MobiDev vs Sigmoid: full comparison for 2026
Last updated: July 2026
Quick verdict
MobiDev (4.3/5) edges ahead of Sigmoid (4.2/5) overall. MobiDev is the better choice for small and mid-sized companies wanting a dedicated ML/data-science consulting arm within a broader software development partner.. Sigmoid is the stronger option for enterprises whose primary bottleneck is data infrastructure and pipeline reliability ahead of, or alongside, ML model development.. The right choice depends on your project size, budget, and required tech stack.
MobiDev vs Sigmoid: head-to-head summary
| Criterion | MobiDev | Sigmoid |
|---|---|---|
| Founded | 2009 | 2013 |
| HQ | Norcross, USA (delivery centers: Lodz, Poland and Chernivtsi, Ukraine) | San Francisco, USA |
| Team size | 201–500 | 501–1,000 |
| Rating | 4.3 / 5 | 4.2 / 5 |
| Best for | Small and mid-sized companies wanting a dedicated ML/data-science consulting arm within a broader software development partner. | Enterprises whose primary bottleneck is data infrastructure and pipeline reliability ahead of, or alongside, ML model development. |
| Pricing model | Time & Material, Fixed project | Not published; project and retainer engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Computer vision frameworks, Cloud ML platforms | AWS, Microsoft Azure, Google Cloud |
| Industries served | Healthcare, Retail, Manufacturing, Media | Retail, CPG, Media, Financial services |
MobiDev vs Sigmoid: overview
MobiDev
MobiDev is a software development and consulting company founded in 2009, with business units in Norcross, Georgia (US) and Sheffield (UK), and R&D delivery centers in Lodz, Poland and Chernivtsi, Ukraine staffed by more than 400 engineers. Its consulting services span data science, machine learning, augmented reality, IoT, and DevOps, aimed at small and medium-sized companies rather than large enterprises. The company reports a 100 percent project success rate on Upwork and was named the #1 machine learning development company by Clutch in 2021.
Sigmoid
Sigmoid is a data engineering services and AI consulting company founded in 2013 and headquartered in San Francisco, with additional offices in New York, Dallas, Lima, Amsterdam, and Bengaluru. The company reports more than 950 cloud-certified engineers across AWS, Azure, and GCP, reflecting a data-engineering-first approach to enabling downstream machine learning work. Sigmoid positions itself around helping enterprises build the data infrastructure layer that ML models depend on, rather than leading with model development alone.
Services and capabilities: MobiDev vs Sigmoid
| Capability | MobiDev | Sigmoid |
|---|---|---|
| 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: MobiDev vs Sigmoid
| Framework / platform | MobiDev | Sigmoid |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Amazon Bedrock | N/A | N/A |
| Google Cloud | N/A | ✓ |
| Microsoft Azure | N/A | ✓ |
| Kubernetes | ✓ | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: MobiDev vs Sigmoid
| Criterion | MobiDev | Sigmoid |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Time & Material, Fixed project, Dedicated team | Project-based, Managed data engineering retainer |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: MobiDev vs Sigmoid
| Dimension | MobiDev | Sigmoid |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Healthcare, Retail, Manufacturing | Retail, CPG, Media |
| Best use cases | Building a custom ML model for a small or medium-sized business without an internal data science team, Combining computer vision or ML work with broader mobile/IoT product development | Building the data pipeline and warehouse layer needed to support ML model training at scale, Modernizing legacy ETL infrastructure as a precursor to an ML initiative |
| Typical project type | Time & Material | Project-based |
MobiDev vs Sigmoid: pros and cons
| MobiDev | |
|---|---|
| + | Historical Clutch #1 ranking in machine learning development (2021). |
| + | 16 Clutch reviews with consistently positive delivery feedback. |
| + | Explicit focus on small/medium-sized clients, a niche underserved by larger enterprise-first firms. |
| + | Multi-country delivery footprint (Poland, Ukraine) with 400+ engineers provides meaningful bench depth. |
| - | Team-size figures vary by source (roughly 200–500), indicating some reporting inconsistency. |
| - | SME focus may mean less experience with very large, complex enterprise-scale ML platforms. |
| - | Machine learning is one of several practice areas (alongside AR, IoT) rather than the sole focus. |
| - | Minimum engagement size is not published, requiring a scoping conversation. |
| Sigmoid | |
|---|---|
| + | Very large pool of cloud-certified engineers (950+) across all three major hyperscalers. |
| + | Data-engineering-first approach reduces the risk of building models on unreliable data pipelines. |
| + | Multi-continent office footprint (US, Europe, South America, India) supports global delivery. |
| + | Twelve-plus years of continuous operation as a bootstrapped, profitable company (per reporting on ~$100M ARR). |
| - | Employee headcount estimates vary meaningfully by source (roughly 600–950), creating some uncertainty. |
| - | Model development itself is positioned as downstream of data engineering, which may not suit buyers wanting a model-first specialist. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Pricing and minimum engagement are not published. |
Who should choose MobiDev?
