Many companies today are investing heavily in AI and machine learning. However, models often work perfectly in testing but fail in production. Data changes, performance drops, and suddenly your AI project becomes more expensive than valuable.
This challenge worsens when teams lack the necessary expertise, automation, and monitoring systems to maintain model reliability. Without the right MLOps support, deployments slow down, and ROI takes a hit.
However, to make your selection easier, we’ve listed the top 10 MLOPs consulting companies in the USA known for strong cloud capabilities, security compliance, and enterprise-grade support.
In this blog, we’ll explore:
- What is MLOps?
- Why Businesses Need MLOps Consulting in 2026?
- Key Criteria for Choosing the Right MLOps Consulting Partner and more.
What is MLOps?
MLOps (Machine Learning Operations) is a set of practices that helps teams build, deploy, and manage machine learning models efficiently in real-world environments.
According to Fortune Business Insight, it is expected to grow to about USD 19.55 billion by 2032. Think of it like DevOps, but specifically for ML.
It combines three things:
1. Data – preparing, cleaning, and managing data pipelines.
2. Models – training, testing, improving, and versioning ML models.
3. Operations – deploying models, monitoring performance, and scaling systems.
Why it matters:
Without MLOps, models often fail after deployment due to data drift, performance issues, or a lack of automation. MLOps ensures models stay accurate, secure, and reliable over time.
Why Businesses Need MLOps Consulting in 2026?
By 2026, businesses will be using artificial intelligence everywhere, from customer service to supply chains. But scaling AI isn’t easy. That’s where MLOps consulting can simplify and secure everything.
1. Growth of AI Adoption Across Industries
AI models are expanding fast across sectors like finance, retail, and healthcare. MLOps consulting helps companies deploy and manage these models efficiently without slowing business operations.
2. Need for Automation, Governance, and Secure Model Lifecycle Management
As AI grows, manual model updates and risky operations won’t work. MLOps ensures automated pipelines, strict compliance, and strong security for every stage of the ML lifecycle.
3. Support for Cloud Migration and Enterprise Integration
Businesses want flexibility and scalability. MLOps experts help organisations move to the cloud smoothly and integrate AI systems with existing tools, ensuring everything works seamlessly.
Top 10 MLOps Consulting Companies in the USA
Here’s a detailed comparison of the top MLOps consulting companies helping U.S. enterprises scale AI securely and efficiently in 2026.
| Companies | Industries served | Key features | Support & Scalability |
| SoluLab | Healthcare, Finance, Retail | End-to-end MLOps services, CI/CD pipelines, model monitoring | Enterprise-grade support with scalable cloud deployments |
| MLopsCrew | E-commerce, SaaS, Automotive | Model lifecycle automation, governance, security | Designed for high-availability and performance in production |
| Addepto | Finance, Manufacturing, Telecom | Data engineering + MLOps combo, MLflow and Kubernetes expertise | Flexible support with global scalability for AI products |
| Dysnix | FinTech, Web3, Cloud Platforms | High-performance MLOps with GPU optimisation and DevOps | Strong DevSecOps foundation for scaling distributed workloads |
| Citrusbug | Healthcare, Retail, Startups | Custom pipelines, real-time model updates, analytics dashboards | Adaptive solutions for fast-growing businesses |
| Inoxoft | Logistics, Energy, Pharmaceuticals | Automated model deployment, performance tuning | Reliable long-term support to maintain production stability |
| Softweb Solutions | Manufacturing, Smart Cities, BFSI | IoT + MLOps integration, strong Azure stack | Enterprise scalability with 24/7 managed operations |
| instictools | Retail, Media, Tech Startups | Kubeflow-based pipelines, MLOps consulting for rapid releases | Built to handle large data and multiple deployments |
| DataRobot | Banking, Insurance, Telecom | Automated machine learning with built-in MLOps platform | Robust scalability with enterprise AI governance |
| Arriktto | Tech Enterprises, Cloud-Native Businesses | Kubeflow-powered MLOps, versioning, reproducibility | Strong Kubernetes capabilities for global scaling |
Now that you know all the top 10 MLOps consulting companies, let’s know each one in detail:
1. SoluLab
SoluLab, an MLOps consulting company, helps enterprises operationalise AI faster with strong MLOps services, including model deployment, monitoring, and automated pipelines. Their team is skilled in tools like MLflow, Docker, Kubernetes, and Kubeflow for scalable model management.
