Every data strategy stands on a technical foundation. Reliable databases, fast messaging and scalable compute are what let a lakehouse architecture or an AI initiative actually deliver. As AI moves from experiment to production, that foundation matters more, because models need GPU resources, inference endpoints and operational tooling to run in the real world. evoila’s data platform engineering team builds exactly that.

We architect and operate production-grade data services across on-premises, hybrid and cloud. From automated database provisioning to Kubernetes-native platforms with Stackable, and from sovereign AI inference with Nvidia AI Enterprise and VMware Private AI Services to model serving, we deliver the layer that makes your data and AI workloads run reliably, securely and at scale.

Key benefits at a glance:

• Automated, self-service database and messaging provisioning. No tickets, no waiting
• Production-ready Kafka and RabbitMQ for high-throughput event streaming
• Kubernetes-native data platforms for maximum digital sovereignty
• Enterprise-grade NoSQL with MongoDB and Elasticsearch, built for scale and security
• Sovereign AI inference on your own infrastructure with Nvidia AI Enterprise, VMware Private AI Services and KServe
• Full lifecycle support: from architecture design through managed operations

Many organisations have grown their data infrastructure over time without a consistent platform model. The result is a patchwork of manually provisioned databases, standalone message brokers and siloed storage systems, each managed differently, scaled by hand and monitored with separate tools. When a development team needs a new database, it can take days or weeks of back and forth with operations. When a Kafka cluster reaches capacity, someone has to react manually.

At the same time, the pressure to operationalise AI is increasing. Data science teams build models in notebooks, but moving those models into production with proper GPU allocation, autoscaling, monitoring and governance requires infrastructure capabilities that many organisations do not yet have. The result is familiar. AI initiatives stall after the proof of concept, and the gap between experimentation and production grows.

This fragmented approach creates real problems: inconsistent configurations lead to security gaps, manual provisioning slows down teams, and the lack of standardised tooling makes it nearly impossible to enforce governance at scale. Meanwhile, new demands from AI workloads, real-time analytics, and streaming architectures push these legacy setups beyond their limits.

evoila bridges this gap by transforming your raw data infrastructure into an automated, production-ready platform. We design and implement declarative, code-driven data environments that treat databases, queues and pipelines as scalable software assets. By automating the underlying lifecycle operations, we free your data engineers and data scientists from infrastructure firefighting so they can focus entirely on delivering business value.

VMware Data Service Manager (DSM)
DSM is an infrastructure management layer that brings cloud-like self-service capabilities into on-premises environments. Platform teams define database service offerings such as PostgreSQL, MySQL, RabbitMQ or Kafka with standardised configurations, resource limits and security policies. Application teams then provision those services through an API or portal, with backup, monitoring and lifecycle management already built in. The advantage is clear. Your data stays on your infrastructure, while teams get the speed and convenience they expect from cloud-native services.

Stackable Data Platform
Stackable is a Kubernetes-native platform that uses custom operators to manage the full lifecycle of data services. Each component, whether Apache Spark for distributed processing, Trino for federated SQL queries, Apache Kafka for streaming or Apache Hive for metadata management, runs as a managed workload on Kubernetes with automated deployment, scaling, configuration and updates. The operator model means that complex operational tasks (rolling upgrades, configuration changes, scaling) are handled declaratively rather than manually. For lakehouse deployments, Stackable provides the compute and storage layer that runs on top of open object storage (S3-compatible or HDFS), forming a fully sovereign alternative to cloud-managed platforms like Databricks.

Apache Kafka
Kafka serves as the central nervous system for real-time data pipelines. We deploy Kafka in production-grade configurations with multi-broker clusters, rack-aware replication, and automated partition rebalancing. For integration with lakehouse architectures, we configure Kafka Connect with connectors for common source systems (databases via CDC, APIs, IoT endpoints) and sinks (Delta Lake, object storage, Elasticsearch). Our Kafka environments are designed for high availability from the start, including monitoring, alerting and automated failover.

RabbitMQ
RabbitMQ provides reliable, standards-based messaging for application integration and event-driven architectures. We deploy clustered RabbitMQ environments with quorum queues for data safety, federation for multi-site deployments, and management plugins for operational visibility. RabbitMQ excels in scenarios where message routing flexibility (topic, fanout, header-based routing) is more important than raw throughput.

MongoDB & Elasticsearch
For MongoDB, we engineer replica sets and sharded clusters with automated failover, encryption, and role-based access control. For Elasticsearch, we design index strategies, shard allocation, and lifecycle policies optimised for your data volume and query patterns, whether the use case is log analytics, application search, or security information and event management (SIEM).

VMware Private AI Foundation with NVIDIA
Private AI Foundation runs on VMware Cloud Foundation and provides a secure, sovereign platform for generative AI workloads. It integrates NVIDIA GPUs (via vGPU or passthrough) with model runtime management, a built-in model store, vector database integration, and RAG pipeline tooling. For enterprises already running VCF, this is the most direct path to production AI without exposing data to external cloud providers. It supports fine-tuning LLMs, running inference, and deploying RAG workflows, all within your data centre, under your governance policies.

KServe
KServe is a CNCF incubating project that provides a standardised, Kubernetes-native inference platform for deploying ML and generative AI models at scale. It supports multiple frameworks (TensorFlow, PyTorch, vLLM, NVIDIA Triton), offers serverless autoscaling from zero to multi-GPU, and integrates natively with NVIDIA NIM for optimised LLM inference. KServe handles the operational complexity of model serving canary rollouts, request batching, GPU scheduling, multi-node inference for large models, so your data science teams can focus on the models, not the infrastructure.

All components can be deployed on bare metal, VMware vSphere, or Kubernetes, on-premises, in a private cloud, or in a hybrid configuration with public cloud resources.

evoila has been engineering data platforms since its founding. Our roots are in infrastructure (VMware, Kubernetes, network & security) which means we understand the full stack beneath the data layer, not just the software on top. This is exactly why we are uniquely positioned to deliver AI infrastructure: we already own the layers that AI runs on.

Our platform engineers are certified across VMware, Kubernetes, Kafka, Elasticsearch, and all major cloud providers. We are a Broadcom Pinnacle Partner (the highest tier in the Broadcom Advantage Partner Program) and hold ISO/IEC 27001, BSI C5, and TISAX certifications, proof that we meet the strictest security and compliance standards

Strategic enterprise alliances

Data service automation: VMware Data Service Manager

Kubernetes-native data platform: Stackable Data Platform (Apache Spark, Trino, Apache Hive, Apache Kafka, Apache NiFi on Kubernetes Operators)

Event streaming & messaging: Apache Kafka, RabbitMQ (architecture, optimisation, managed services)

NoSQL & search: MongoDB, Elasticsearch (cluster design, performance tuning, managed operations)

Future-proof infrastructure and open standards

Infrastructure & orchestration: Kubernetes, VMware vSphere / VCF, Docker, Helm, ArgoCD

Cloud platforms: Microsoft Azure, Amazon Web Services, Google Cloud Platform

Monitoring & observability: Prometheus, Grafana, Elastic Stack

Certifications & partnerships: Broadcom Pinnacle Partner, Databricks Partner, Microsoft Silver Partner, AWS Partner, ISO/IEC 27001, BSI C5, TISAX