MakinaRocks

Name: MakinaRocks
Brand: MakinaRocks

Transforming Industries with Specialized AI

Est 2017|Valuation $84.3M|Raised $39M|7 Ref Customers

LinkedIn 3K|Web traffic 19K|FTEs 92|HQ Seoul, Seoul, KR

MarketsData Science & Machine Learning Platforms Machine Learning Ops (MLOps)

01Competitive Landscape

Narrative analysis

- Where MakinaRocks may be relevant - The company emphasizes closed network and high-security deployments in vendor materials (public sector and defense-oriented use cases), including statements about operability in restricted environments. - There is a consistent focus in product materials on industrial data types (PLC/time-series, anomaly detection, optimization/control). - Where competitors often have advantages - Dataiku: broader enterprise adoption and a general-purpose analytics-to-ML platform with more peer feedback and documentation. - Databricks: breadth across data engineering and AI workloads, and strong channel/marketplace presence. - DataRobot: recognized AutoML and governance/MLOps workflows, often used by business-facing data science teams. - Market adoption signals - Public communications cite deployed industrial AI models and patents; independent adoption metrics are limited from the public materials reviewed. - Some third-party news coverage lists named organizations as users, but details are not fully verifiable from primary customer sources in the materials reviewed.

	MakinaRocks (Runway)	Dataiku	Databricks
Starting Price	Not publicly listed (quote-based; Lite/Enterprise tiers)	Free edition exists (limited); paid is contact sales	Pay-as-you-go model; pricing depends on usage/SKUs
Most Applicable For	Industrial enterprises needing on-prem/hybrid and security-constrained MLOps and industrial AI workflows	Organizations wanting a broad enterprise AI platform spanning analytics/ML with collaboration and governance features	Teams standardized on lakehouse/data engineering workflows and scalable ML/serving in cloud ecosystems
Key Strength	Focus on industrial and closed-network deployments; emphasis on industrial AI use cases; ISO/IEC 27001 reported in vendor materials	Mature enterprise feature set, large customer base, many reviews and peer benchmarks	Integration with cloud ecosystems, scalable compute for ML/serving
Key Weakness	Limited independent reviews and community footprint; pricing transparency limited	Can require significant implementation effort for large deployments	Can become expensive at scale; governance and operationalization require engineering investment

01Competitive Landscape

Narrative analysis

Where MakinaRocks may be relevant
- The company emphasizes closed network and high-security deployments in vendor materials (public sector and defense-oriented use cases), including statements about operability in restricted environments.
- There is a consistent focus in product materials on industrial data types (PLC/time-series, anomaly detection, optimization/control).
Where competitors often have advantages
- Dataiku: broader enterprise adoption and a general-purpose analytics-to-ML platform with more peer feedback and documentation.
- Databricks: breadth across data engineering and AI workloads, and strong channel/marketplace presence.
- DataRobot: recognized AutoML and governance/MLOps workflows, often used by business-facing data science teams.
Market adoption signals
- Public communications cite deployed industrial AI models and patents; independent adoption metrics are limited from the public materials reviewed.
- Some third-party news coverage lists named organizations as users, but details are not fully verifiable from primary customer sources in the materials reviewed.

	MakinaRocks (Runway)	Dataiku	Databricks
Starting Price	Not publicly listed (quote-based; Lite/Enterprise tiers)	Free edition exists (limited); paid is contact sales	Pay-as-you-go model; pricing depends on usage/SKUs
Most Applicable For	Industrial enterprises needing on-prem/hybrid and security-constrained MLOps and industrial AI workflows	Organizations wanting a broad enterprise AI platform spanning analytics/ML with collaboration and governance features	Teams standardized on lakehouse/data engineering workflows and scalable ML/serving in cloud ecosystems
Key Strength	Focus on industrial and closed-network deployments; emphasis on industrial AI use cases; ISO/IEC 27001 reported in vendor materials	Mature enterprise feature set, large customer base, many reviews and peer benchmarks	Integration with cloud ecosystems, scalable compute for ML/serving
Key Weakness	Limited independent reviews and community footprint; pricing transparency limited	Can require significant implementation effort for large deployments	Can become expensive at scale; governance and operationalization require engineering investment