They support cloud platforms such as AWS, Azure, and Google Cloud to ensure scalability. SoluLab integrates ML solutions with existing enterprise systems, including CRMs and data warehouses. Backed by certified ML engineers and DevOps experts, they deliver reliable solutions tailored for enterprise needs.
$25 - $49/ hr
200 – 249
2014
2. MLOpsCrew
MLOpsCrew specialises in end-to-end MLOps services from automated pipelines to model monitoring and governance. Their team is skilled with tools such as Kubeflow, Flyte, MLflow, and Feast.
They support all major cloud platforms, including AWS, Google Cloud, and Azure, for scalable ML deployment. A certified team of ML engineers and DevOps experts helps organisations move from real-time collaboration at ~70% lower cost.
$50-$75/ hr
50+
2025
3. Addepto
Addepto provides MLops consulting services and delivers end-to-end MLOps consulting, helping enterprises automate model workflows, monitoring, and governance. Their team is skilled in MLflow, Weights & Biases, Kubeflow, Apache Airflow, Metaflow and TensorFlow Serving. They support AWS, Google Cloud, and Azure environments for flexible migration and hosting.
Addepto excels at integration with enterprise data warehouses, CRMs, and ERP systems. With certified ML engineers and DevOps specialists, they ensure business-ready AI operations with consistent performance.
$75-$99/ hr
200+
2018
4. Dysnix
Dysnix, an MLOps consulting company, specialises in end-to-end MLOps services, including model deployment, automation, and real-time monitoring for enterprise workloads. They have strong expertise in tools like Kubernetes, Terraform, MLflow, and cloud-native architectures.
The team works across AWS, Google Cloud, and Azure to ensure scalable and secure model operations. Dysnix excels at integrating ML pipelines with existing data platforms, enterprise apps, and microservices environments.
$50 - $60/ hr
50-100
2016
5. Citrusbug
Citrusbug provides MLops consulting services, specialising in end-to-end MLOps services including model deployment, monitoring, and CI/CD automation. Their team is skilled with tools like MLflow, Kubeflow, and Docker to ensure efficient workflows.
They support AWS, Azure, and Google Cloud environments with scalable infrastructure setups. Citrusbug excels at integrating AI solutions with CRMs, ERPs, and custom data pipelines for smooth operations.
$50-$70/ hr
150-200
2013
6. Inoxoft
Inoxoft, an MLOps consulting company, offers strong MLOps services focused on model deployment, monitoring, and automation to help enterprises scale AI. Their technology expertise spans MLflow, Docker, and Kubernetes for reliable workflows.
They support major cloud platforms like AWS and Azure to enable flexible, secure environments. Inoxoft ensures integration with existing enterprise systems and data pipelines. Backed by a skilled team of ML engineers and DevOps experts.
$50-$99/ hr
150-200
2014
7. SoftWeb Solutions
SoftWeb Solutions offers MLOps consulting services, including model deployment, monitoring, and CI/CD automation for faster AI rollout. They specialise in leading technologies like MLflow, Kubernetes, TensorFlow, and Databricks.
The team supports multi-cloud environments across AWS, Azure, and Databricks, Azure Data Factory. They ensure smooth integration of ML models with enterprise systems like CRMs, ERPs, and data warehouses. With certified data scientists and MLOps engineers, they help enterprises scale AI securely and efficiently.
$80-$90/ hr
400+
2014
8. Instinctools
Instinctools delivers end-to-end MLOps services, including model deployment, monitoring, and automation of the ML lifecycle. They bring strong expertise in tools like MLflow, Kubernetes, and Databricks for scalable AI operations.
