AI applications introduce infrastructure requirements that go beyond conventional app hosting. Production systems may need scalable inference, reliable data pipelines, automated model deployment, observability, GPU or cloud resource management, and controls for security and cost. MLOps and DevOps are increasingly interconnected as AI features move from prototypes into customer-facing software. The right infrastructure approach should support both the application layer and the AI workloads running behind it without making deployment or maintenance unnecessarily complex.
Gilzor handles infrastructure as part of taking web, mobile, and AI-built products into production. Its engineering stack includes AWS, Docker, Kubernetes, GitHub Actions, CircleCI, Ansible, Lambda, NGINX, message queues, databases, and storage technologies. For AI-built applications specifically, the company works on production deployment, architecture, observability, database hardening, performance, reliability, security, and cloud cost control. This combination is particularly relevant to startups and product teams that already have a functional AI-assisted prototype but need stronger engineering underneath it before scaling traffic or launching to customers.
OSKI Solutions combines AI product engineering with a substantial Cloud and DevOps practice. Its cloud services cover AWS, Microsoft Azure, and Google Cloud, along with CI/CD automation, containers, Kubernetes, infrastructure operation, observability, migration, and cost optimization. The company also integrates LLMs, computer vision, and predictive machine learning into production software, making its infrastructure services relevant when AI features have to operate alongside existing application backends. OSKI can take responsibility from architecture and deployment through ongoing operations, which suits teams that do not want separate software development and cloud infrastructure vendors.
ScienceSoft combines AI engineering, MLOps, cloud enablement, DevOps, and managed infrastructure services. Its AI teams include MLOps specialists who work on model deployment automation, versioning, orchestration, monitoring, scaling, and reproducibility across cloud and on-premises environments. The wider engineering practice covers AWS, Azure, GCP, Docker, Kubernetes, data pipelines, observability, application development, cybersecurity, and ongoing infrastructure management. This breadth is useful for organizations where the AI layer must be integrated with an existing enterprise application, data estate, or regulated technology environment rather than deployed as an isolated service.
AI Superior focuses directly on the infrastructure and operational requirements behind production AI systems. Its services include AI cloud deployment, LLM infrastructure setup, model hosting, scalable machine learning infrastructure, monitoring, computational resource management, performance tuning, and managed AI operations. The company supports cloud, hybrid, and on-premises deployment models and works with technologies such as AWS, Azure, Kubernetes, Databricks, Grafana, TensorFlow, and DevOps tooling. This makes it particularly relevant when the central infrastructure problem is operating models reliably rather than building a conventional application stack around them.
A-listware provides cloud and IT infrastructure services alongside software engineering, data, and machine learning capabilities. Its infrastructure practice covers cloud architecture, migration, management, security, DevOps consulting, CI/CD, containerization, monitoring, infrastructure support, and hybrid environments. AWS, Azure, Google Cloud, Docker, Kubernetes, Terraform, Jenkins, Prometheus, Grafana, and Datadog are among the technologies it publishes. A-listware also supports cloud application development and machine learning platforms, so it can address both the operational infrastructure and the software using it.
N-iX has dedicated cloud, AI, MLOps, and LLMOps practices that address the infrastructure needed to run machine learning systems in production. Its teams design model training and deployment pipelines, registries, monitoring, retraining workflows, Kubernetes environments, and infrastructure-as-code setups across AWS, Azure, and Google Cloud. N-iX also works on AI-ready data infrastructure and cloud environments with elastic compute for training and inference. For larger organizations, its ability to combine data platforms, software engineering, cloud modernization, MLOps, and AI implementation can reduce the handoffs between model development and production operations.
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net-devs combines AI engineering with cloud and platform engineering for production enterprise software. Its AI practice covers RAG, agents, LLM operations, and evaluations, while its infrastructure practice includes cloud-native architecture, infrastructure as code, platform engineering, Terraform, and Kubernetes across Azure, AWS, and GCP. This structure is useful for AI applications that must become part of a larger enterprise system instead of remaining standalone experiments. Production deployment and continued software evolution are part of the delivery process, with senior engineers retaining responsibility for architecture and final technical decisions.
DataArt provides infrastructure consulting and engineering for enterprises moving AI systems from experimentation into production. Its NVIDIA-focused AI practice covers data pipelines, model engineering, application integration, inference optimization, GPU-accelerated workloads, CI/CD for AI, observability, MLOps, and Kubernetes-based deployment. Cloud, hybrid, and on-premises environments are supported, which is useful where data residency or existing infrastructure limits a cloud-only approach. DataArt also brings conventional backend, frontend, and enterprise integration capabilities into AI infrastructure projects, allowing the model layer and surrounding software to be engineered together.
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SoftPro works across custom software, cloud development, and AI integration. Its cloud practice includes cloud-native application development, migration, containerization, infrastructure management, and re-architecture using AWS and Microsoft Azure. On the AI side, the company works with LLM integrations, RAG architectures, machine learning models, and workflow automation. This mix fits projects where infrastructure requirements are part of a broader web or enterprise software build, particularly for teams already using Microsoft technologies such as .NET and Azure. SoftPro also provides modernization and ongoing support for systems that continue evolving after deployment.
