Best ML Model Development Companies

ELEKS

European software engineering company founded in 1991 with a dedicated data science practice.

Founded 1991 | Tallinn, Estonia (engineering hub: Lviv, Ukraine) | 1,001–5,000 employees
custom-model-trainingdata-engineering-mlmlops-pipeline

What is ELEKS?

ELEKS is a long-running European software engineering company founded in 1991, with corporate presence in Tallinn, Estonia and its largest engineering hub in Lviv, Ukraine, alongside additional offices across Europe and North America. The company reports more than 2,000 employees and operates a dedicated data science and AI practice layered onto its broader enterprise software engineering services. Its history predates the modern AI/ML consulting wave by roughly three decades, giving it an unusually long operating track record compared to most peers in this list.

ELEKS was founded in 1991 and is headquartered in Tallinn, Estonia (engineering hub: Lviv, Ukraine). The firm employs 1,001–5,000 people and works primarily with clients in Financial services, Healthcare, Manufacturing, Insurance sectors. Its primary differentiator is: One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades.

ELEKS tech stack and services

PythonCloud ML platforms (AWS/Azure/GCP)Data engineering toolingKubernetes
Service area
Custom Model Training
Data Engineering for ML
MLOps Pipeline

ELEKS use cases

Short answer: ELEKS is best suited for Enterprises, established European engineering plus data science.

Use case
Running an enterprise-scale data science initiative alongside a broader software modernization program
Engaging a long-tenured, stable partner for a multi-year digital transformation that includes ML components
Building ML capability into an existing large enterprise software system
Combining data engineering and model development within one established European vendor

ELEKS pricing

Short answer: ELEKS uses a time & material, fixed project pricing approach. Minimum engagement starts at Not published.

Engagement model Typical range Best for
Time & Material Variable; depends on team size Large programmes or team augmentation
Fixed project From Not published Well-defined scope
Dedicated team Variable; depends on team size Large programmes or team augmentation
ELEKS does not publish a public rate card. Contact them directly via their website to get project-specific pricing.

ELEKS pros and cons

Advantages Things to consider
+Over three decades of continuous operation, unusually long for this category. -AI/ML is one practice area within a much broader enterprise software portfolio, not the company's primary specialization.
+Large engineering bench (2,000+ employees) supports substantial delivery capacity. -Specific, named ML case studies with quantified outcomes are limited in available public sources.
+Data science practice is embedded within a mature enterprise software engineering organization. -Pricing minimums are not published.
+Multi-region European and North American office footprint. -Long operating history does not necessarily translate into deep modern ML/LLM specialization relative to newer, AI-first boutiques.

ELEKS vs alternatives

How ELEKS compares to the other top ML Model Development companies.

