Quantiphi vs ELEKS: full comparison for 2026
Quick verdict
Quantiphi (4.2/5) edges ahead of ELEKS (4.1/5) overall. Quantiphi is the better choice for AWS-standardized enterprises, deepest AWS AI/ML credentials. ELEKS is the stronger option for Enterprises, established European engineering plus data science. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs ELEKS: head-to-head summary
| Criterion | Quantiphi | ELEKS |
|---|---|---|
| Founded | 2013 | 1991 |
| HQ | Marlborough, USA | Tallinn, Estonia (engineering hub: Lviv, Ukraine) |
| Team size | 1,001–5,000 | 1,001–5,000 |
| Rating | 4.2 / 5 | 4.1 / 5 |
| Primary differentiator | Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status | One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades |
| Pricing model | Not published; enterprise project engagements | Time & Material, Fixed project |
| Min. engagement | Not published | Not published |
| Primary tech stack | AWS SageMaker, Amazon Bedrock, AWS | Python, Cloud ML platforms (AWS/Azure/GCP), Data engineering tooling |
| Industries served | Public sector, Healthcare, Financial services, Media | Financial services, Healthcare, Manufacturing, Insurance |
Quantiphi vs ELEKS: overview
Quantiphi
Quantiphi is a digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, focused on applied artificial intelligence, machine learning, and data science for complex business problems. Headquartered in Marlborough, Massachusetts, the company operates across six global locations and reports between 1,000 and 5,000 employees. Quantiphi holds AWS Premier Global Consulting Partner status and was named the first Preferred Amazon Quick Global SI Partner by the AWS Generative AI Innovation Center, alongside being recognized as 2025 AWS Public Sector Global GenAI Consulting Partner of the Year.
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.
Services and capabilities: Quantiphi vs ELEKS
| Capability | Quantiphi | ELEKS |
|---|---|---|
| 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: Quantiphi vs ELEKS
| Framework / platform | Quantiphi | ELEKS |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | ✓ | N/A |
| Amazon Bedrock | ✓ | N/A |
| Google Cloud | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Kubernetes | ✓ | ✓ |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: Quantiphi vs ELEKS
| Criterion | Quantiphi | ELEKS |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Enterprise project engagement, Managed AI services | Time & Material, Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Quantiphi vs ELEKS
| Dimension | Quantiphi | ELEKS |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Public sector, Healthcare, Financial services | Financial services, Healthcare, Manufacturing |
| Best use cases | Building and deploying ML models on AWS SageMaker at enterprise scale, Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support | 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 |
| Typical project type | Enterprise project engagement | Time & Material |
Quantiphi vs ELEKS: pros and cons
| Quantiphi | |
|---|---|
| + | Strongest documented AWS partnership tier (Premier Global Consulting Partner) among companies in this comparison. |
| + | 2025 AWS Public Sector Global GenAI Consulting Partner of the Year recognition. |
| + | Reported $630.2M in revenue signals substantial scale and financial stability. |
| + | Multi-location global presence supports enterprise clients needing regional delivery. |
| - | Heavy AWS specialization may be less useful for clients standardized on Azure or GCP. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Employee count range (1,000–5,000) is wide, making exact delivery capacity hard to pin down. |
| - | Pricing model and minimum engagement are not published. |
| ELEKS | |
|---|---|
| + | Over three decades of continuous operation, unusually long for this category. |
| + | Large engineering bench (2,000+ employees) supports substantial delivery capacity. |
| + | Data science practice is embedded within a mature enterprise software engineering organization. |
| + | Multi-region European and North American office footprint. |
| - | AI/ML is one practice area within a much broader enterprise software portfolio, not the company's primary specialization. |
| - | Specific, named ML case studies with quantified outcomes are limited in available public sources. |
| - | Pricing minimums are not published. |
| - | Long operating history does not necessarily translate into deep modern ML/LLM specialization relative to newer, AI-first boutiques. |
Who should choose Quantiphi?
A typical fit: building and deploying ML models on AWS SageMaker at enterprise scale.
Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. Minimum engagement starts at Not published. Works best with clients in Public sector, Healthcare, Financial services, Media.
Who should choose ELEKS?
A typical fit: running an enterprise-scale data science initiative alongside a broader software modernization program.
One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. Minimum engagement starts at Not published. Works best with clients in Financial services, Healthcare, Manufacturing, Insurance.
Decision matrix: Quantiphi vs ELEKS
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | ELEKS |
| You need a large dedicated team for an ongoing programme | ELEKS |
| Your budget is at the lower end | Compare: Quantiphi (Not published) vs ELEKS (Not published) |
| You need specialist depth in a specific vertical | Quantiphi |
| 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: Quantiphi vs ELEKS
| Use case | Quantiphi fit | ELEKS fit | Winner |
|---|---|---|---|
| Building and deploying ML models on AWS SageMaker at enterprise scale | Strong | Strong | Both equally |
| Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support | Strong | Strong | Both equally |
| Running an enterprise-scale data science initiative alongside a broader software modernization program | Strong | Strong | Both equally |
| Engaging a long-tenured, stable partner for a multi-year digital transformation that includes ML components | Limited | Strong | ELEKS |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Strong | Limited | Quantiphi |
Verdict: Quantiphi vs ELEKS
Quantiphi (4.2/5) is the stronger overall choice for most ML Model Development projects. Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status.
ELEKS (4.1/5) is worth a look if you need engaging a long-tenured, stable partner for a multi-year digital transformation that includes ML components. If your situation matches that, ELEKS is a competitive option.
Related comparisons
Quantiphi vs ELEKS FAQ
Is Quantiphi better than ELEKS?
Quantiphi (4.2/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: strongest documented AWS partnership tier (Premier Global Consulting Partner) among companies in this comparison. ELEKS's strongest advantage: over three decades of continuous operation, unusually long for this category.
How do Quantiphi and ELEKS differ in pricing?
Quantiphi uses not published; enterprise project engagements pricing with a minimum engagement of Not published. ELEKS uses time & material, fixed project 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: Quantiphi or ELEKS?
Quantiphi 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 Quantiphi and ELEKS?
Quantiphi's primary differentiator is: deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. ELEKS's primary differentiator is: one of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. They also differ in team size (1,001–5,000 vs 1,001–5,000), minimum engagement (Not published vs Not published), and primary industries served (Public sector, Healthcare vs Financial services, Healthcare).