Best Machine Learning Agencies

Yalantis vs Avenga: full comparison for 2026

Last updated: July 2026

Quick verdict

Yalantis (3.9/5) edges ahead of Avenga (3.9/5) overall. Yalantis is the better choice for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering. Avenga is the stronger option for european enterprise clients seeking large-scale ML and digital transformation from a well-resourced regional firm. The right choice depends on your project size, budget, and required tech stack.

Yalantis vs Avenga: head-to-head summary

Criterion Yalantis Avenga
Founded 2008 2019
HQ Kyiv, Ukraine Prague, Czech Republic
Team size 200–400 3,884
Rating 3.9 / 5 3.9 / 5
Best for Healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering European enterprise clients seeking large-scale ML and digital transformation from a well-resourced regional firm
Pricing model Fixed project, T&M T&M, dedicated team
Min. engagement $25K+ $50K+
Primary tech stack Python, TensorFlow, PyTorch Python, Azure, AWS
Industries served healthcare, fintech, saas, logistics, manufacturing financial, healthcare, retail, telecommunications, manufacturing

Yalantis vs Avenga: overview

Yalantis

Yalantis was founded in 2008 and operates with a focus on compliance-first IoT and software engineering alongside machine learning consulting. The company's ML team provides domain-specific consulting, model deployment, and ongoing support, with depth in regulated industries including healthcare and fintech. ML consultants hold master's degrees in machine learning and have production data science experience. (Founded year per Tracxn; specialisation per Yalantis official website.)

Avenga

Avenga was formed in 2019 through the merger of multiple European IT firms and is headquartered in Prague, Czech Republic, with approximately 3,884 employees as of December 2025 (per Avenga LinkedIn). The company provides AI, ML, and digital transformation services for enterprise clients, drawing on its merged entities' combined delivery capabilities across finance, healthcare, and retail. (Employee count per Avenga LinkedIn, December 2025; merger history per Avenga Wikipedia.)

Services and capabilities: Yalantis vs Avenga

Capability Yalantis Avenga
Custom ML build
ML consulting
Computer vision
NLP / LLM
Predictive analytics
MLOps
Data engineering
Generative AI
Staff augmentation
Fixed-price projects
Dedicated team model

Tech stack comparison: Yalantis vs Avenga

Framework / platform Yalantis Avenga
Python
TensorFlow
PyTorch N/A
AWS SageMaker N/A N/A
Azure ML N/A N/A

Pricing comparison: Yalantis vs Avenga

Criterion Yalantis Avenga
Minimum engagement $25K+ $50K+
Engagement models Fixed project, T&M, Retainer T&M, Dedicated team
Rate transparency Minimum disclosed Minimum disclosed
Price tier Accessible Accessible

Target audience comparison: Yalantis vs Avenga

Dimension Yalantis Avenga
Best company size Startup to mid-market Startup to mid-market
Best industries healthcare, fintech, saas financial, healthcare, retail
Best use cases Compliance-aware ML model for healthcare data, Predictive analytics for fintech risk management Enterprise ML platform within digital transformation programme, Data modernisation with ML integration for financial services
Typical project type Fixed project T&M

Yalantis vs Avenga: pros and cons

Yalantis
+ Compliance-first approach for regulated healthcare and fintech projects
+ Full-lifecycle ML: from consulting through deployment and support
+ Master's-qualified ML consultants — verifiable technical depth
+ IoT integration experience alongside ML — rare combination
- Ukraine-based delivery carries geographic risk considerations for some clients
- Less suited to pure data science research or exploratory projects
Avenga
+ 3,800+ engineers — strong capacity for large-scale programmes
+ European delivery presence across multiple countries
+ Multi-sector ML experience: finance, healthcare, retail, telecom
- Formed from merger in 2019 — company culture and process integration still maturing
- ML is part of broader IT consulting — not ML-first
- Large minimum engagements not suited to startups or SMBs

Who should choose Yalantis?

Yalantis is the right choice for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering.

Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs. Minimum engagement starts at $25K+. Works best with clients in healthcare, fintech, saas, logistics, manufacturing.

Who should choose Avenga?

Avenga is the right choice for european enterprise clients seeking large-scale ML and digital transformation from a well-resourced regional firm.

Formed from a 2019 merger — 3,800+ engineers across Europe for large ML and digital transformation programmes. Minimum engagement starts at $50K+. Works best with clients in financial, healthcare, retail, telecommunications, manufacturing.

Decision matrix: Yalantis vs Avenga

Your situation Recommended choice
You need full-ownership delivery on a defined project scope Yalantis
You need a large dedicated team for an ongoing programme Avenga
Your budget is at the lower end Yalantis
You need specialist depth in a specific vertical Yalantis
You need staff augmentation or team extension Neither; consider alternatives that offer staff aug
You need consulting before committing to a build Yalantis

Use case fit: Yalantis vs Avenga

Use case Yalantis fit Avenga fit Winner
Compliance-aware ML model for healthcare data Strong Limited Yalantis
Predictive analytics for fintech risk management Strong Limited Yalantis
Enterprise ML platform within digital transformation programme Strong Strong Both equally
Data modernisation with ML integration for financial services Strong Strong Both equally
Fixed-price build Limited Limited Both equally
Staff augmentation Limited Limited Both equally

Verdict: Yalantis vs Avenga

Yalantis (3.9/5) is the stronger overall choice for most Machine Learning projects. Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs. It is best for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering.

Avenga (3.9/5) is the better choice when european enterprise clients seeking large-scale ML and digital transformation from a well-resourced regional firm. If your situation matches those criteria, Avenga is a competitive option.

Related comparisons

Yalantis vs Avenga FAQ

Is Yalantis better than Avenga?

Yalantis (3.9/5) scores higher overall, but "better" depends on your use case. Yalantis is better for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering. Avenga is better for european enterprise clients seeking large-scale ML and digital transformation from a well-resourced regional firm.

How do Yalantis and Avenga differ in pricing?

Yalantis uses fixed project, t&m pricing with a minimum engagement of $25K+. Avenga uses t&m, dedicated team pricing with a minimum engagement of $50K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.

Which is better for enterprise: Yalantis or Avenga?

Yalantis is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.

What are the main differences between Yalantis and Avenga?

Yalantis's primary differentiator is: compliance-first ml delivery — particularly strong for healthcare and regulated fintech with iot integration needs. Avenga's primary differentiator is: formed from a 2019 merger — 3,800+ engineers across europe for large ml and digital transformation programmes. They also differ in team size (200–400 vs 3,884), minimum engagement ($25K+ vs $50K+), and primary industries served (healthcare, fintech vs financial, healthcare).

Last reviewed: July 2026. Verify all details directly with each agency before making a decision.