Top 8 MLOps Consulting Companies in USA [2025]

Machine learning isn’t just for big enterprises anymore — SMBs are using AI to cut costs, boost efficiency, and personalize customer experiences.

The tricky part? Moving from experiments to production. That’s where MLOps comes in: it streamlines workflows, keeps models accurate, and manages costs.

Instead of building costly in-house teams, many SMBs partner with MLOps consulting firms. In this post, we’ll cover 8 leading MLOps Consulting Companies in the USA and share MLOpsCrew’s advice on choosing the right fit for your business.

How We Selected These Companies

To ensure this list is practical for SMBs, we used these criteria:

• Demonstrated MLOps expertise (case studies, products, or frameworks).
• SMB alignment — flexible pricing, smaller project models, or startup-friendly engagement styles.
• Clear technical differentiators (platform focus, cost savings, rapid deployment, or governance).
• Industry credibility through client work or published results.

Top MLOps Consulting Companies in the USA

1. MLOpsCrew — Outcome-First Remote MLOps Squads

MLOpsCrew offers outcome-based consulting tailored for SMBs. MLOpsCrew specialize in helping businesses streamline their machine learning operations—from data ingestion to model deployment and monitoring.

With deep expertise in tools like MLflow, Kubeflow, and cloud-native MLOps frameworks, we bridge the gap between AI experimentation and real-world value.

Their differentiator lies in quick-start packs and fixed-price sprints designed to move models from notebooks to production in weeks. They operate inside your VPC to maintain data security and avoid lock-in by leveraging open-source MLOps stacks.

MLOpsCrew Core Services:

• MLOps strategy & implementation
• End-to-end ML pipeline automation
• CI/CD for ML models Model versioning, monitoring & governance
• Integration with AWS, GCP, Azure, and open-source MLOps tools

Best for SMBs who want: Predictable costs, fast deployments, and a lightweight alternative to hiring a full team.

2. Dysnix — MLOps + Infra Automation Specialists

Dysnix blends DevOps and MLOps, with a focus on predictive autoscaling and cost efficiency. Their product, PredictKube, dynamically adjusts compute resources, cutting cloud costs while maintaining low latency.

Case studies show quantifiable savings and reliability improvements, making them appealing for startups scaling fast.

Best for SMBs who want: Infra + MLOps expertise with a focus on lowering cloud bills and improving performance.

3. Arrikto — Kubeflow and AI-Native Storage Experts

Arrikto is known for its enterprise distribution of Kubeflow and Rok, a storage system optimized for ML workflows. Their focus is on data reproducibility and pipeline automation across hybrid Kubernetes environments.

While more technical than consulting-led, their stack removes bottlenecks for SMBs with large datasets or Kubernetes-native operations.

Best for SMBs who want: Scalable ML platforms where storage, reproducibility, and K8s integration are critical.

4. Mosaic Data Science — Pragmatic, Tool-Agnostic Partner

Mosaic emphasizes ROI-driven consulting through its Mosaic deploy framework and a flexible “rent a data scientist” model. They don’t lock clients into proprietary platforms, instead adapting to existing stacks.

Their industry experience (healthcare, finance, manufacturing) shows adaptability and practical problem-solving.

Best for SMBs who want: Tool-agnostic, ROI-focused engagements with pragmatic consultants.

5. Addepto — Governance-Driven MLOps Consulting

Addepto offers enterprise-grade MLOps with a focus on governance, monitoring, and compliance. Their methodology emphasizes reproducible pipelines, CI/CD for ML, and model monitoring frameworks.

While they cater to enterprises, SMBs in regulated industries benefit from their mature practices.

Best for SMBs who want: Strong governance, compliance-ready ML pipelines, and mature delivery processes.

6. Markovate — Generative AI + MLOps Rapid Prototyping

Markovate bridges AI development and MLOps, specializing in generative AI and quick POCs. Their site highlights sector-specific wins: faster claim processing, construction blueprint classification, and inspection automation.

They emphasize turning prototypes into production-grade systems using integrated MLOps.

Best for SMBs who want: Generative AI experiments with a fast path to production.

7. DataRobot — Enterprise-Grade MLOps Platform

DataRobot is a heavyweight in MLOps, offering an end-to-end platform with model monitoring, challenger models, and governance. Its strength is scale and breadth — but with pricing and integration overhead that may be too heavy for lean SMBs. Still, for mid-sized firms ready to invest in enterprise-level AI, it offers unmatched maturity.

Best for SMBs who want: A comprehensive MLOps platform with governance and lifecycle management.

8. LeewayHertz — Full-Stack AI Product + MLOps Development

LeewayHertz integrates MLOps within its full-stack AI product development services. They automate ML pipelines and ensure reproducibility while also building surrounding applications. Their focus on end-to-end development makes them ideal for SMBs creating AI-driven products rather than just deploying internal models.

Best for SMBs who want: One vendor to build both the product and the MLOps backbone.

Why Businesses Struggle With MLOps Alone

• Talent scarcity: Hiring MLOps engineers is costly and competitive.
• Cloud costs: Poorly tuned infra drives runaway bills.
• Model drift: Without monitoring, models decay quickly in production.
• Integration gaps: ML often breaks when plugged into real apps.

Consultants solve these by providing frameworks, cost-optimized infra, and proven CI/CD workflows.

How to Choose the Right MLOps Consultant — Expert Advice by MLOpsCrew

1. Start with your bottleneck. Need cost savings? Consider Dysnix. Need rapid production? Try MLOpsCrew or Mosaic.
2. Engagement model matters. SMBs benefit from sprint-based or outcome-based pricing over long retainers.
3. Align with your stack. If you’re K8s-heavy, Arrikto fits. If you want enterprise governance, DataRobot is the choice.
4. Prioritize knowledge transfer. Ensure consultants leave you with playbooks and artifacts, not a black box.
5. Balance cost vs ROI. Cheapest isn’t best — focus on outcomes like deployment speed, cost control, or governance.
6. Check industry relevance. Ask for references or case studies in your vertical.

Final Take

Each of the eight firms brings something unique:

• Fast, SMB-friendly wins: MLOpsCrew, Mosaic, Markovate.
• Infra & scaling optimization: Dysnix, Arrikto.
• Governance-heavy projects: DataRobot, Addepto.
• Product + MLOps builds: LeewayHertz.

For small and medium businesses, the sweet spot is often small fixed-scope pilots that scale into production, delivering results without massive upfront cost.

At MLOpsCrew, we specialize in helping operationalize AI with tailored MLOps solutions — blending automation, cost optimization, and long-term scalability.

Ready to move your AI models from experiments to production without the overhead of building an in-house team?

Partner with MLOpsCrew — your MLOps consulting experts. From quick-win pilots to scalable pipelines, we can help your business cut costs and accelerate AI adoption.

Book a 45-minute free consultation