Miquido vs Modak: full comparison for 2026
Last updated: July 2026
Quick verdict
Miquido (4.2/5) edges ahead of Modak (3.7/5) overall. Miquido is the better choice for product companies and scale-ups needing ML features embedded within polished mobile or web products. Modak is the stronger option for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. The right choice depends on your project size, budget, and required tech stack.
Miquido vs Modak: head-to-head summary
| Criterion | Miquido | Modak |
|---|---|---|
| Founded | 2011 | 2016 |
| HQ | Kraków, Poland | San Jose, CA |
| Team size | 200+ | 100–200 |
| Rating | 4.2 / 5 | 3.7 / 5 |
| Best for | Product companies and scale-ups needing ML features embedded within polished mobile or web products | Large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption |
| Pricing model | Fixed project, T&M | T&M, retainer |
| Min. engagement | $25K+ | $50K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Apache Spark, Databricks |
| Industries served | saas, media, retail, healthcare, fintech | financial, healthcare, manufacturing, logistics, saas |
Miquido vs Modak: overview
Miquido
Miquido was founded in 2011 and is headquartered in Kraków, Poland, with 200+ engineers. The company specialises in AI and ML development integrated within mobile and web product engineering, serving clients including Skyscanner and Abbey Road Studios (per Miquido Clutch profile and official website). Miquido is known for combining UI/UX engineering with AI capabilities — particularly computer vision, recommendation systems, and NLP — for product-driven clients.
Modak
Modak is an AI-native data engineering company headquartered in San Jose, California, founded in 2016. The company uses machine learning techniques to transform how structured and unstructured enterprise data is prepared, consumed, and shared — focusing on AI-driven data modernisation for large organisations. Global consulting services help enterprises modernise data infrastructure, accelerate AI readiness, and drive measurable business outcomes. (Founding year and approach per Modak official website and ZoomInfo.)
Services and capabilities: Miquido vs Modak
| Capability | Miquido | Modak |
|---|---|---|
| Custom ML build | ✓ | ✗ |
| ML consulting | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP / LLM | ✓ | ✗ |
| Predictive analytics | ✓ | ✓ |
| MLOps | ✗ | ✓ |
| Data engineering | ✗ | ✓ |
| Generative AI | ✗ | ✗ |
| Staff augmentation | ✗ | ✗ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✗ | ✗ |
Tech stack comparison: Miquido vs Modak
| Framework / platform | Miquido | Modak |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: Miquido vs Modak
| Criterion | Miquido | Modak |
|---|---|---|
| Minimum engagement | $25K+ | $50K+ |
| Engagement models | Fixed project, T&M, Retainer | T&M, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Miquido vs Modak
| Dimension | Miquido | Modak |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | saas, media, retail | financial, healthcare, manufacturing |
| Best use cases | AI features within mobile travel app, Recommendation system for media platform | Enterprise data modernisation for AI readiness, ML-powered ETL and data prep pipeline |
| Typical project type | Fixed project | T&M |
Miquido vs Modak: pros and cons
| Miquido | |
|---|---|
| + | Strong integration of ML with product and UI engineering — rare combination |
| + | Named clients include Skyscanner and Abbey Road Studios |
| + | Full product lifecycle capability: design to ML to mobile/web delivery |
| + | Kraków studio with transparent pricing and verifiable Clutch reviews |
| + | Computer vision and NLP experience in production applications |
| - | Less suitable for standalone ML research or data science consulting |
| - | Product engineering focus means less depth in MLOps or large-scale data infrastructure |
| Modak | |
|---|---|
| + | ML applied to data engineering itself — accelerates data prep for ML programmes |
| + | AI-native from inception — not a repositioned data warehouse firm |
| + | Strong on unstructured data processing for AI readiness |
| + | San Jose HQ with enterprise client focus |
| - | Data engineering focus — not suited to custom ML model development or computer vision |
| - | Minimum engagement oriented toward large enterprise programmes |
| - | Less suited to companies without an existing large data estate |
Who should choose Miquido?
Miquido is the right choice for product companies and scale-ups needing ML features embedded within polished mobile or web products.
AI-plus-product development — ML capabilities integrated with UX engineering, not delivered as a standalone model. Minimum engagement starts at $25K+. Works best with clients in saas, media, retail, healthcare, fintech.
Who should choose Modak?
Modak is the right choice for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
ML-powered data engineering — uses ML itself to accelerate data prep and modernisation at enterprise scale. Minimum engagement starts at $50K+. Works best with clients in financial, healthcare, manufacturing, logistics, saas.
Decision matrix: Miquido vs Modak
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Miquido |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Miquido |
| You need specialist depth in a specific vertical | Miquido |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Miquido |
Use case fit: Miquido vs Modak
| Use case | Miquido fit | Modak fit | Winner |
|---|---|---|---|
| AI features within mobile travel app | Strong | Strong | Both equally |
| Recommendation system for media platform | Strong | Limited | Miquido |
| Enterprise data modernisation for AI readiness | Limited | Strong | Modak |
| ML-powered ETL and data prep pipeline | Limited | Strong | Modak |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Miquido vs Modak
Miquido (4.2/5) is the stronger overall choice for most Machine Learning projects. AI-plus-product development — ML capabilities integrated with UX engineering, not delivered as a standalone model. It is best for product companies and scale-ups needing ML features embedded within polished mobile or web products.
Modak (3.7/5) is the better choice when large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. If your situation matches those criteria, Modak is a competitive option.
Related comparisons
Miquido vs Modak FAQ
Is Miquido better than Modak?
Miquido (4.2/5) scores higher overall, but "better" depends on your use case. Miquido is better for product companies and scale-ups needing ML features embedded within polished mobile or web products. Modak is better for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
How do Miquido and Modak differ in pricing?
Miquido uses fixed project, t&m pricing with a minimum engagement of $25K+. Modak uses t&m, retainer pricing with a minimum engagement of $50K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Miquido or Modak?
Modak is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between Miquido and Modak?
Miquido's primary differentiator is: ai-plus-product development — ml capabilities integrated with ux engineering, not delivered as a standalone model. Modak's primary differentiator is: ml-powered data engineering — uses ml itself to accelerate data prep and modernisation at enterprise scale. They also differ in team size (200+ vs 100–200), minimum engagement ($25K+ vs $50K+), and primary industries served (saas, media vs financial, healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.