Data Platform Engineering

Engineered to run. Built to scale.

We design, deploy and operate the foundational data and AI infrastructure your business depends on, from automated database services and high-availability streaming to sovereign AI inference platforms.

Build your AI platform

The layer your data and AI actually run on

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

Platform Evolution

Stop provisioning infrastructure, start Engineering Platforms

When infrastructure is a patchwork of manual tasks, your data pipelines scale by headcount, not by code.

Many organizations 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 organizations 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 standardized 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.

Most legacy data infrastructure was never designed to scale automatically.

We engineer platforms that are.

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The Challenge

Four signs your data infrastructure is slowing growth

Manual pipelines and siloed stacks don’t just create overhead, they directly block your developers from shipping value.

Ticket-based provisioning

Hand-built environments slow engineering down.

Manual, ticket-based database provisioning and search cluster scaling create severe bottlenecks. Because these systems require constant manual intervention and deep specialist knowledge, development teams wait days or weeks for essential production environments to be ready.

Fragmented implementations

Missing platform standards create hidden compliance risks.

Operating without a standardized approach to deploying data services across environments leads to high infrastructure fragmentation. Without unified tooling, it becomes nearly impossible to enforce data governance, meet growing sovereignty requirements, and secure your environments at scale.

Brittle event-streaming

Message brokers lack the necessary enterprise-grade resilience.

Legacy message brokers and event-driven architectures are rarely engineered for modern production workloads. When data volume spikes, these unmanaged streaming platforms lack the automated scaling and recovery capabilities needed to prevent critical pipeline downtime.

Stalled AI production

High-value models remain locked in local environments.

Data science teams build highly promising algorithms in notebooks, but they rarely make it to production. AI initiatives routinely stall after the proof-of-concept phase because the necessary enterprise GPU infrastructure, secure inference endpoints, and automated MLOps tooling are completely missing.

Stop fighting your data pipeline bottlenecks.

Let’s connect your workflows to automated, declarative infrastructure built for modern AI and enterprise streaming.

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Our Solution

Declarative platforms built for production workloads

We do not stop at architecture. evoila designs, builds and operates the automated lifecycle of your data services, shifting operations from fragile manual provisioning to scalable, code-driven platforms.

Your data and AI platform

Engineered for Reliability, Automation & Sovereignty

evoila’s data platform engineering covers the full stack of foundational data and AI infrastructure. We do not just advise: we design, build and operate the platforms that power your data and AI workloads.

Automated data service provisioning

VMware Data Service Manager Integration

We architect and deploy VMware Data Service Manager to enable self-service provisioning of databases and messaging queues within your own infrastructure. Development teams get the databases they need in minutes instead of days, featuring consistent configurations, built-in security policies, and full auditability. With no cloud dependency required, everything runs entirely on your infrastructure under your absolute control.

Kubernetes-native data platforms

Sovereign Big Data with Stackable

For organizations that require maximum digital sovereignty and flexibility, we build modular data platforms on Kubernetes using the Stackable Data Platform. Stackable uses an Operator-based architecture to automate the deployment and lifecycle management of complex data workloads including Apache Spark, Trino, Apache Hive, and Apache Kafka. This delivers a fully open-source, vendor-independent lakehouse foundation without any hyperscaler dependency, running entirely on your own hardware or private cloud.

High-availability messaging

Event Streaming with Kafka & RabbitMQ

We design and deploy production-ready Apache Kafka and RabbitMQ environments engineered for high throughput, low latency, and resilient message distribution. Whether you need Kafka as the backbone for real-time data pipelines and CDC, or RabbitMQ for reliable asynchronous communication between microservices — we deliver architectures that are built for 24/7 operations, not proof-of-concept environments.

Enterprise NoSQL & search

Scalable MongoDB & Elasticsearch Environments

We build and tune MongoDB and Elasticsearch environments tailored for enterprise scale. This includes cluster design with integrated security (TLS, RBAC, encryption at rest), automated scaling strategies, persistent storage configurations, and performance optimization. Whether you need Elasticsearch for log analytics, full-text search, or observability, or MongoDB as a flexible document store for application data, we engineer environments that handle your workload reliably.

