Addepto vs Brainpool AI: full comparison for 2026
Quick verdict
Addepto (3.9/5) edges ahead of Brainpool AI (3.6/5) overall. Addepto is the better choice for industrial and automotive companies adding AI and data engineers to an internal team. Brainpool AI is the stronger option for buyers who need a rare academic AI specialist for a short engagement. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Brainpool AI: head-to-head summary
| Criterion | Addepto | Brainpool AI |
|---|---|---|
| Founded | 2017 | 2017 |
| HQ | Warsaw, Poland | London, UK |
| Team size | 50–99 (directory estimate) | Small core team; 500+ network experts (per company) |
| Rating | 3.9 / 5 | 3.6 / 5 |
| Primary differentiator | AI-heavy team with manufacturing domain experience, now backed by a larger group | Academic-heavy expert network across 23 countries |
| Pricing model | Collaborative team model or managed delivery; rates on request | Per-expert or project pricing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Databricks, Spark | Python, PyTorch, Vertex AI |
| Industries served | Manufacturing, Automotive, Retail, Aviation | Financial services, Retail, Healthcare, Public sector |
Addepto vs Brainpool AI: 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.
Brainpool AI
Brainpool AI was set up in London in 2017 (its incorporation date is February 2016) as a network of AI and ML experts, and it now counts more than 500 vetted PhD and MSc specialists across 23 countries. Co-founder Kasia Borowska built the business on matching that network to client problems. Over time it has shifted toward its own platform, Cortex, on which it builds LLM agents, fine-tuned models and MLOps setups. In 2019 it raised just over £200,000 through equity crowdfunding.
Services and capabilities: Addepto vs Brainpool AI
| Capability | Addepto | Brainpool AI |
|---|---|---|
| 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 Brainpool AI
| Framework / platform | Addepto | Brainpool AI |
|---|---|---|
| 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 |
| MLflow | N/A | N/A |
Pricing comparison: Addepto vs Brainpool AI
| Criterion | Addepto | Brainpool AI |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Embedded team, Project delivery | Fractional experts, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Addepto vs Brainpool AI
| Dimension | Addepto | Brainpool AI |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Retail | Financial services, Retail, Healthcare |
| Best use cases | Adding Databricks engineers to a manufacturer's data team, Building a GenAI assistant for automotive service documents | Bringing in a PhD expert to review a fine-tuning plan, Running a short research spike on a novel model |
| Typical project type | Embedded team | Fractional experts |
Addepto vs Brainpool AI: 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 |
| Brainpool AI | |
|---|---|
| + | Deep academic bench for unusual research questions |
| + | Experts available in many countries |
| + | Can switch to building on its own platform if you need delivery |
| - | The company is moving from expert placement toward its own product |
| - | Sources disagree on the founding year (2016 or 2017) |
| - | Small core team behind a large external network |
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 Brainpool AI?
A typical fit: bringing in a PhD expert to review a fine-tuning plan.
Academic-heavy expert network across 23 countries. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Retail, Healthcare, Public sector.
Decision matrix: Addepto vs Brainpool AI
| Your situation | Recommended choice |
|---|---|
| You need one AI specialist part-time | Brainpool AI |
| You need several engineers working as one team | Addepto |
| 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 Brainpool AI (Not published) |
| You need engineers deployed inside your organization | Addepto |
| You need specialist depth in a specific vertical | Addepto |
Use case fit: Addepto vs Brainpool AI
| Use case | Addepto fit | Brainpool AI fit | Winner |
|---|---|---|---|
| Adding Databricks engineers to a manufacturer's data team | Strong | Limited | Addepto |
| Building a GenAI assistant for automotive service documents | Strong | Strong | Both equally |
| Bringing in a PhD expert to review a fine-tuning plan | Limited | Strong | Brainpool AI |
| Running a short research spike on a novel model | Limited | Strong | Brainpool AI |
Verdict: Addepto vs Brainpool AI
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.
Brainpool AI (3.6/5) is worth a look if you need running a short research spike on a novel model. If your situation matches that, Brainpool AI is a competitive option.
Related comparisons
Addepto vs Brainpool AI FAQ
Is Addepto better than Brainpool AI?
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. Brainpool AI's strongest advantage: deep academic bench for unusual research questions.
How do Addepto and Brainpool AI differ in pricing?
Addepto uses collaborative team model or managed delivery; rates on request pricing. Brainpool AI uses per-expert or project pricing; 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 Brainpool AI?
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 Brainpool AI?
Addepto's primary differentiator is: AI-heavy team with manufacturing domain experience, now backed by a larger group. Brainpool AI's primary differentiator is: academic-heavy expert network across 23 countries. They also differ in team size (50–99 (directory estimate) vs Small core team; 500+ network experts (per company)), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Automotive vs Financial services, Retail).
Verify all details directly with each company before making a decision.