior AI work |
| - | Small team; the 40-specialist figure may be out of date |
| - | Little public detail on augmentation terms |
| - | Wartime operating conditions in Ukraine |
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.
Who should choose Sciforce?
A typical fit: adding NLP engineers to a health-data platform.
Medical and scientific data experience in a small AI-first firm. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Logistics, Sports & media.
Decision matrix: Addepto vs Sciforce
| 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; Addepto 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: Addepto (Not published) vs Sciforce (Not published) |
| You need engineers deployed inside your organization | Addepto |
| You need specialist depth in a specific vertical | Addepto |
Use case fit: Addepto vs Sciforce
| Use case | Addepto fit | Sciforce fit | Winner |
|---|---|---|---|
| Adding Databricks engineers to a manufacturer's data team | Strong | Strong | Both equally |
| Building a GenAI assistant for automotive service documents | Strong | Strong | Both equally |
| Adding NLP engineers to a health-data platform | Strong | Strong | Both equally |
| Placing ML engineers with a Nordic fintech for several years | Limited | Strong | Sciforce |
Verdict: Addepto vs Sciforce
Addepto (3.9/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. AI-heavy team with manufacturing domain experience, now backed by a larger group.
Sciforce (3.9/5) is worth a look if you need placing ML engineers with a Nordic fintech for several years. If your situation matches that, Sciforce is a competitive option.
Related comparisons
Addepto vs Sciforce FAQ
Is Addepto better than Sciforce?
Addepto (3.9/5) scores higher overall, but "better" depends on your use case. Addepto's strongest advantage: nearly the whole team is AI engineers, according to its CEO. Sciforce's strongest advantage: four-year augmentation engagement on record with a Swedish client.
How do Addepto and Sciforce differ in pricing?
Addepto uses collaborative team model or managed delivery; rates on request pricing. Sciforce uses monthly per engineer for augmentation; project pricing otherwise; 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 Sciforce?
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 Addepto and Sciforce?
Addepto's primary differentiator is: AI-heavy team with manufacturing domain experience, now backed by a larger group. Sciforce's primary differentiator is: medical and scientific data experience in a small AI-first firm. They also differ in team size (50–99 (directory estimate) vs 40+ specialists (per company; may be dated)), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Automotive vs Healthcare, Financial services).
Verify all details directly with each company before making a decision.