Most machine learning models stall between a working notebook and a production service. MLOps and model deployment infrastructure firms close that gap: they build reproducible training pipelines, feature stores, model registries, CI/CD for models, scalable serving endpoints and monitoring that catches drift before customers do. Teams hire them when data scientists spend more time deploying than modeling, when cloud GPU bills climb with no visibility, or when an auditor asks which model version made a decision.
Compare these firms on the platforms they have built on, such as SageMaker, Vertex AI, Azure ML, Kubeflow or MLflow, and on whether they hand over infrastructure your team can run.
We evaluate companies on verified profile data: services, pricing, locations, and portfolio. Client reviews are being collected. We may earn a fee for some placements. How we rank and verify
Simplify provisioning with infrastructure automation services for repeatable workflows and efficient management. Aziro helps reduce manual tasks, improve consistency, and streamline infrastructure operations.
2-9
Employees
Chennai, Tamil Nadu, In
No services available
“No AI summary available for this company yet.”
A collaborative cloud environment for the entire data science team, compatible with Python and R. Work together using robust tools, job management, deployments, and additional features.
2-9
Employees
New York, New York, Us
“Saturn Cloud provides a collaborative cloud platform enabling entire data science teams to work efficiently with Python and R, offering tools for job management, deployment, and more.”
Syndicai is a platform for hosting, deploying, and integrating AI models. Simply upload any AI model to the platform to receive an API. We'll handle scalability, security, versioning, and monitoring—no configuration is required. We also provide several additional benefits: - Faster deployment of AI
2-9
Employees
Warszawa, Masovian, Pl
“Syndicai enables seamless AI model deployment with scalable, secure hosting, monitoring, and integration, offering comprehensive control, compatibility, and 24/7 support for enterprise needs.”
Winder.AI is a specialized data science agency focused on helping clients optimize their data usage. We identify inefficient areas of your business and processes that require excessive effort. By leveraging data science, along with machine learning and reinforcement learning techniques, we aim to el
$25,000+
Min Project Size
$150+/hr
Avg Hourly Rate
2-9
Employees
Harrogate, England, Gb
“Winder.AI is a data science agency specializing in automation and AI, leveraging machine learning and reinforcement learning to optimize business processes for clients including tech giants and industry leaders.”
Enabling organizations to outpace their opponents through expert human insight, AI accuracy, and ongoing penetration testing.
$10,000+
Min Project Size
$50-99/hr
Avg Hourly Rate
50-249
Employees
Chicago, Illinois, Us
“TrekShield empowers organizations to surpass competitors by combining expert human insight, AI precision, and continuous penetration testing for enhanced cybersecurity.”
ServerBee is a team of dedicated DevOps professionals with a primary office located in Spain. We assist businesses in managing critical software applications and infrastructure on platforms such as Amazon Web Services, Google Cloud Platform, Azure, DigitalOcean, and even BareMetal. Generally, we pro
$1,000+
Min Project Size
$50-99/hr
Avg Hourly Rate
10-49
Employees
Salou, Catalonia, Es
“ServerBee is a Spain-based DevOps team specializing in cloud infrastructure management, migration to microservices, continuous integration, monitoring, and advancing MLOps using modern DevOps tools and practices.”
Founded in 2017, Omniboom aims to address intricate data challenges by delivering intelligent, business-oriented solutions that can swiftly and securely link information across any application or device, regardless of its location. Our premier product, the Omniplexus Platform, allows you to effortle
2-9
Employees
Norwood, Massachusetts, Us
“Omniboom, founded in 2017, delivers intelligent, secure data integration solutions with its Omniplexus Platform, specializing in IoT, AI enablement, application modernization, and event streaming.”
We create, develop, and deliver AI solutions that generate their own returns—your partner for ROI-focused AI implementations. Dreamloop Studio assists businesses in reducing expenses, accelerating processes, and discovering new revenue opportunities through practical, deployed AI. Our services inclu
$1,000+
Min Project Size
$50-99/hr
Avg Hourly Rate
50-249
Employees
Seefeld, De
“Dreamloop Studio develops ROI-focused AI solutions that enhance efficiency, automate workflows, and support customer engagement, emphasizing practical deployment, compliance, and measurable business impact.”
