Addepto vs Scopic: full comparison for 2026
Last updated: July 2026
Quick verdict
Addepto (4.2/5) edges ahead of Scopic (3.9/5) overall. Addepto is the better choice for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness. Scopic is the stronger option for organizations needing fully custom ML engineering with 20+ years of distributed team experience and strong computer vision and deep learning capability. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Scopic: head-to-head summary
| Criterion | Addepto | Scopic |
|---|---|---|
| Founded | 2016 | 2006 |
| HQ | Warsaw, Poland | Marlborough, MA, USA |
| Team size | 50–200 | 250–500 |
| Rating | 4.2 / 5 | 3.9 / 5 |
| Best for | Mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness | Organizations needing fully custom ML engineering with 20+ years of distributed team experience and strong computer vision and deep learning capability |
| Pricing model | Fixed project, T&M, Dedicated team | Fixed project, T&M, Dedicated team |
| Min. engagement | $20K | $20K |
| Primary tech stack | Python, TensorFlow, PyTorch | TensorFlow, PyTorch, Keras |
| Industries served | fintech, energy, retail, manufacturing, logistics | transportation, healthcare, manufacturing, financial services, edtech |
Addepto vs Scopic: overview
Addepto
Addepto is a Poland-based AI consulting and development firm focused on end-to-end machine learning solutions for mid-market and enterprise clients. The company specializes in building data pipelines, custom ML models, and decision-support tools with particular depth in financial services, energy, and retail — industries where regulatory awareness and data governance are non-negotiable. Addepto covers the full stack from data engineering through model development, deployment, and integration.
Scopic
Scopic is a globally distributed software development company founded in 2006 and headquartered in Marlborough, Massachusetts. The company employs 250–500 professionals and has 20 years of experience building custom ML systems using TensorFlow, neural networks, PyTorch, and computer vision pipelines. Scopic has confirmed production ML deployments across transportation, healthcare, manufacturing, and financial services.
Services and capabilities: Addepto vs Scopic
| Capability | Addepto | Scopic |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| ML consulting | ✓ | ✗ |
| Deep learning | ✓ | ✓ |
| NLP | ✗ | ✓ |
| Computer vision | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| Predictive analytics | ✓ | ✓ |
| Generative AI | ✓ | ✗ |
| Data engineering | ✓ | ✗ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Addepto vs Scopic
| Framework / platform | Addepto | Scopic |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| Scikit-Learn | ✓ | ✓ |
| LangChain | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
| GCP Vertex AI | N/A | N/A |
| Kubernetes | N/A | N/A |
| Apache Spark | ✓ | N/A |
| MLflow | N/A | N/A |
Pricing comparison: Addepto vs Scopic
| Criterion | Addepto | Scopic |
|---|---|---|
| Minimum engagement | $20K | $20K |
| Engagement models | Fixed project, T&M, Dedicated team | Fixed project, T&M, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Addepto vs Scopic
| Dimension | Addepto | Scopic |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | fintech, energy, retail | transportation, healthcare, manufacturing |
| Best use cases | Credit risk scoring and fraud detection model development for fintech platforms, Energy demand forecasting and grid optimization using time-series ML models | Custom computer vision pipeline development for transportation safety or logistics automation, Deep learning model development for medical image analysis or clinical data classification |
| Typical project type | Fixed project | Fixed project |
Addepto vs Scopic: pros and cons
| Addepto | |
|---|---|
| + | Genuine depth in finance and energy ML — not a generalist firm claiming vertical expertise |
| + | Covers the full stack from data pipeline architecture through model deployment |
| + | Generative AI capability alongside classical ML for hybrid solution architectures |
| + | Warsaw delivery hub provides competitive rates with EU-based data handling |
| + | Accessible minimum engagement for early-stage ML projects or POCs |
| - | Smaller team than enterprise-tier firms; large-scale concurrent programmes may strain capacity |
| - | Less US-based client management than North American competitors |
| - | Limited public case studies compared to larger firms with dedicated marketing teams |
| Scopic | |
|---|---|
| + | 20 years of distributed ML delivery with consistent process maturity across time zones |
| + | Deep computer vision and neural network expertise with production deployments in transportation |
| + | Custom ML system engineering — not platform-reliant solutions dependent on third-party services |
| + | Accessible minimum engagement and competitive rates for the level of specialization offered |
| + | Healthcare ML experience with sensitivity to data privacy and regulatory considerations |
| - | Distributed-first model may introduce coordination overhead for clients preferring on-site collaboration |
| - | Less public brand presence than US-headquartered firms of similar capability |
| - | Less generative AI and LLM tooling depth than newer AI-first firms |
Who should choose Addepto?
Addepto is the right choice for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness.
End-to-end AI/ML delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability. Minimum engagement starts at $20K. Works best with clients in fintech, energy, retail, manufacturing, logistics.
Who should choose Scopic?
Scopic is the right choice for organizations needing fully custom ML engineering with 20+ years of distributed team experience and strong computer vision and deep learning capability.
20+ years as a distributed software company gives Scopic strong custom ML engineering discipline with confirmed production deployments across transportation and healthcare. Minimum engagement starts at $20K. Works best with clients in transportation, healthcare, manufacturing, financial services, edtech.
Decision matrix: Addepto vs Scopic
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Addepto |
| You need a large dedicated team for an ongoing programme | Addepto |
| Your budget is at the lower end | Addepto |
| You need specialist depth in a specific vertical | Addepto |
| 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: Addepto vs Scopic
| Use case | Addepto fit | Scopic fit | Winner |
|---|---|---|---|
| Credit risk scoring and fraud detection model development for fintech platforms | Strong | Limited | Addepto |
| Energy demand forecasting and grid optimization using time-series ML models | Strong | Limited | Addepto |
| Custom computer vision pipeline development for transportation safety or logistics automation | Strong | Strong | Both equally |
| Deep learning model development for medical image analysis or clinical data classification | Limited | Strong | Scopic |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Addepto vs Scopic
Addepto (4.2/5) is the stronger overall choice for most Machine Learning Development projects. End-to-end AI/ML delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability. It is best for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness.
Scopic (3.9/5) is the better choice when organizations needing fully custom ML engineering with 20+ years of distributed team experience and strong computer vision and deep learning capability. If your situation matches those criteria, Scopic is a competitive option.
Related comparisons
Addepto vs Scopic FAQ
Is Addepto better than Scopic?
Addepto (4.2/5) scores higher overall, but "better" depends on your use case. Addepto is better for mid-market companies in finance, energy, or retail needing bespoke ML models with full data pipeline support and sector-specific regulatory awareness. Scopic is better for organizations needing fully custom ML engineering with 20+ years of distributed team experience and strong computer vision and deep learning capability.
How do Addepto and Scopic differ in pricing?
Addepto uses fixed project, t&m, dedicated team pricing with a minimum engagement of $20K. Scopic uses fixed project, t&m, dedicated team pricing with a minimum engagement of $20K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Scopic?
Scopic 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 Scopic?
Addepto's primary differentiator is: end-to-end ai/ml delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability. Scopic's primary differentiator is: 20+ years as a distributed software company gives scopic strong custom ml engineering discipline with confirmed production deployments across transportation and healthcare. They also differ in team size (50–200 vs 250–500), minimum engagement ($20K vs $20K), and primary industries served (fintech, energy vs transportation, healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.