Miquido vs Persistent Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
Miquido (4.6/5) edges ahead of Persistent Systems (3.9/5) 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.. Persistent Systems is the stronger option for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators.. The right choice depends on your project size, budget, and required tech stack.
Miquido vs Persistent Systems: head-to-head summary
| Criterion | Miquido | Persistent Systems |
|---|---|---|
| Founded | 2011 | 1990 |
| HQ | Krakow, Poland | Pune, India |
| Team size | 201–500 | 10,000+ |
| Rating | 4.6 / 5 | 3.9 / 5 |
| Best for | Companies that need ML/computer-vision capability bundled with broader product engineering (mobile, web) under one delivery team. | Mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators. |
| Pricing model | Not published; project-based and dedicated team | Not published; enterprise project engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | AWS, Microsoft Azure, Google Cloud |
| Industries served | Fintech, Healthcare, Consumer/retail, Media | Healthcare, Financial services, Technology/software, Life sciences |
Miquido vs Persistent Systems: 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.
Persistent Systems
Persistent Systems Limited was founded in 1990 in Pune, India, by Dr. Anand Deshpande, and has grown into a publicly traded (NSE/BSE: PERSISTENT) multinational technology services company with more than 24,000 employees. Its Data Science and Machine Learning practice spans data engineering through enterprise ML deployment across AWS, Azure, and Google Cloud, supported by its Data Experience Hub (DxH), a set of accelerators aimed at operationalizing ML and detecting bias in models through explainable AI. Persistent was named a Leader in the Everest Group Data & AI PEAK Matrix 2025 for the mid-market segment, and holds AWS Premier Tier Partner and Google Cloud Data & Analytics plus Machine Learning Specializations.
Services and capabilities: Miquido vs Persistent Systems
| Capability | Miquido | Persistent Systems |
|---|---|---|
| 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 Persistent Systems
| Framework / platform | Miquido | Persistent Systems |
|---|---|---|
| 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 | ✓ |
| Microsoft Azure | N/A | ✓ |
| Kubernetes | N/A | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: Miquido vs Persistent Systems
| Criterion | Miquido | Persistent Systems |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Dedicated team | Enterprise project engagement, Managed AI services |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Miquido vs Persistent Systems
| Dimension | Miquido | Persistent Systems |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Fintech, Healthcare, Consumer/retail | Healthcare, Financial services, Technology/software |
| 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 | Operationalizing ML models at enterprise scale using pre-built MLOps accelerators, Running bias detection and explainable AI reviews on existing production models |
| Typical project type | Fixed project | Enterprise project engagement |
Miquido vs Persistent Systems: 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. |
| Persistent Systems | |
|---|---|
| + | Everest Group Leader ranking in the Data & AI PEAK Matrix 2025 (mid-market segment) is an independently sourced third-party validation. |
| + | Purpose-built DxH accelerators for MLOps and bias detection add concrete, named tooling beyond generic claims. |
| + | Publicly traded with 35-year operating history, providing financial transparency. |
| + | Named healthcare client work (e.g., cancer-detection collaboration) with a specific, checkable use case. |
| - | Very large scale (24,000+ employees) means ML/AI is one of several major practice areas competing for delivery focus. |
| - | No clearly located aggregate Clutch/G2 star rating specific to its AI practice in available public sources. |
| - | Pricing model and minimum engagement are not published. |
| - | India-centric delivery model may require additional coordination for clients preferring more localized 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 Persistent Systems?
Persistent Systems is the right choice for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators..
Purpose-built DxH accelerator suite for MLOps and bias detection, plus a specific Everest Group Leader ranking in the mid-market Data & AI segment rather than only the largest enterprise tier.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Financial services, Technology/software, Life sciences.
Decision matrix: Miquido vs Persistent Systems
| 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 Persistent Systems (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 | Both may offer discovery engagements |
Use case fit: Miquido vs Persistent Systems
| Use case | Miquido fit | Persistent Systems 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 |
| Operationalizing ML models at enterprise scale using pre-built MLOps accelerators | Limited | Strong | Persistent Systems |
| Running bias detection and explainable AI reviews on existing production models | Limited | Strong | Persistent Systems |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Strong | Persistent Systems |
Verdict: Miquido vs Persistent Systems
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..
Persistent Systems (3.9/5) is the better choice when mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators.. If your situation matches those criteria, Persistent Systems is a competitive option.
Related comparisons
Miquido vs Persistent Systems FAQ
Is Miquido better than Persistent Systems?
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.. Persistent Systems is better for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators..
How do Miquido and Persistent Systems differ in pricing?
Miquido uses not published; project-based and dedicated team pricing with a minimum engagement of Not published. Persistent Systems uses not published; enterprise project engagements 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 Persistent Systems?
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 Persistent Systems?
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.. Persistent Systems's primary differentiator is: purpose-built dxh accelerator suite for mlops and bias detection, plus a specific everest group leader ranking in the mid-market data & ai segment rather than only the largest enterprise tier.. They also differ in team size (201–500 vs 10,000+), minimum engagement (Not published vs Not published), and primary industries served (Fintech, Healthcare vs Healthcare, Financial services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.