ScienceSoft vs Ciklum: full comparison for 2026
Last updated: July 2026
Quick verdict
ScienceSoft (3.9/5) edges ahead of Ciklum (3.6/5) overall. ScienceSoft is the better choice for manufacturing, healthcare, and oil & gas companies needing a long-established US-headquartered ML partner with Microsoft and AWS credentials. Ciklum is the stronger option for global enterprises seeking AI features embedded in large-scale digital product programmes with Experience Engineering focus. The right choice depends on your project size, budget, and required tech stack.
ScienceSoft vs Ciklum: head-to-head summary
| Criterion | ScienceSoft | Ciklum |
|---|---|---|
| Founded | 1989 | 2002 |
| HQ | McKinney, TX, USA | London, UK |
| Team size | 700–1,000 | 4,000+ |
| Rating | 3.9 / 5 | 3.6 / 5 |
| Best for | Manufacturing, healthcare, and oil & gas companies needing a long-established US-headquartered ML partner with Microsoft and AWS credentials | Global enterprises seeking AI features embedded in large-scale digital product programmes with Experience Engineering focus |
| Pricing model | Fixed project, dedicated team, T&M | Dedicated team, T&M |
| Min. engagement | $50K | $100K |
| Primary tech stack | Python, Scikit-learn, TensorFlow | Python, LangChain, OpenAI API |
| Industries served | Manufacturing, Healthcare, SaaS, Logistics, Fintech | Fintech, Healthcare, E-commerce, SaaS, Logistics |
ScienceSoft vs Ciklum: overview
ScienceSoft
ScienceSoft is a global IT services company founded in 1989 and headquartered in McKinney, Texas, with 700+ employees and delivery centres in Eastern Europe and the Americas. The firm's machine learning practice focuses on custom ML solutions for manufacturing, healthcare, and oil & gas industries, with a 35-year IT track record across 20+ countries. ScienceSoft's ML engineers design and implement models for demand forecasting, quality prediction, medical diagnostics, and production optimisation. The company holds Microsoft Gold Partnership and AWS Partner certifications.
Ciklum
Ciklum is a global Experience Engineering firm headquartered in London, UK, founded in 2002, with 4,000+ employees serving 250+ global enterprise clients. The company acquired GoSolve Group in 2025, adding cloud-native development and high-performance computing capability. Ciklum's AI services include generative AI development, ML integration into digital products, and AI-powered SDLC acceleration. The firm delivers next-generation product engineering and AI-powered customer experiences for large enterprises and digital disruptors.
Services and capabilities: ScienceSoft vs Ciklum
| Capability | ScienceSoft | Ciklum |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP & text analytics | ✗ | ✓ |
| MLOps & deployment | ✓ | ✓ |
| Generative AI | ✗ | ✓ |
| ML consulting & strategy | ✓ | ✗ |
| Staff augmentation | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: ScienceSoft vs Ciklum
| Framework / platform | ScienceSoft | Ciklum |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| Scikit-learn | ✓ | N/A |
| AWS SageMaker | ✓ | N/A |
| MLflow | ✓ | ✓ |
| Hugging Face | N/A | N/A |
| LangChain | N/A | ✓ |
| Docker/Kubernetes | N/A | N/A |
| Databricks | N/A | ✓ |
Pricing comparison: ScienceSoft vs Ciklum
| Criterion | ScienceSoft | Ciklum |
|---|---|---|
| Minimum engagement | $50K | $100K |
| Engagement models | Fixed project, Dedicated team, Time & materials | Dedicated team, Time & materials, Consulting retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: ScienceSoft vs Ciklum
| Dimension | ScienceSoft | Ciklum |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Manufacturing, Healthcare, SaaS | Fintech, Healthcare, E-commerce |
| Best use cases | Demand forecasting and production optimisation ML for manufacturing plants, Clinical decision support ML for healthcare providers and hospital systems | Generative AI features integrated into large enterprise digital products, ML-powered personalisation for consumer-facing applications at scale |
| Typical project type | Fixed project | Dedicated team |
ScienceSoft vs Ciklum: pros and cons
| ScienceSoft | |
|---|---|
| + | 35-year delivery track record provides confidence for regulated industry procurement requirements |
| + | Microsoft Gold and AWS Partner certifications verify cloud ML deployment credentials |
| + | Deep manufacturing, healthcare, and oil & gas ML vertical expertise with named case studies |
| + | 700+ employees provide delivery capacity for large concurrent enterprise programmes |
| + | US Texas HQ for North American enterprise client engagement and account management |
| - | ML is one of many IT service lines — not a pure-play AI specialist firm |
| - | Primary vertical focus on manufacturing and healthcare may not serve other sectors equally well |
| - | Higher minimum engagement than boutique ML alternatives at similar quality tier |
| Ciklum | |
|---|---|
| + | 4,000+ employees serving 250+ enterprises demonstrates delivery scale and breadth |
| + | Generative AI services alongside traditional ML within product engineering |
| + | GoSolve acquisition (2025) adds cloud-native and high-performance computing depth |
| + | London HQ provides EU and UK enterprise relationship management |
| + | Experience Engineering focus connects ML outcomes to user-facing product features |
| - | $100K minimum engagement limits access for smaller and mid-market companies |
| - | AI is part of a broader service offering — not an ML-first or AI-specialist firm |
| - | Less publicly documented in pure ML model research than boutique ML competitors |
Who should choose ScienceSoft?
