Kineo.ai vs FELD M: full comparison for 2026
Last updated: July 2026
Quick verdict
Kineo.ai (4.6/5) edges ahead of FELD M (4.2/5) overall. Kineo.ai is the better choice for mid-market European businesses wanting a lean, senior AI consulting partner for a scoped efficiency project. FELD M is the stronger option for european enterprises wanting a long-established, multi-country data and AI consulting partner. The right choice depends on your project size, budget, and required tech stack.
Kineo.ai vs FELD M: head-to-head summary
| Criterion | Kineo.ai | FELD M |
|---|---|---|
| Founded | 2020 | 2002 |
| HQ | Berlin, Germany | Munich, Germany |
| Team size | 11–50 | 51–200 |
| Rating | 4.6 / 5 | 4.2 / 5 |
| Best for | Mid-market European businesses wanting a lean, senior AI consulting partner for a scoped efficiency project | European enterprises wanting a long-established, multi-country data and AI consulting partner |
| Pricing model | Fixed project, consulting retainer | Retainer, fixed project |
| Min. engagement | $20K | $25K |
| Primary tech stack | Python, Scikit-learn, Azure | Python, Google Cloud, Azure |
| Industries served | Manufacturing, Logistics, Retail, Financial Services | Retail, Media, Automotive, Financial Services |
Kineo.ai vs FELD M: overview
Kineo.ai
Kineo.ai is a Berlin-headquartered AI consulting firm founded in 2020. With a team of 11 to 50 employees based entirely in Germany, Kineo partners with businesses to identify and implement customized AI and ML projects aimed at improving operational efficiency. As a younger boutique, its public track record is shorter than more established German AI consultancies.
FELD M
FELD M was founded in 2002 in Munich as a one-person web analytics consultancy and has grown into a team of around 60 employees, with offices in Munich, Berlin, Hamburg, Warsaw (FELD M Poland), and Basel (FELD M Switzerland). The firm offers AI, data science, and machine learning product consulting for enterprise clients.
Services and capabilities: Kineo.ai vs FELD M
| Capability | Kineo.ai | FELD M |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✓ | ✗ |
| MLOps | ✗ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Kineo.ai vs FELD M
| Framework / platform | Kineo.ai | FELD M |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | N/A |
| Azure | ✓ | ✓ |
| Kubernetes | N/A | N/A |
Pricing comparison: Kineo.ai vs FELD M
| Criterion | Kineo.ai | FELD M |
|---|---|---|
| Minimum engagement | $20K | $25K |
| Engagement models | Fixed project, Retainer | Retainer, Fixed project |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Kineo.ai vs FELD M
| Dimension | Kineo.ai | FELD M |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Logistics, Retail | Retail, Media, Automotive |
| Best use cases | Operational efficiency AI audits, Predictive analytics for logistics scheduling | Data and AI strategy consulting for an enterprise client, Predictive analytics for retail or media audience data |
| Typical project type | Fixed project | Retainer |
Kineo.ai vs FELD M: pros and cons
| Kineo.ai | |
|---|---|
| + | Fully Germany-based team, useful for clients requiring EU-only data handling |
| + | Focused specifically on operational-efficiency AI use cases rather than broad generalist scope |
| + | Lean boutique structure enables direct access to senior consultants |
| - | Founded in 2020, so has a shorter track record than established German AI consultancies |
| - | Small team size (11–50) limits capacity for large multi-workstream programmes |
| - | Fewer public named case studies available for independent verification |
| FELD M | |
|---|---|
| + | Over two decades of operating history since founding in 2002, among the longest-running firms on this list |
| + | Multi-country footprint across Germany, Poland, and Switzerland supports pan-European delivery |
| + | Grew organically from a single-client analytics practice into a full AI and data consultancy |
| + | Deep experience translating business analytics needs into ML and data science products |
| - | Roots in web analytics consulting mean ML engineering depth is narrower than pure-play ML specialists |
| - | Mid-size team of around 60 spread across five offices, which may limit concentration on any single project |
Who should choose Kineo.ai?
Kineo.ai is the right choice for mid-market European businesses wanting a lean, senior AI consulting partner for a scoped efficiency project.
All-Germany team of AI consultants focused specifically on operational-efficiency ML use cases. Minimum engagement starts at $20K. Works best with clients in Manufacturing, Logistics, Retail, Financial Services.
Who should choose FELD M?
FELD M is the right choice for european enterprises wanting a long-established, multi-country data and AI consulting partner.
Over two decades of operating history since founding in 2002, with organic growth into a five-office pan-European practice. Minimum engagement starts at $25K. Works best with clients in Retail, Media, Automotive, Financial Services.
Decision matrix: Kineo.ai vs FELD M
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Kineo.ai |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Kineo.ai |
| You need specialist depth in a specific vertical | Kineo.ai |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Kineo.ai |
Use case fit: Kineo.ai vs FELD M
| Use case | Kineo.ai fit | FELD M fit | Winner |
|---|---|---|---|
| Operational efficiency AI audits | Strong | Limited | Kineo.ai |
| Predictive analytics for logistics scheduling | Strong | Strong | Both equally |
| Data and AI strategy consulting for an enterprise client | Limited | Strong | FELD M |
| Predictive analytics for retail or media audience data | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Kineo.ai vs FELD M
Kineo.ai (4.6/5) is the stronger overall choice for most Machine Learning Development projects. All-Germany team of AI consultants focused specifically on operational-efficiency ML use cases. It is best for mid-market European businesses wanting a lean, senior AI consulting partner for a scoped efficiency project.
FELD M (4.2/5) is the better choice when european enterprises wanting a long-established, multi-country data and AI consulting partner. If your situation matches those criteria, FELD M is a competitive option.
Related comparisons
Kineo.ai vs FELD M FAQ
Is Kineo.ai better than FELD M?
Kineo.ai (4.6/5) scores higher overall, but "better" depends on your use case. Kineo.ai is better for mid-market European businesses wanting a lean, senior AI consulting partner for a scoped efficiency project. FELD M is better for european enterprises wanting a long-established, multi-country data and AI consulting partner.
How do Kineo.ai and FELD M differ in pricing?
Kineo.ai uses fixed project, consulting retainer pricing with a minimum engagement of $20K. FELD M uses retainer, fixed project pricing with a minimum engagement of $25K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Kineo.ai or FELD M?
FELD M 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 Kineo.ai and FELD M?
Kineo.ai's primary differentiator is: all-germany team of ai consultants focused specifically on operational-efficiency ml use cases. FELD M's primary differentiator is: over two decades of operating history since founding in 2002, with organic growth into a five-office pan-european practice. They also differ in team size (11–50 vs 51–200), minimum engagement ($20K vs $25K), and primary industries served (Manufacturing, Logistics vs Retail, Media).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.