Sigmoid vs Addepto: full comparison for 2026
Quick verdict
Sigmoid (4.2/5) edges ahead of Addepto (3.9/5) overall. Sigmoid is the better choice for CPG and retail data teams that need ML and data engineers billed monthly. Addepto is the stronger option for industrial and automotive companies adding AI and data engineers to an internal team. The right choice depends on your project size, budget, and required tech stack.
Sigmoid vs Addepto: head-to-head summary
| Criterion | Sigmoid | Addepto |
|---|---|---|
| Founded | 2013 | 2017 |
| HQ | San Francisco, California, USA | Warsaw, Poland |
| Team size | 500–600 (directory estimates) | 50–99 (directory estimate) |
| Rating | 4.2 / 5 | 3.9 / 5 |
| Primary differentiator | Requirement-by-requirement split between project work and monthly staff augmentation | AI-heavy team with manufacturing domain experience, now backed by a larger group |
| Pricing model | Monthly billing for augmented staff; fixed bids of three to five months for projects; rates on request | Collaborative team model or managed delivery; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Spark, Databricks | Python, Databricks, Spark |
| Industries served | CPG, Retail, Banking & financial services, Manufacturing | Manufacturing, Automotive, Retail, Aviation |
Sigmoid vs Addepto: overview
Sigmoid
Sigmoid is a San Francisco company founded in 2013 that built its business on data engineering for Fortune 500 consumer brands and later moved deeper into machine learning and generative AI. Its own sales deck describes a hybrid model: each client requirement is classified as either a project or a staff-augmentation need, and augmented staff are billed monthly with a dedicated project manager and engineering manager on top. The company reports more than 200 ML models put into production and over 5,000 data workflows built (per company materials; independently unverifiable). Delivery runs from more than 12 centers across the U.S., Europe, Latin America and India.
Addepto
Addepto has worked on AI and data in Warsaw since 2017, with a strong client base in industrial and automotive companies. KMS Technology, an Atlanta engineering firm backed by Sunstone Partners, acquired it in December 2025. Its collaborative cooperation model puts Addepto engineers alongside the client's own team, and the company has said publicly it is not a body-leasing firm. After the deal, its CEO said 97% of the team are AI engineers.
Services and capabilities: Sigmoid vs Addepto
| Capability | Sigmoid | Addepto |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✓ |
| AI agent development | ✗ | ✗ |
| MLOps & deployment | ✓ | ✗ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| Fractional / part-time experts | ✗ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Forward-deployed engineers | ✗ | ✗ |
| Access to a wider AI talent network | ✗ | ✗ |
Tech stack comparison: Sigmoid vs Addepto
| Framework / platform | Sigmoid | Addepto |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Databricks | ✓ | ✓ |
| MLflow | ✓ | N/A |
Pricing comparison: Sigmoid vs Addepto
| Criterion | Sigmoid | Addepto |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineers, Embedded team, Project delivery | Embedded team, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Sigmoid vs Addepto
| Dimension | Sigmoid | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | CPG, Retail, Banking & financial services | Manufacturing, Automotive, Retail |
| Best use cases | Adding ML engineers to a CPG demand-forecasting team, Staffing a Databricks migration while keeping models in production | Adding Databricks engineers to a manufacturer's data team, Building a GenAI assistant for automotive service documents |
| Typical project type | Dedicated engineers | Embedded team |
Sigmoid vs Addepto: pros and cons
| Sigmoid | |
|---|---|
| + | Augmented engineers come with management support included in the monthly fee |
| + | Delivery centers in Lima and Amsterdam as well as India give time-zone choice |
| + | Long track record with Fortune 500 consumer brands |
| + | Reported revenue of about $100M in 2024 suggests a stable supplier |
| - | Its roots are in data engineering, so pure research ML roles are less of a focus |
| - | Headcount estimates range from about 500 to more than 1,000 |
| - | No published rates |
| Addepto | |
|---|---|
| + | Nearly the whole team is AI engineers, according to its CEO |
| + | Industrial and automotive client experience |
| + | KMS ownership adds broader engineering capacity behind it |
| - | Acquired by KMS Technology in December 2025; ownership changes can bring new contract terms |
| - | Prefers joint delivery to straight staff placement |
| - | Team size estimates range from 8 to 99 |
Who should choose Sigmoid?
A typical fit: adding ML engineers to a CPG demand-forecasting team.
Requirement-by-requirement split between project work and monthly staff augmentation. Minimum engagement is not publicly disclosed. Works best with clients in CPG, Retail, Banking & financial services, Manufacturing.
Who should choose Addepto?
A typical fit: adding Databricks engineers to a manufacturer's data team.
AI-heavy team with manufacturing domain experience, now backed by a larger group. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Automotive, Retail, Aviation.
Decision matrix: Sigmoid vs Addepto
| Your situation | Recommended choice |
|---|---|
| You need one AI specialist part-time | Neither advertises part-time experts; ask about reduced hours |
| You need several engineers working as one team | Both; Sigmoid rates higher overall |
| You want to test an engineer before committing | Neither publishes a trial; negotiate a short first term |
| Your budget is at the lower end | Compare: Sigmoid (Not published) vs Addepto (Not published) |
| You need engineers deployed inside your organization | Both; Sigmoid rates higher overall |
| You need specialist depth in a specific vertical | Sigmoid |
Use case fit: Sigmoid vs Addepto
| Use case | Sigmoid fit | Addepto fit | Winner |
|---|---|---|---|
| Adding ML engineers to a CPG demand-forecasting team | Strong | Strong | Both equally |
| Staffing a Databricks migration while keeping models in production | Strong | Limited | Sigmoid |
| Adding Databricks engineers to a manufacturer's data team | Strong | Strong | Both equally |
| Building a GenAI assistant for automotive service documents | Limited | Strong | Addepto |
Verdict: Sigmoid vs Addepto
Sigmoid (4.2/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Requirement-by-requirement split between project work and monthly staff augmentation.
Addepto (3.9/5) is worth a look if you need building a GenAI assistant for automotive service documents. If your situation matches that, Addepto is a competitive option.
Related comparisons
Sigmoid vs Addepto FAQ
Is Sigmoid better than Addepto?
Sigmoid (4.2/5) scores higher overall, but "better" depends on your use case. Sigmoid's strongest advantage: augmented engineers come with management support included in the monthly fee. Addepto's strongest advantage: nearly the whole team is AI engineers, according to its CEO.
How do Sigmoid and Addepto differ in pricing?
Sigmoid uses monthly billing for augmented staff; fixed bids of three to five months for projects; rates on request pricing. Addepto uses collaborative team model or managed delivery; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Sigmoid or Addepto?
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 Sigmoid and Addepto?
Sigmoid's primary differentiator is: requirement-by-requirement split between project work and monthly staff augmentation. Addepto's primary differentiator is: AI-heavy team with manufacturing domain experience, now backed by a larger group. They also differ in team size (500–600 (directory estimates) vs 50–99 (directory estimate)), minimum engagement (Not published vs Not published), and primary industries served (CPG, Retail vs Manufacturing, Automotive).
Verify all details directly with each company before making a decision.