Addepto vs Sigmoid: full comparison for 2026
Last updated: July 2026
Quick verdict
Addepto (4.4/5) edges ahead of Sigmoid (4.2/5) overall. Addepto is the better choice for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. 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.
Addepto vs Sigmoid: head-to-head summary
| Criterion | Addepto | Sigmoid |
|---|---|---|
| Founded | 2018 | 2013 |
| HQ | Warsaw, Poland | San Francisco, USA |
| Team size | 51–200 | 501–1,000 |
| Rating | 4.4 / 5 | 4.2 / 5 |
| Best for | Cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build. | Enterprises whose primary bottleneck is data infrastructure and pipeline reliability ahead of, or alongside, ML model development. |
| Pricing model | Project-based | Not published; project and retainer engagements |
| Min. engagement | $10,000 | Not published |
| Primary tech stack | Python, MLOps tooling, Cloud ML platforms (AWS/GCP/Azure) | AWS, Microsoft Azure, Google Cloud |
| Industries served | Finance, Healthcare, Retail | Retail, CPG, Media, Financial services |
Addepto vs Sigmoid: overview
Addepto
Addepto is a Poland-based AI consulting firm founded in 2018 by Artur Haponik and Edwin Lisowski that focuses specifically on machine learning consulting, MLOps consulting, and data/analytics advisory work rather than broader software development. The company has around 52 employees and holds a 4.7 Clutch rating, with Clutch-reported project costs typically in the $10,000–$49,000 range, making it one of the more budget-accessible options among firms in this category. Addepto has been recognized among Forbes' top AI consulting companies and appeared on the Deloitte Technology Fast 500 EMEA list, citing 1,193 percent revenue growth over the qualifying period. In December 2025, Addepto was acquired by KMS Technology, a US-based digital engineering, data, and AI company backed by growth private equity firm Sunstone Partners; Addepto now operates as an integrated division rather than as a fully independent company.
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: Addepto vs Sigmoid
| Capability | Addepto | 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: Addepto vs Sigmoid
| Framework / platform | Addepto | 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 | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: Addepto vs Sigmoid
| Criterion | Addepto | Sigmoid |
|---|---|---|
| Minimum engagement | $10,000 | Not published |
| Engagement models | Fixed project, Advisory/consulting retainer | Project-based, Managed data engineering retainer |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Addepto vs Sigmoid
| Dimension | Addepto | Sigmoid |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Finance, Healthcare, Retail | Retail, CPG, Media |
| Best use cases | Auditing an existing ML pipeline and recommending MLOps improvements, Running a well-scoped, budget-constrained machine learning pilot | 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 | Fixed project | Project-based |
Addepto vs Sigmoid: pros and cons
| Addepto | |
|---|---|
| + | 4.7 Clutch rating with lower typical project cost ($10K–$49K) than most peers in this comparison. |
| + | Named a top 10 AI consulting company by Forbes. |
| + | Deloitte Technology Fast 500 EMEA recognition (#143) signals strong recent revenue growth. |
| + | Focused specifically on ML/MLOps consulting rather than diluting attention across general software development. |
| - | Small team (~52 employees) caps capacity for large or multiple concurrent enterprise engagements. |
| - | Lower typical project size may signal a fit for smaller-scope work rather than large production ML platforms. |
| - | Public case studies with named enterprise clients are limited in available sources. |
| - | Now part of KMS Technology following the December 2025 acquisition, introducing near-term integration and roadmap uncertainty for prospective clients. |
| 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 Addepto?
Addepto is the right choice for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..
Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. Minimum engagement starts at $10,000. Works best with clients in Finance, Healthcare, Retail.
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: Addepto vs Sigmoid
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Addepto |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Compare: Addepto ($10,000) vs Sigmoid (Not published) |
| You need specialist depth in a specific vertical | Sigmoid |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Addepto |
Use case fit: Addepto vs Sigmoid
| Use case | Addepto fit | Sigmoid fit | Winner |
|---|---|---|---|
| Auditing an existing ML pipeline and recommending MLOps improvements | Strong | Limited | Addepto |
| Running a well-scoped, budget-constrained machine learning pilot | Strong | Strong | Both equally |
| Building the data pipeline and warehouse layer needed to support ML model training at scale | Limited | Strong | Sigmoid |
| Modernizing legacy ETL infrastructure as a precursor to an ML initiative | Limited | Strong | Sigmoid |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Strong | Limited | Addepto |
Verdict: Addepto vs Sigmoid
Addepto (4.4/5) is the stronger overall choice for most ML Model Development projects. Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. It is best for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..
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
Addepto vs Sigmoid FAQ
Is Addepto better than Sigmoid?
Addepto (4.4/5) scores higher overall, but "better" depends on your use case. Addepto is better for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. Sigmoid is better for enterprises whose primary bottleneck is data infrastructure and pipeline reliability ahead of, or alongside, ML model development..
How do Addepto and Sigmoid differ in pricing?
Addepto uses project-based pricing with a minimum engagement of $10,000. 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: Addepto 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 Addepto and Sigmoid?
Addepto's primary differentiator is: dedicated mlops-consulting service line and clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. 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 (51–200 vs 501–1,000), minimum engagement ($10,000 vs Not published), and primary industries served (Finance, Healthcare vs Retail, CPG).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.