Company Best for Key difference Rating Compare
Tensorway Mid-market fintech and SaaS, hybrid forecasting models. Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. 4.8 Full comparison
Neurons Lab Financial services, engineering-led production AI. End-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services. 4.6 Full comparison
DataRoot Labs Startups needing a senior AI-only team, LLM/CV projects. Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University). 4.6 Full comparison
Miquido Companies wanting ML/CV bundled with mobile/web engineering. 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. 4.6 Full comparison
Provectus Mid-market companies, cloud data plus ML in one. Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves. 4.5 Full comparison
Neoteric Organizations wanting a structured AI feasibility phase. Two-decade operating history combined with a formal upfront feasibility-assessment stage before any model-building work begins. 4.5 Full comparison
Addepto Cost-conscious teams, MLOps consulting or advisory-only. Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option. 4.4 Full comparison
N-iX Enterprise buyers, certified data-platform plus ML partner. Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice. 4.4 Full comparison
InData Labs Companies needing predictive-analytics or CV, documented accuracy. Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. 4.3 Full comparison
MobiDev Small/mid-size companies, dedicated ML consulting arm. Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. 4.3 Full comparison
Sciforce Research-oriented boutique, NLP, DSP, computer vision. R&D-first culture with named specializations in digital signal processing and NLP that are less commonly offered as distinct practice areas by peers. 4.2 Full comparison
Sigmoid Enterprises with data-infrastructure bottlenecks ahead of ML. Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse. 4.2 Full comparison
Tredence Enterprises, vertical analytics for supply chain at scale. Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting. 4.2 Full comparison
Quantiphi AWS-standardized enterprises, deepest AWS AI/ML credentials. Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. 4.2 Full comparison
Sigma Software Group Companies wanting a Snowflake-certified data platform plus ML. Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). 4.1 Full comparison
Intellectsoft Companies wanting enterprise clients, dedicated AI Lab. Unusually strong roster of large, publicly named enterprise clients (EY, Qualcomm, London Stock Exchange) for a company of its relatively modest team size. 4.1 Full comparison
Fractal Analytics Large enterprises, scaled AI partner with a research... Maintains a dedicated internal foundational AI research team alongside client delivery work, and is now a publicly listed company (NSE/BSE) rather than privately held like most peers of similar size. 4.1 Full comparison
Xebia Enterprises, craftsmanship-rooted production AI consultancy. Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone. 4.0 Full comparison
Grid Dynamics Fortune 1000 companies, publicly-traded ML partner. The only publicly traded company (NASDAQ: GDYN) in this comparison among the mid-to-large tier, giving buyers audited financial transparency unavailable from private peers. 4.0 Full comparison
Iterate.ai Regulated enterprises, fully private-infrastructure AI deployment. Purpose-built for on-premise/private-infrastructure AI deployment, so client data and proprietary code never leave the client's own environment. 4.0 Full comparison
Modus Create Distributed orgs, remote-first data plus AI/ML delivery. Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. 4.0 Full comparison
Aptus Data Labs Boutique India-based data engineering, AWS AI depth. Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. 4.0 Full comparison
SoftServe Enterprises, edge CV and asset-monitoring ML at scale. Only company in this list simultaneously holding AWS Premier, Google Cloud AI/ML Specialization, and NVIDIA Elite Consulting Partner status, reflecting particular strength in edge and GPU-accelerated computer vision. 4.0 Full comparison
DataRobot Enterprises standardizing on one automated ML platform. The only platform-first vendor in this comparison, meaning model development work happens on and around DataRobot's own automated ML software rather than being platform-agnostic. 3.9 Full comparison
Persistent Systems Mid-market/enterprise buyers, MLOps plus 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. 3.9 Full comparison
EPAM Systems Very large enterprises, proprietary AI orchestration platform. Proprietary EPAM DIAL platform for enterprise AI orchestration, combined with the 2025 AWS Global Innovation Partner of the Year distinction, an award-level differentiator not held by most peers. 3.9 Full comparison
Globant Large enterprises, pre-packaged industry "AI Pods". Only company in this list organized around a formal "studio + AI Pods" delivery model, and the only one with an IDC MarketScape Worldwide Leader in AI Services designation. 3.9 Full comparison
LTIMindtree BFSI and tech enterprises, dedicated ModelOps and governance. Explicit ModelOps templates and model-governance/responsible-AI tooling as named, productized capabilities rather than only bespoke consulting delivery, backed by an IBM watsonx Center of Excellence. 3.9 Full comparison
Cognizant Healthcare-focused enterprises, large AI/analytics consulting bench. Dedicated, named MLOps platform specifically built for healthcare, combined with one of the largest disclosed data/AI consultant headcounts (23,000+) in this comparison. 3.9 Full comparison
HCLTech Very large enterprises, full-stack AI from chip to... Unusually broad "chip-to-cloud" AI stack claim backed by two named proprietary platforms (Graviton for ML development, AION for AI lifecycle management), a combination not matched by most peers in this list. 3.9 Full comparison
Infosys Global enterprises, large library of reusable AI assets. Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds. 3.9 Full comparison
Accenture Largest global enterprises, AI within multi-year transformation. By far the largest scale of any company in this comparison (approximately 779,000 employees, $69.67B FY2025 revenue), trading breadth and compliance maturity for less niche, hands-on model-engineering depth than boutique specialists. 3.9 Full comparison
Devbridge (a Cognizant company) Clients wanting Devbridge's model within Cognizant's structure. The clearest ownership-change disclosure in this comparison: a formerly independent boutique now operating explicitly as a Cognizant subsidiary, combining boutique delivery heritage with large-parent-company backing. 3.8 Full comparison

ELEKS FAQ

What is ELEKS?

ELEKS is a long-running European software engineering company founded in 1991, with corporate presence in Tallinn, Estonia and its largest engineering hub in Lviv, Ukraine, alongside additional offices across Europe and North America. The company reports more than 2,000 employees and operates a dedicated data science and AI practice layered onto its broader enterprise software engineering services. Its history predates the modern AI/ML consulting wave by roughly three decades, giving it an unusually long operating track record compared to most peers in this list.

How much does ELEKS charge?

ELEKS uses time & material, fixed project pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.

What tech stack does ELEKS use?

ELEKS works with Python, Cloud ML platforms (AWS/Azure/GCP), Data engineering tooling, Kubernetes. Primary industries served include Financial services, Healthcare, Manufacturing, Insurance.

Is ELEKS right for enterprise?

Enterprises, established European engineering plus data science. 1,001–5,000 team size. Key consideration: AI/ML is one practice area within a much broader enterprise software portfolio, not the company's primary specialization.

What are the best ELEKS alternatives?

The best alternatives to ELEKS depend on your use case. Top options are:

  • Tensorway: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
  • Neurons Lab: end-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services.
  • DataRoot Labs: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).
See full alternatives list

Compare ELEKS with other ML Model Development companies