ScienceSoft is the right choice for manufacturing, healthcare, and oil & gas companies needing a long-established US-headquartered ML partner with Microsoft and AWS credentials.
35-year IT firm with Microsoft Gold and AWS partner certifications and documented vertical depth in manufacturing, healthcare, and oil & gas ML. Minimum engagement starts at $50K. Works best with clients in Manufacturing, Healthcare, SaaS, Logistics, Fintech.
Who should choose Ciklum?
Ciklum is the right choice for global enterprises seeking AI features embedded in large-scale digital product programmes with Experience Engineering focus.
4,000-person Experience Engineering firm with 250+ enterprise clients and generative AI delivery integrated into large product programmes. Minimum engagement starts at $100K. Works best with clients in Fintech, Healthcare, E-commerce, SaaS, Logistics.
Decision matrix: ScienceSoft vs Ciklum
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | ScienceSoft |
| You need a large dedicated team for an ongoing programme | ScienceSoft |
| Your budget is at the lower end | ScienceSoft |
| You need specialist depth in a specific vertical | ScienceSoft |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | ScienceSoft |
Use case fit: ScienceSoft vs Ciklum
| Use case | ScienceSoft fit | Ciklum fit | Winner |
|---|---|---|---|
| Demand forecasting and production optimisation ML for manufacturing plants | Strong | Limited | ScienceSoft |
| Clinical decision support ML for healthcare providers and hospital systems | Strong | Limited | ScienceSoft |
| Generative AI features integrated into large enterprise digital products | Limited | Strong | Ciklum |
| ML-powered personalisation for consumer-facing applications at scale | Limited | Strong | Ciklum |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: ScienceSoft vs Ciklum
ScienceSoft (3.9/5) is the stronger overall choice for most Machine Learning Development projects. 35-year IT firm with Microsoft Gold and AWS partner certifications and documented vertical depth in manufacturing, healthcare, and oil & gas ML. It is best for manufacturing, healthcare, and oil & gas companies needing a long-established US-headquartered ML partner with Microsoft and AWS credentials.
Ciklum (3.6/5) is the better choice when global enterprises seeking AI features embedded in large-scale digital product programmes with Experience Engineering focus. If your situation matches those criteria, Ciklum is a competitive option.
Related comparisons
ScienceSoft vs Ciklum FAQ
Is ScienceSoft better than Ciklum?
ScienceSoft (3.9/5) scores higher overall, but "better" depends on your use case. ScienceSoft is better for manufacturing, healthcare, and oil & gas companies needing a long-established US-headquartered ML partner with Microsoft and AWS credentials. Ciklum is better for global enterprises seeking AI features embedded in large-scale digital product programmes with Experience Engineering focus.
How do ScienceSoft and Ciklum differ in pricing?
ScienceSoft uses fixed project, dedicated team, t&m pricing with a minimum engagement of $50K. Ciklum uses dedicated team, t&m pricing with a minimum engagement of $100K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: ScienceSoft or Ciklum?
ScienceSoft 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 ScienceSoft and Ciklum?
ScienceSoft's primary differentiator is: 35-year it firm with microsoft gold and aws partner certifications and documented vertical depth in manufacturing, healthcare, and oil & gas ml. Ciklum's primary differentiator is: 4,000-person experience engineering firm with 250+ enterprise clients and generative ai delivery integrated into large product programmes. They also differ in team size (700–1,000 vs 4,000+), minimum engagement ($50K vs $100K), and primary industries served (Manufacturing, Healthcare vs Fintech, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.