MobiDev is the right choice for small and mid-sized companies wanting a dedicated ML/data-science consulting arm within a broader software development partner..
Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Retail, Manufacturing, Media.
Who should choose Sigmoid?
Sigmoid is the right choice for enterprises whose primary bottleneck is data infrastructure and pipeline reliability ahead of, or alongside, ML model development..
Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse.. Minimum engagement starts at Not published. Works best with clients in Retail, CPG, Media, Financial services.
Decision matrix: MobiDev vs Sigmoid
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | MobiDev |
| You need a large dedicated team for an ongoing programme | MobiDev |
| Your budget is at the lower end | Compare: MobiDev (Not published) vs Sigmoid (Not published) |
| You need specialist depth in a specific vertical | MobiDev |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | MobiDev |
Use case fit: MobiDev vs Sigmoid
| Use case | MobiDev fit | Sigmoid fit | Winner |
|---|---|---|---|
| Building a custom ML model for a small or medium-sized business without an internal data science team | Strong | Strong | Both equally |
| Combining computer vision or ML work with broader mobile/IoT product development | Strong | Limited | MobiDev |
| Building the data pipeline and warehouse layer needed to support ML model training at scale | Strong | Strong | Both equally |
| Modernizing legacy ETL infrastructure as a precursor to an ML initiative | Limited | Strong | Sigmoid |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: MobiDev vs Sigmoid
MobiDev (4.3/5) is the stronger overall choice for most ML Model Development projects. Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model.. It is best for small and mid-sized companies wanting a dedicated ML/data-science consulting arm within a broader software development partner..
Sigmoid (4.2/5) is the better choice when enterprises whose primary bottleneck is data infrastructure and pipeline reliability ahead of, or alongside, ML model development.. If your situation matches those criteria, Sigmoid is a competitive option.
Related comparisons
MobiDev vs Sigmoid FAQ
Is MobiDev better than Sigmoid?
MobiDev (4.3/5) scores higher overall, but "better" depends on your use case. MobiDev is better for small and mid-sized companies wanting a dedicated ML/data-science consulting arm within a broader software development partner.. Sigmoid is better for enterprises whose primary bottleneck is data infrastructure and pipeline reliability ahead of, or alongside, ML model development..
How do MobiDev and Sigmoid differ in pricing?
MobiDev uses time & material, fixed project pricing with a minimum engagement of Not published. Sigmoid uses not published; project and retainer engagements 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: MobiDev or Sigmoid?
Sigmoid 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 MobiDev and Sigmoid?
MobiDev's primary differentiator is: historical clutch #1 ranking for machine learning development (2021) combined with a specifically sme-oriented service model.. Sigmoid's primary differentiator is: data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ml rather than the reverse.. They also differ in team size (201–500 vs 501–1,000), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Retail vs Retail, CPG).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.