Sparkbit is a software development company that specializes in machine learning, data processing, and backend systems. Our team comprises highly qualified software engineers with PhDs and master's degrees, enabling us to develop software solutions that leverage scientific expertise to address real-w
$5,000+
Min Project Size
$100-149/hr
Avg Hourly Rate
10-49
Employees
Warszawa, Masovian, Pl
“Sparkbit is a software development firm specializing in machine learning, backend systems, and R&D prototypes, leveraging advanced scientific expertise to deliver scalable, secure, and innovative solutions.”
We provide a comprehensive suite of data processing solutions, covering everything from initial concepts to final delivery, across Big Data, Data Architecture, Data Engineering, Data Science, and ML Ops.
$5,000+
Min Project Size
$50-99/hr
Avg Hourly Rate
10-49
Employees
“Digital Back Office delivers end-to-end data processing solutions encompassing Big Data, Data Architecture, Engineering, Science, and ML Ops, supporting clients from concept through deployment.”
What the work covers, how to judge a partner and what drives the price.
Work usually begins with an assessment of how models are trained, versioned and released today. The firm then designs a target platform: data and feature pipelines, experiment tracking, a model registry with approval steps, automated retraining, containerized serving on Kubernetes or managed endpoints, and monitoring for latency, data drift and prediction quality. Infrastructure is written as code with Terraform or similar so it can be rebuilt.
Engagements take three forms. A platform build delivers the stack and runbooks over a defined scope. A productionization project takes one or two models from prototype to live service as a template for the rest. Ongoing support or staff augmentation provides MLOps engineers who run the platform, handle incidents and tune cost.
Generative AI adds LLMOps work: hosting open-weight models, GPU autoscaling, prompt and evaluation pipelines, and guardrails logged for review.
Ask for a reference architecture from a past client and an explanation of why each component was chosen. A credible firm can say when a managed service beats self-hosted Kubeflow and what each choice costs to operate. Check cloud credentials such as AWS, Google Cloud or Azure machine learning specialties, and for regulated work, experience with SOC 2 or HIPAA controls around training data.
Ask how they measure success: deployment frequency, time from trained model to production, inference cost per request, and mean time to detect drift. Ask what your team will own on day one after handover, and how documentation and training are delivered.
Red flags include a firm that pushes its own proprietary platform with heavy licensing, no plan for monitoring after launch, and proposals that ignore data governance and access control. Be wary of teams that are strong data scientists but have never run production on-call.
MLOps firms bill time and materials for assessments and support, fixed price for scoped platform builds, and monthly fees for managed operations or embedded engineers. Cloud consumption is a separate line you pay directly, and a good partner will forecast it.
Price rises with the number of models and teams the platform serves, real-time versus batch inference, GPU requirements, multi-cloud or on-premises deployment, compliance needs and the state of your data pipelines. Messy upstream data often consumes more effort than the serving layer itself.
Most firms in this listing set a minimum project between $1,000 and $25,000, with the floors spread fairly evenly across that band. A group of specialists starts at $100,000, typically for enterprise ML platforms covering many teams.
Short answers to what buyers ask before hiring a MLOps & Model Deployment Infrastructure company.
Question not covered? Ask our team
MLOps consulting is billed as time and materials, fixed-price platform builds, or monthly managed support, with cloud costs paid separately. Most firms on Edverise set a minimum project of $1,000 to $25,000. Model count, GPU use, real-time serving and compliance requirements raise the budget.
Setup time depends on the number of models, the condition of your data pipelines and how much of the stack already exists. The phases are assessment, architecture design, infrastructure build, migration of a first model, then rollout to other teams. Data quality problems and security reviews are the usual delays.
Machine learning model development produces the model itself, through data preparation, feature engineering, training and evaluation. MLOps builds the infrastructure that deploys, monitors and retrains that model in production. Many firms offer both, but the skills differ: data science versus platform and reliability engineering.
Financial services, IT, healthcare, ecommerce, retail and business services are the main clients of MLOps firms listed here. Banks and insurers need audit trails for model decisions, healthcare needs controlled access to patient data, and retailers run many forecasting and recommendation models that need frequent retraining.
The largest groups of MLOps and model deployment firms on Edverise are in New York, Ahmedabad, Hanoi, London, Kyiv and Pune. Much of this work is done remotely, so overlapping hours for incident response matter more than shared city.
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