Miquido vs Addepto: full comparison for 2026
Last updated: July 2026
Quick verdict
Miquido (4.6/5) edges ahead of Addepto (4.4/5) 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.. Addepto is the stronger option for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. The right choice depends on your project size, budget, and required tech stack.
Miquido vs Addepto: head-to-head summary
| Criterion | Miquido | Addepto |
|---|---|---|
| Founded | 2011 | 2018 |
| HQ | Krakow, Poland | Warsaw, Poland |
| Team size | 201–500 | 51–200 |
| Rating | 4.6 / 5 | 4.4 / 5 |
| Best for | Companies that need ML/computer-vision capability bundled with broader product engineering (mobile, web) under one delivery team. | Cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build. |
| Pricing model | Not published; project-based and dedicated team | Project-based |
| Min. engagement | Not published | $10,000 |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, MLOps tooling, Cloud ML platforms (AWS/GCP/Azure) |
| Industries served | Fintech, Healthcare, Consumer/retail, Media | Finance, Healthcare, Retail |
Miquido vs Addepto: 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.
Addepto
Addepto is a Poland-based AI consulting firm founded in 2018 by Artur Haponik and Edwin Lisowski that focuses specifically on machine learning consulting, MLOps consulting, and data/analytics advisory work rather than broader software development. The company has around 52 employees and holds a 4.7 Clutch rating, with Clutch-reported project costs typically in the $10,000–$49,000 range, making it one of the more budget-accessible options among firms in this category. Addepto has been recognized among Forbes' top AI consulting companies and appeared on the Deloitte Technology Fast 500 EMEA list, citing 1,193 percent revenue growth over the qualifying period. In December 2025, Addepto was acquired by KMS Technology, a US-based digital engineering, data, and AI company backed by growth private equity firm Sunstone Partners; Addepto now operates as an integrated division rather than as a fully independent company.
Services and capabilities: Miquido vs Addepto
| Capability | Miquido | Addepto |
|---|---|---|
| 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 Addepto
| Framework / platform | Miquido | Addepto |
|---|---|---|
| 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 Addepto
| Criterion | Miquido | Addepto |
|---|---|---|
| Minimum engagement | Not published | $10,000 |
| Engagement models | Fixed project, Dedicated team | Fixed project, Advisory/consulting retainer |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: Miquido vs Addepto
| Dimension | Miquido | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Healthcare, Consumer/retail | Finance, Healthcare, Retail |
| 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 | Auditing an existing ML pipeline and recommending MLOps improvements, Running a well-scoped, budget-constrained machine learning pilot |
| Typical project type | Fixed project | Fixed project |
Miquido vs Addepto: 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. |
| Addepto | |
|---|---|
| + | 4.7 Clutch rating with lower typical project cost ($10K–$49K) than most peers in this comparison. |
| + | Named a top 10 AI consulting company by Forbes. |
| + | Deloitte Technology Fast 500 EMEA recognition (#143) signals strong recent revenue growth. |
| + | Focused specifically on ML/MLOps consulting rather than diluting attention across general software development. |
| - | Small team (~52 employees) caps capacity for large or multiple concurrent enterprise engagements. |
| - | Lower typical project size may signal a fit for smaller-scope work rather than large production ML platforms. |
| - | Public case studies with named enterprise clients are limited in available sources. |
| - | Now part of KMS Technology following the December 2025 acquisition, introducing near-term integration and roadmap uncertainty for prospective clients. |
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 Addepto?
Addepto is the right choice for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..
Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. Minimum engagement starts at $10,000. Works best with clients in Finance, Healthcare, Retail.
Decision matrix: Miquido vs Addepto
| 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 Addepto ($10,000) |
| 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 | Addepto |
Use case fit: Miquido vs Addepto
| Use case | Miquido fit | Addepto 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 | Limited | Miquido |
| Auditing an existing ML pipeline and recommending MLOps improvements | Limited | Strong | Addepto |
| Running a well-scoped, budget-constrained machine learning pilot | Limited | Strong | Addepto |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Strong | Addepto |
Verdict: Miquido vs Addepto
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..
Addepto (4.4/5) is the better choice when cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. If your situation matches those criteria, Addepto is a competitive option.
Related comparisons
Miquido vs Addepto FAQ
Is Miquido better than Addepto?
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.. Addepto is better for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..
How do Miquido and Addepto differ in pricing?
Miquido uses not published; project-based and dedicated team pricing with a minimum engagement of Not published. Addepto uses project-based pricing with a minimum engagement of $10,000. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Miquido or Addepto?
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 Miquido and Addepto?
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.. Addepto's primary differentiator is: dedicated mlops-consulting service line and clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. They also differ in team size (201–500 vs 51–200), minimum engagement (Not published vs $10,000), and primary industries served (Fintech, Healthcare vs Finance, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.