The company supports major cloud platforms, including AWS, Google Cloud, and Azure. They ensure integration with enterprise systems such as CRMs, data warehouses, and APIs. Their certified team of ML engineers and DevOps specialists helps businesses accelerate AI adoption securely and efficiently.
$150-$200/ hr
400+
2000
9. DataRobot
DataRobot delivers end-to-end MLOps services, including automated model deployment, monitoring, and governance that help enterprises scale AI. The company excels in leading tools like MLflow, Kubeflow, and its own AI Platform to streamline the ML lifecycle.
It supports major cloud environments such as AWS, Google Cloud, and Microsoft Azure. DataRobot enables integration with enterprise data systems, BI tools, and APIs for workflows.
$100-$150/ hr
500+
2012
10. Arrikto
Arrikto specialises in MLOps solutions that improve model deployment, monitoring, and versioning across enterprise environments. Their strong expertise in tools like Kubeflow and MLflow enables scalable and automated ML pipelines.
They support leading cloud platforms, including AWS, Google Cloud, and Azure, ensuring flexibility for modern businesses. Arrikto’s integration capabilities allow connection with existing data systems and DevOps workflows.
$50-$100/ hr
50+
2014
Key Criteria for Choosing the Right MLOps Consulting Partner
Choosing the right MLOps consulting partner can make or break your AI success. Look for expertise, security, and strong support systems that ensure your models keep performing in real-world environments.
1. Industry experience & case studies: Pick partners with proven work in your industry. Their past implementations and results help you trust their ability to solve similar challenges for your business.
2. Full lifecycle support (data → deployment → monitoring): A reliable partner manages everything from data pipelines to deployment and ongoing monitoring. This ensures models stay accurate and aligned with changing business needs.
3. Cloud certifications (AWS, Azure, GCP): Certified teams bring strong technical skills and compliance knowledge. They can design cost-efficient, secure solutions across major cloud platforms used in enterprise environments.
4. Proven frameworks & security compliance: Check whether they follow standard frameworks, CI/CD pipelines, and strong security protocols to protect sensitive business data from risks or breaches.
5. SLA-backed support and scalability options: Ensure they offer performance guarantees and can scale infrastructure as your AI usage grows. This protects service quality during peak demand.
6. CTO/VP-level Decision Criteria (TCO, integration & risk reduction): Leaders must evaluate long-term Total Cost of Ownership, vendor lock-in risks, ability to integrate with existing tech stacks, and alignment with internal security and regulatory mandates.
Conclusion
Finding the right MLOps consulting company can be the difference between a great AI idea and a real, scalable business impact. Whether you need end-to-end automation, cloud-native deployment, or stronger data security, a strong MLOps partner helps you move faster, reduce downtime, and ensure your models deliver consistent value.
Choosing a team with proven frameworks, industry expertise, and reliable support becomes essential. The right partner doesn’t just deploy models; they help your business stay ahead.
SoluLab, a top MLOps consulting company, can help you improve data pipelines, optimise deployments, and continuously monitor your models.
Book a free discovery call to discuss further!
FAQs
1. Why should companies hire an MLOps consultant instead of building in-house teams?
Expert MLOps and AI Consultants bring specialised expertise, faster implementation, lower risk, and proven frameworks that help enterprises scale efficiently.
2. How does an MLOps implementation consultant improve scalability?
They design automated pipelines and cloud-native setups that support large-scale deployments across distributed environments.
3. Who are MLOps engineers and what do they do?
They build automated ML workflows, production-ready pipelines, and robust monitoring systems to keep models performing accurately.
4. What industries benefit the most from MLOps services in the USA?
Sectors like finance, retail, healthcare, manufacturing, and telecom rely heavily on AI and need reliable MLOps governance and automation.
5. How do companies measure success after implementing MLOps?
They track deployment frequency, model accuracy improvements, downtime reduction, and faster iteration cycles.