Itransition supports AI infrastructure through its machine learning, MLOps, cloud, data engineering, and DevOps services. Its AI practice includes automated model training and deployment, model version control, continuous monitoring, and production integration. The company works with SageMaker, Azure Machine Learning, Vertex AI, MLflow, Kubeflow, BentoML, and other AI engineering tools alongside conventional cloud infrastructure services. Its wider service portfolio also covers AWS DevOps and cloud infrastructure, which makes Itransition a practical option when an AI application has to be integrated into complex enterprise software and data systems.
21century.tech operates as an AI-native software studio focused on building and shipping production software with AI-assisted engineering. Senior engineers handle architecture, business logic, security decisions, reviews, and accountability, while AI tools support code generation, testing, documentation, and refactoring. Its delivery process includes CI/CD, testing, documentation, and deployment either into the client's existing infrastructure or infrastructure managed for the project. The infrastructure scope is less specialized than a dedicated cloud consultancy, but the studio is relevant to teams seeking rapid application development with production deployment included in the engagement.
ELEKS provides AI development, machine learning, MLOps, DevOps, cloud, and software engineering services. Its MLOps work focuses on automating model development, integration, testing, deployment, and operational workflows, while its DevOps practice supports the application infrastructure surrounding those models. ELEKS has also worked with an edge AI company on an MLOps SDK intended to optimize, secure, test, and deploy AI models and runtimes. This makes the company relevant for enterprises that need AI infrastructure services alongside broader product development, modernization, data engineering, and application integration capabilities.
Simform combines cloud engineering, MLOps, DevOps, application modernization, and AI development. Its MLOps offering includes managed MLOps infrastructure, model deployment, monitoring, maintenance, drift detection, and CI/CD for machine learning. The company also works across Microsoft Azure and other cloud environments, with cloud-native and containerized architectures forming part of its wider engineering practice. For companies building AI-enabled software rather than isolated models, Simform's application engineering and infrastructure services can cover both the customer-facing product and the operational systems needed to deploy and maintain AI functionality.
SoftServe has long-standing capabilities in MLOps, cloud modernization, AI engineering, and enterprise infrastructure. Its MLOps work includes ML infrastructure modernization, managed machine learning infrastructure, model workload migration, automated lifecycle management, and cloud-based model deployment. Current AI projects also use microservices, cloud-native architecture, managed pipelines, data engineering, and production MLOps to turn prototypes into scalable applications. SoftServe is especially relevant to larger organizations that need AI infrastructure to coexist with existing enterprise platforms, cloud transformation programs, observability, and application modernization work.
InData Labs focuses on data science and AI product engineering, with infrastructure support built into its route from architecture to production. Its AI product services include architecture design, MLOps, CI/CD, cloud engineering, DevOps, deployment, monitoring, and ongoing support. The company also advises on AI cloud services and MLOps platforms when teams need help selecting a production stack before implementation. This makes InData Labs particularly relevant to projects where data pipelines, machine learning models, application code, and infrastructure need to be planned together rather than handed between separate vendors.
Itexus combines AI engineering with DevOps and cloud infrastructure services, giving it a strong fit for production AI applications. Its DevOps practice covers cloud architecture, infrastructure as code, CI/CD, monitoring, containerization, security, backups, and production reliability across AWS, Azure, Google Cloud, and other platforms. For AI workloads, Itexus works with model serving, MLOps pipelines, private LLM deployment, AI data pipelines, and cloud machine learning platforms. Its technology stack includes Kubernetes, Docker, Terraform, MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, NVIDIA Triton, and BentoML.
Mobian Studio develops production software across AI, mobile, backend, and cloud infrastructure. Its AI work includes custom agents, private knowledge base assistants, computer vision, and LLM-powered workflows, while its broader engineering scope covers APIs, cloud infrastructure, production deployment, scalable architecture, monitoring, and post-launch support. Mobian can build an application from the product layer through backend and infrastructure, which makes it relevant for companies that want AI functionality integrated into a wider digital platform rather than managed as a standalone model project. The company also works on scaling existing infrastructure and integrating new systems with established technology environments.
AI infrastructure is increasingly becoming application infrastructure rather than a separate layer managed only by machine learning teams. Production AI now depends on familiar engineering disciplines such as CI/CD, containers, observability, security, capacity planning, and cost control, while adding model monitoring, evaluation, data pipelines, inference optimization, and lifecycle management. For smaller AI products, a unified engineering team can reduce coordination overhead between app development and infrastructure. Larger organizations often need deeper MLOps, cloud governance, hybrid deployment, and platform engineering capabilities. The strongest technical foundation is therefore one that can evolve as model usage, traffic, data volumes, compliance requirements, and infrastructure costs change.
