Addepto vs Miquido: full comparison for 2026
Quick verdict
Addepto (4.3/5) edges ahead of Miquido (3.9/5) overall. Addepto is the better choice for data teams needing warehouse work before an LLM project. Miquido is the stronger option for product teams adding AI to customer-facing apps. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Miquido: head-to-head summary
| Criterion | Addepto | Miquido |
|---|---|---|
| Founded | 2018 | 2011 |
| HQ | Warsaw, Poland | Kraków, Poland |
| Team size | 50–249 | 150–250 |
| Rating | 4.3 / 5 | 3.9 / 5 |
| Primary differentiator | Data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored | Product design and mobile engineering applied to AI features in apps |
| Pricing model | $50–$99/hr (Clutch band); discovery workshops, then T&M | $50–$99/hr (Clutch band); fixed-price and T&M |
| Min. engagement | $10,000+ (Clutch) | Not disclosed |
| Primary tech stack | Databricks, Snowflake, Azure OpenAI | Azure OpenAI, Google Vertex AI, Flutter |
| Industries served | Manufacturing, Retail & e-commerce, Aviation, Financial services | Financial services, Healthcare, Retail & e-commerce, Entertainment |
Addepto vs Miquido: overview
Addepto
Addepto is a Warsaw-based AI and data consultancy that started trading in April 2018 (some directories list 2017). Clutch shows 50–249 employees, a $50–$99 hourly band and a $10,000 minimum project. CB Insights reports that KMS Technology acquired the company in December 2025, after an earlier tie-up with Grape Up. Its work typically begins with data engineering and moves on to generative AI, MLOps and AI discovery workshops.
Miquido
Miquido is a software product studio founded in 2011 in Kraków, Poland. Directory headcount estimates range from about 175 to more than 250 people. Clutch lists a $50–$99 hourly band and an overall rating of 4.9, with reviewers noting occasional difficulty scaling teams quickly. It now describes itself as an AI-native development firm, but most of its portfolio is consumer-facing apps rather than back-office system integration.
Services and capabilities: Addepto vs Miquido
| Capability | Addepto | Miquido |
|---|---|---|
| CRM / ERP integration | ✗ | ✗ |
| LLM API gateway & cost control | ✓ | ✓ |
| Document processing | ✗ | ✗ |
| Agentic workflows | ✗ | ✗ |
| Fixed-price pilot | ✗ | ✗ |
| Managed services after launch | ✗ | ✗ |
| PII masking & access control | ✗ | ✗ |
Tech stack comparison: Addepto vs Miquido
| Framework / platform | Addepto | Miquido |
|---|---|---|
| Salesforce | N/A | N/A |
| SAP | N/A | N/A |
| Microsoft Dynamics 365 | N/A | N/A |
| HubSpot | N/A | N/A |
| Snowflake | ✓ | N/A |
| Databricks | ✓ | N/A |
| Azure OpenAI | ✓ | ✓ |
| AWS Bedrock | N/A | N/A |
| LangChain | ✓ | ✓ |
| ServiceNow | N/A | N/A |
Pricing comparison: Addepto vs Miquido
| Criterion | Addepto | Miquido |
|---|---|---|
| Minimum engagement | $10,000+ (Clutch) | Not disclosed |
| Engagement models | Fixed project, Time & materials | Fixed project, Time & materials |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Addepto vs Miquido
| Dimension | Addepto | Miquido |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail & e-commerce, Aviation | Financial services, Healthcare, Retail & e-commerce |
| Best use cases | Building a Databricks lakehouse that later feeds demand forecasts and an internal assistant., Productionizing ML models with MLflow and monitoring. | Adding an AI assistant to a banking or fintech app., Personalized recommendations inside a mobile app. |
| Typical project type | Fixed project | Fixed project |
Addepto vs Miquido: pros and cons
| Addepto | |
|---|---|
| + | Publicly listed Clutch rate band and $10,000 minimum make budgeting straightforward |
| + | Databricks and Spark experience helps when the data estate is the real blocker |
| + | Discovery workshops scope use cases before larger spend |
| + | Predictive analytics and LLM work available from the same team |
| - | Acquired by KMS Technology in December 2025 (per CB Insights), so ownership and leadership may change |
| - | Corporate history includes several restructurings, which complicates long-term vendor planning |
| - | Limited published work inside CRM platforms |
| Miquido | |
|---|---|
| + | Strong product design and mobile craft |
| + | Published Clutch rate band |
| + | Good fit for consumer-facing AI features |
| - | Little back-office integration work with CRM or ERP |
| - | Reviewers mention resource constraints when scaling up |
Who should choose Addepto?
A typical fit: building a Databricks lakehouse that later feeds demand forecasts and an internal assistant.
Data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored. Minimum engagement starts at $10,000+ (Clutch). Works best with clients in Manufacturing, Retail & e-commerce, Aviation, Financial services.
Who should choose Miquido?
A typical fit: adding an AI assistant to a banking or fintech app.
Product design and mobile engineering applied to AI features in apps. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Entertainment.
Decision matrix: Addepto vs Miquido
| Your situation | Recommended choice |
|---|---|
| You want a fixed-price audit or pilot before committing | Neither advertises one; ask for a scoped pilot |
| AI has to work inside your existing CRM or ERP | Neither lists CRM/ERP integration work |
| Personal data must be masked and answers limited by user permissions | Ask both for their PII and access-control design |
| Your budget is at the lower end | Compare: Addepto ($10,000+ (Clutch)) vs Miquido (Not disclosed) |
| You want the vendor to run the AI service after launch | Neither offers managed services; plan in-house operations |
| You are building multi-step agents across systems | Neither lists agentic AI work |
Use case fit: Addepto vs Miquido
| Use case | Addepto fit | Miquido fit | Winner |
|---|---|---|---|
| Building a Databricks lakehouse that later feeds demand forecasts and an internal assistant. | Strong | Limited | Addepto |
| Productionizing ML models with MLflow and monitoring. | Strong | Limited | Addepto |
| Adding an AI assistant to a banking or fintech app. | Limited | Strong | Miquido |
| Personalized recommendations inside a mobile app. | Limited | Strong | Miquido |
Verdict: Addepto vs Miquido
Addepto (4.3/5) is the stronger overall choice for most AI Integration projects. Data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored.
Miquido (3.9/5) is worth a look if you need personalized recommendations inside a mobile app. If your situation matches that, Miquido is a competitive option.
Related comparisons
Addepto vs Miquido FAQ
Is Addepto better than Miquido?
Addepto (4.3/5) scores higher overall, but "better" depends on your use case. Addepto's strongest advantage: publicly listed Clutch rate band and $10,000 minimum make budgeting straightforward. Miquido's strongest advantage: strong product design and mobile craft.
How do Addepto and Miquido differ in pricing?
Addepto pricing: $50–$99/hr (Clutch band); discovery workshops, then T&M. Minimum engagement: $10,000+ (Clutch). Miquido pricing: $50–$99/hr (Clutch band); fixed-price and T&M. 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: data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored. Miquido's primary differentiator is: product design and mobile engineering applied to AI features in apps. They also differ in team size (50–249 vs 150–250), minimum engagement ($10,000+ (Clutch) vs Not disclosed), and primary industries served (Manufacturing, Retail & e-commerce vs Financial services, Healthcare).
Verify all details directly with each company before making a decision.