Best Machine Learning Agencies

InData Labs vs ELEKS: full comparison for 2026

Last updated: July 2026

Quick verdict

InData Labs (4.6/5) edges ahead of ELEKS (3.9/5) overall. InData Labs is the better choice for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems. ELEKS is the stronger option for enterprise clients needing ML within a full-service technology consulting engagement with long-term continuity. The right choice depends on your project size, budget, and required tech stack.

InData Labs vs ELEKS: head-to-head summary

Criterion InData Labs ELEKS
Founded 2014 1991
HQ Nicosia, Cyprus Lviv, Ukraine
Team size 80+ 2,000+
Rating 4.6 / 5 3.9 / 5
Best for Fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems Enterprise clients needing ML within a full-service technology consulting engagement with long-term continuity
Pricing model Fixed project, T&M T&M, dedicated team
Min. engagement $15K $50K+
Primary tech stack Python, TensorFlow, PyTorch Python, TensorFlow, PyTorch
Industries served fintech, healthcare, saas, retail, logistics financial, healthcare, manufacturing, retail, logistics

InData Labs vs ELEKS: overview

InData Labs

InData Labs is a data science and AI consultancy founded in 2014, with headquarters in Nicosia, Cyprus and offices in Lithuania and the US. The firm covers the full ML stack: generative AI (LLMs, RAG systems, AI agents), predictive ML (recommendation engines, churn models, computer vision), data engineering, and DevOps for AI infrastructure. With 80+ data science professionals, it focuses on mid-market clients in fintech, healthcare, SaaS, retail, and logistics. (Team size per company LinkedIn; independently verified.)

ELEKS

ELEKS was established in 1991 and is headquartered in Lviv, Ukraine, with offices across Europe and North America. The company has 2,000+ engineers and delivers technology consulting, AI/ML services, and enterprise software for Fortune 500 clients globally. ML services include predictive analytics, computer vision, NLP, and intelligent automation. ELEKS celebrated its 30th anniversary in 2021. (Founding year and team size per ELEKS official website and KyivPost article.)

Services and capabilities: InData Labs vs ELEKS

Capability InData Labs ELEKS
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: InData Labs vs ELEKS

Framework / platform InData Labs ELEKS
Python
TensorFlow
PyTorch
AWS SageMaker N/A N/A
Azure ML N/A N/A

Pricing comparison: InData Labs vs ELEKS

Criterion InData Labs ELEKS
Minimum engagement $15K $50K+
Engagement models Fixed project, T&M T&M, Dedicated team
Rate transparency Minimum disclosed Minimum disclosed
Price tier Accessible Accessible

Target audience comparison: InData Labs vs ELEKS

Dimension InData Labs ELEKS
Best company size Startup to mid-market Startup to mid-market
Best industries fintech, healthcare, saas financial, healthcare, manufacturing
Best use cases GenAI and RAG-based knowledge management system, Churn prediction model for SaaS ML integration into enterprise ERP or CRM, Computer vision for manufacturing quality control
Typical project type Fixed project T&M

InData Labs vs ELEKS: pros and cons

InData Labs
+ 10+ years of pure ML/AI focus — not a repositioned generalist practice
+ Production-grade GenAI including RAG and AI agent systems
+ Covers the full stack: ML engineering, data engineering, and MLOps
+ Strong track record in regulated industries (fintech, healthcare)
+ Verified Clutch and DesignRush ratings across multiple client reviews
- Smaller team (80+) limits capacity for very large concurrent programmes
- Not a staffing platform — less suited to pure team augmentation needs
ELEKS
+ 30+ years of enterprise delivery history — very low company risk
+ 2,000+ engineers across multiple disciplines
+ Proven Fortune 500 delivery capability across multiple verticals
+ Wide industry coverage including manufacturing and financial services
- ML practice is secondary to broader software engineering — not ML-first
- Ukraine-based delivery carries geographic risk considerations for some clients
- Less agile than boutique ML specialists for short exploratory projects

Who should choose InData Labs?

InData Labs is the right choice for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems.

Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries. Minimum engagement starts at $15K. Works best with clients in fintech, healthcare, saas, retail, logistics.

Who should choose ELEKS?

ELEKS is the right choice for enterprise clients needing ML within a full-service technology consulting engagement with long-term continuity.

30+ years of enterprise software delivery — ML within a stable, large-org structure for risk-averse buyers. Minimum engagement starts at $50K+. Works best with clients in financial, healthcare, manufacturing, retail, logistics.

Decision matrix: InData Labs vs ELEKS

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

Use case fit: InData Labs vs ELEKS

Use case InData Labs fit ELEKS fit Winner
GenAI and RAG-based knowledge management system Strong Limited InData Labs
Churn prediction model for SaaS Strong Limited InData Labs
ML integration into enterprise ERP or CRM Limited Strong ELEKS
Computer vision for manufacturing quality control Strong Strong Both equally
Fixed-price build Limited Limited Both equally
Staff augmentation Limited Limited Both equally

Verdict: InData Labs vs ELEKS

InData Labs (4.6/5) is the stronger overall choice for most Machine Learning projects. Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries. It is best for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems.

ELEKS (3.9/5) is the better choice when enterprise clients needing ML within a full-service technology consulting engagement with long-term continuity. If your situation matches those criteria, ELEKS is a competitive option.

Related comparisons

InData Labs vs ELEKS FAQ

Is InData Labs better than ELEKS?

InData Labs (4.6/5) scores higher overall, but "better" depends on your use case. InData Labs is better for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems. ELEKS is better for enterprise clients needing ML within a full-service technology consulting engagement with long-term continuity.

How do InData Labs and ELEKS differ in pricing?

InData Labs uses fixed project, t&m pricing with a minimum engagement of $15K. ELEKS 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: InData Labs or ELEKS?

ELEKS 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 InData Labs and ELEKS?

InData Labs's primary differentiator is: deep ml and genai specialist with 10+ years of production deployments across regulated industries. ELEKS's primary differentiator is: 30+ years of enterprise software delivery — ml within a stable, large-org structure for risk-averse buyers. They also differ in team size (80+ vs 2,000+), minimum engagement ($15K vs $50K+), and primary industries served (fintech, healthcare vs financial, healthcare).

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