Miquido vs Modus Create: full comparison for 2026
Last updated: July 2026
Quick verdict
Miquido (4.6/5) edges ahead of Modus Create (4.0/5) overall. Miquido is the better choice for companies that need ML/computer-vision capability bundled with broader product engineering (mobile, web) under one delivery team.. Modus Create is the stronger option for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery.. The right choice depends on your project size, budget, and required tech stack.
Miquido vs Modus Create: head-to-head summary
| Criterion | Miquido | Modus Create |
|---|---|---|
| Founded | 2011 | 2011 |
| HQ | Krakow, Poland | Reston, USA |
| Team size | 201–500 | 501–1,000 |
| Rating | 4.6 / 5 | 4.0 / 5 |
| Best for | Companies that need ML/computer-vision capability bundled with broader product engineering (mobile, web) under one delivery team. | Distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery. |
| Pricing model | Not published; project-based and dedicated team | Not published; project and dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, AWS, Data governance tooling |
| Industries served | Fintech, Healthcare, Consumer/retail, Media | Technology/SaaS, Retail, Healthcare |
Miquido vs Modus Create: overview
Miquido
Miquido is a Poland-based software development company founded in 2011 that has built out AI/ML, computer vision, and NLP capabilities alongside its core mobile and web engineering practice. It was recognized by Clutch as a Global Leader in Artificial Intelligence in 2023 and reports an average Clutch score near 4.9 from roughly 50 reviews. The company operates from its Krakow headquarters with additional offices in Berlin, Zurich, and other European locations, and serves clients across fintech, healthcare, and consumer product sectors. Its ML offering spans data science, applied computer vision, and NLP work delivered by dedicated squads.
Modus Create
Modus Create is a fully remote, distributed product engineering company founded in 2011 and headquartered in Reston, Virginia, with team members spread across more than 55 countries. The company's AI/ML and data engineering practice includes AI Strategy Roadmap assessments and AI Data Foundation assessments intended to ensure underlying data is reliable and properly governed before or alongside model development work. Modus Create has partnered with technology providers including Atlassian, GitHub, and AWS, and has been recognized on the Inc. 5000 list for nine consecutive years.
Services and capabilities: Miquido vs Modus Create
| Capability | Miquido | Modus Create |
|---|---|---|
| Custom model training | ✓ | ✓ |
| Fine-tuning & adaptation | ✗ | ✗ |
| MLOps pipeline | ✗ | ✗ |
| Model deployment & serving | ✗ | ✗ |
| Data engineering for ML | ✓ | ✓ |
| ML infrastructure management | ✗ | ✗ |
| Computer vision | ✓ | ✗ |
| NLP & LLM development | ✓ | ✗ |
| Forecasting & time-series modeling | ✗ | ✗ |
| ML strategy consulting | ✗ | ✓ |
Tech stack comparison: Miquido vs Modus Create
| Framework / platform | Miquido | Modus Create |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Amazon Bedrock | N/A | N/A |
| Google Cloud | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: Miquido vs Modus Create
| Criterion | Miquido | Modus Create |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Dedicated team | Fixed project, Dedicated team, Assessment/audit engagement |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Miquido vs Modus Create
| Dimension | Miquido | Modus Create |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Fintech, Healthcare, Consumer/retail | Technology/SaaS, Retail, Healthcare |
| Best use cases | Adding computer vision or NLP features to an existing mobile or web product, Building a custom ML model as part of a broader digital product engineering engagement | Running an AI Data Foundation assessment before committing to a full model-development engagement, Building an AI strategy roadmap for an organization new to machine learning adoption |
| Typical project type | Fixed project | Fixed project |
Miquido vs Modus Create: pros and cons
| Miquido | |
|---|---|
| + | Strong Clutch track record: near-4.9 average across roughly 50 reviews. |
| + | Clutch-recognized Global Leader in Artificial Intelligence (2023). |
| + | Ability to bundle ML/CV work with broader mobile and web product engineering under one vendor. |
| + | Multi-office European presence (Krakow, Berlin, Zurich) supports EU-based client delivery preferences. |
| - | AI/ML is one specialization among several service lines rather than the company's sole focus. |
| - | Pricing and minimum engagement size are not published, requiring a scoping call. |
| - | Team size estimates vary meaningfully across sources (roughly 200–500), suggesting some data volatility. |
| - | Public case studies more heavily emphasize mobile/app work than deep ML model-development detail. |
| Modus Create | |
|---|---|
| + | Structured AI Data Foundation assessment reduces risk of building models on ungoverned or unreliable data. |
| + | Fully remote, globally distributed team (55+ countries) offers broad timezone coverage. |
| + | Nine consecutive years on the Inc. 5000 list signals sustained growth. |
| + | Technology partnerships with Atlassian, GitHub, and AWS support integrated delivery tooling. |
| - | AI/ML is one of several product engineering service lines rather than the company's sole specialization. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Pricing model and minimum engagement are not published. |
| - | Fully remote delivery model may not suit buyers who prefer localized or on-site teams. |
Who should choose Miquido?
Miquido is the right choice for companies that need ML/computer-vision capability bundled with broader product engineering (mobile, web) under one delivery team..
Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor.. Minimum engagement starts at Not published. Works best with clients in Fintech, Healthcare, Consumer/retail, Media.
Who should choose Modus Create?
Modus Create is the right choice for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery..
Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development.. Minimum engagement starts at Not published. Works best with clients in Technology/SaaS, Retail, Healthcare.
Decision matrix: Miquido vs Modus Create
| 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 | Miquido |
| Your budget is at the lower end | Compare: Miquido (Not published) vs Modus Create (Not published) |
| 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 | Modus Create |
Use case fit: Miquido vs Modus Create
| Use case | Miquido fit | Modus Create fit | Winner |
|---|---|---|---|
| Adding computer vision or NLP features to an existing mobile or web product | Strong | Limited | Miquido |
| Building a custom ML model as part of a broader digital product engineering engagement | Strong | Strong | Both equally |
| Running an AI Data Foundation assessment before committing to a full model-development engagement | Limited | Strong | Modus Create |
| Building an AI strategy roadmap for an organization new to machine learning adoption | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: Miquido vs Modus Create
Miquido (4.6/5) is the stronger overall choice for most ML Model Development projects. Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor.. It is best for companies that need ML/computer-vision capability bundled with broader product engineering (mobile, web) under one delivery team..
Modus Create (4.0/5) is the better choice when distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery.. If your situation matches those criteria, Modus Create is a competitive option.
Related comparisons
Miquido vs Modus Create FAQ
Is Miquido better than Modus Create?
Miquido (4.6/5) scores higher overall, but "better" depends on your use case. Miquido is better for companies that need ML/computer-vision capability bundled with broader product engineering (mobile, web) under one delivery team.. Modus Create is better for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery..
How do Miquido and Modus Create differ in pricing?
Miquido uses not published; project-based and dedicated team pricing with a minimum engagement of Not published. Modus Create uses not published; project and dedicated team pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Miquido or Modus Create?
Modus Create 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 Miquido and Modus Create?
Miquido's primary differentiator is: combines a large, review-verified product engineering practice with a dedicated ai/ml/cv specialization, useful for teams needing both app and model work from one vendor.. Modus Create's primary differentiator is: structured ai data foundation assessment methodology that explicitly evaluates data readiness before committing to model development.. They also differ in team size (201–500 vs 501–1,000), minimum engagement (Not published vs Not published), and primary industries served (Fintech, Healthcare vs Technology/SaaS, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.