Addepto vs Miquido: full comparison for 2026
Last updated: July 2026
Quick verdict
Addepto (4.2/5) edges ahead of Miquido (4.0/5) overall. Addepto is the better choice for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness. Miquido is the stronger option for product teams needing ML embedded inside polished digital products, with Google-certified cloud deployment and design expertise. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Miquido: head-to-head summary
| Criterion | Addepto | Miquido |
|---|---|---|
| Founded | 2016 | 2011 |
| HQ | Warsaw, Poland | Krakow, Poland |
| Team size | 50–200 | 150–300 |
| Rating | 4.2 / 5 | 4.0 / 5 |
| Best for | Mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness | Product teams needing ML embedded inside polished digital products, with Google-certified cloud deployment and design expertise |
| Pricing model | Fixed project, T&M, Dedicated team | Fixed project, Dedicated team, T&M |
| Min. engagement | $20K | $30K |
| Primary tech stack | Python, TensorFlow, PyTorch | TensorFlow, PyTorch, Python |
| Industries served | fintech, energy, retail, manufacturing, logistics | fintech, e-commerce, healthcare, entertainment, media |
Addepto vs Miquido: overview
Addepto
Addepto is a Poland-based AI consulting and development firm focused on end-to-end machine learning solutions for mid-market and enterprise clients. The company specializes in building data pipelines, custom ML models, and decision-support tools with particular depth in financial services, energy, and retail — industries where regulatory awareness and data governance are non-negotiable. Addepto covers the full stack from data engineering through model development, deployment, and integration.
Miquido
Miquido is a Google-certified software development company founded in 2011 and headquartered in Krakow, Poland. The company employs 150–300 professionals and has delivered 250+ digital products for clients including Warner, Dolby, Abbey Road Studios, Skyscanner, and TUI. Miquido's ML practice is distinguished by its integration with product design expertise — delivering machine learning inside well-crafted user experiences rather than as isolated algorithmic components.
Services and capabilities: Addepto vs Miquido
| Capability | Addepto | Miquido |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| ML consulting | ✓ | ✓ |
| Deep learning | ✓ | ✗ |
| NLP | ✗ | ✓ |
| Computer vision | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| Predictive analytics | ✓ | ✗ |
| Generative AI | ✓ | ✓ |
| Data engineering | ✓ | ✗ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Addepto vs Miquido
| Framework / platform | Addepto | Miquido |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| Scikit-Learn | ✓ | N/A |
| LangChain | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
| GCP Vertex AI | N/A | N/A |
| Kubernetes | N/A | N/A |
| Apache Spark | ✓ | N/A |
| MLflow | N/A | N/A |
Pricing comparison: Addepto vs Miquido
| Criterion | Addepto | Miquido |
|---|---|---|
| Minimum engagement | $20K | $30K |
| Engagement models | Fixed project, T&M, Dedicated team | Fixed project, Dedicated team, T&M |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Addepto vs Miquido
| Dimension | Addepto | Miquido |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | fintech, energy, retail | fintech, e-commerce, healthcare |
| Best use cases | Credit risk scoring and fraud detection model development for fintech platforms, Energy demand forecasting and grid optimization using time-series ML models | ML feature integration into mobile and web consumer products (e.g., recommendation, personalization), Computer vision feature development for entertainment or retail apps |
| Typical project type | Fixed project | Fixed project |
Addepto vs Miquido: pros and cons
| Addepto | |
|---|---|
| + | Genuine depth in finance and energy ML — not a generalist firm claiming vertical expertise |
| + | Covers the full stack from data pipeline architecture through model deployment |
| + | Generative AI capability alongside classical ML for hybrid solution architectures |
| + | Warsaw delivery hub provides competitive rates with EU-based data handling |
| + | Accessible minimum engagement for early-stage ML projects or POCs |
| - | Smaller team than enterprise-tier firms; large-scale concurrent programmes may strain capacity |
| - | Less US-based client management than North American competitors |
| - | Limited public case studies compared to larger firms with dedicated marketing teams |
| Miquido | |
|---|---|
| + | Google-certified partnership confirms cloud ML deployment capability on GCP independently |
| + | Named enterprise clients (Warner, Dolby, Skyscanner, TUI) verify delivery at brand scale |
| + | ML plus product design combination delivers end-user-facing AI features, not back-end-only models |
| + | 9/10 projects from referrals signals strong client satisfaction and delivery consistency |
| + | Krakow base with North American, European, and Middle Eastern client experience |
| - | Hourly rates ($70–$150) are higher than Eastern European average for similar team size |
| - | Product-first focus may mean less depth in complex research-adjacent ML or custom model architectures |
| - | Less visible in the US market compared to North American competitors of equivalent capability |
Who should choose Addepto?
Addepto is the right choice for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness.
End-to-end AI/ML delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability. Minimum engagement starts at $20K. Works best with clients in fintech, energy, retail, manufacturing, logistics.
Who should choose Miquido?
Miquido is the right choice for product teams needing ML embedded inside polished digital products, with Google-certified cloud deployment and design expertise.
Google-certified AI/ML capability paired with strong product design — clients receive ML that works inside well-crafted user experiences, not bolted-on algorithms. Minimum engagement starts at $30K. Works best with clients in fintech, e-commerce, healthcare, entertainment, media.
Decision matrix: Addepto vs Miquido
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Addepto |
| You need a large dedicated team for an ongoing programme | Addepto |
| Your budget is at the lower end | Addepto |
| You need specialist depth in a specific vertical | Addepto |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Addepto |
Use case fit: Addepto vs Miquido
| Use case | Addepto fit | Miquido fit | Winner |
|---|---|---|---|
| Credit risk scoring and fraud detection model development for fintech platforms | Strong | Limited | Addepto |
| Energy demand forecasting and grid optimization using time-series ML models | Strong | Limited | Addepto |
| ML feature integration into mobile and web consumer products (e.g., recommendation, personalization) | Strong | Strong | Both equally |
| Computer vision feature development for entertainment or retail apps | Limited | Strong | Miquido |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Addepto vs Miquido
Addepto (4.2/5) is the stronger overall choice for most Machine Learning Development projects. End-to-end AI/ML delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability. It is best for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness.
Miquido (4.0/5) is the better choice when product teams needing ML embedded inside polished digital products, with Google-certified cloud deployment and design expertise. If your situation matches those criteria, Miquido is a competitive option.
Related comparisons
Addepto vs Miquido FAQ
Is Addepto better than Miquido?
Addepto (4.2/5) scores higher overall, but "better" depends on your use case. Addepto is better for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness. Miquido is better for product teams needing ML embedded inside polished digital products, with Google-certified cloud deployment and design expertise.
How do Addepto and Miquido differ in pricing?
Addepto uses fixed project, t&m, dedicated team pricing with a minimum engagement of $20K. Miquido uses fixed project, dedicated team, t&m pricing with a minimum engagement of $30K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Miquido?
Miquido 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 Miquido?
Addepto's primary differentiator is: end-to-end ai/ml delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability. Miquido's primary differentiator is: google-certified ai/ml capability paired with strong product design — clients receive ml that works inside well-crafted user experiences, not bolted-on algorithms. They also differ in team size (50–200 vs 150–300), minimum engagement ($20K vs $30K), and primary industries served (fintech, energy vs fintech, e-commerce).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.