AI platform infrastructure

From Notebooks to Secure Production

We build the infrastructure layer that brings AI models from notebooks safely into production. Using VMware Private AI Foundation with NVIDIA, we deploy sovereign AI environments on your existing VCF infrastructure equipped with GPU passthrough, model runtime management, and RAG-ready vector database integration, all without sending data to a public cloud. For Kubernetes-native environments, we deploy KServe as a scalable, framework-agnostic model serving platform that supports autoscaling inference endpoints, multi-model serving, and native integration with NVIDIA NIM for optimized GPU inference. Whether your team runs classical ML models, fine-tuned LLMs, or retrieval-augmented generative AI, we engineer the serving and orchestration layer that makes it production-ready.

Tech-Deep-Dive

The technology stack behind our data & AI platforms

Our data platform engineering is built on proven, production-grade technologies. Here is a closer look at the core components and how they work together.

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 standardized 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 optimized 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 center, under your governance policies.

KServe
KServe is a CNCF incubating project that provides a standardized, 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 optimized 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.

Technical Advantages

Engineering the platform your data architecture deserves

We bridge the gap between cloud-like speed and absolute data control by building on proven open standards.

Sovereign self-service without cloud dependency

VMware Data Service Manager brings cloud-like provisioning speed into your own data center. Development teams get standardized databases and messaging services in minutes, fully automated, policy-compliant and without any data leaving your infrastructure.

Zero vendor lock-in

The Stackable Data Platform runs entirely on Kubernetes and open-source components. There is no vendor lock-in, no proprietary runtime, and no hyperscaler dependency. You own the platform, the configuration, and the data — a critical requirement for regulated industries and organizations with strict data residency policies

Modular & composable architecture

Every component in our data platform stack can be deployed independently or as part of an integrated platform. Need just Kafka managed services? Done. Want a full on-premises lakehouse on Stackable with Kafka, Spark, and Trino? Also done. The architecture grows with your requirements.

Air-gapped AI infrastructure

With Nvidia AI Enterprise and VMware Private AI Services on VCF and KServe on Kubernetes, you can run model training, fine-tuning, and inference on your own infrastructure. Your data and models stay sovereign, your compliance requirements are met, and your AI workloads get the GPU resources they need — without a single API call leaving your network

Flexible managed operations

Every platform we build is designed to be operated, by your team, by evoila or jointly. Managed service tiers range from 8×5 support to full 24/7 operations with active monitoring, incident management, proactive maintenance and security remediation.

Your partner of choice

Data platform engineering needs production experience

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

What makes evoila different

We are not just consultants who design architectures on paper. We build them, we deploy them, and we operate them. Nearly 500 employees across multiple European locations deliver hands-on engineering, managed services, and 24/7 support. When something breaks at 3 AM, our on-call team is already on it.

Broadcom Pinnacle Partner

The highest tier in the Broadcom Advantage Partner Program, proving enterprise infrastructure excellence.

Enterprise certifications

Fully audited and compliant with the strictest global security frameworks including ISO 27001, BSI C5, and TISAX.

24/7 managed operations

Real hands-on engineering and a dedicated on-call support team that is already active when something breaks at 3 AM.

European engineering scale

Certified platform experts across multiple European locations providing reliable full-stack deployment capabilities.

Technologies & Partner

Strategic alliances meet open technology

These are the enterprise platforms and open-source frameworks behind our implementations.

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, optimization, 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

Our Process

How we work with you

From Architecture Design to Fully Managed Operations

01 | Assessment & architecture design

We start with a comprehensive assessment of your current data infrastructure to identify operations bottlenecks and scaling limits. Based on these insights, we engineer a target architecture tailored precisely to your requirements for automation, scalability, data sovereignty, and resilience.

02 | Implementation & deployment

Our platform engineers build and deploy your new data environment across private, public, or hybrid cloud infrastructures. Following infrastructure as code principles with Terraform, Helm, and GitOps, we ensure every single deployment is reproducible, secure, version-controlled, and fully auditable.

03 | Managed operations

After go-live, we secure your platform continuity with tailored operations support. We offer two managed service tiers:

Managed Service Full: 24/7 or 9×5 support with time-to-response and time-to-resolve SLAs, active monitoring, incident and problem management, proactive maintenance (system checks, updates, patches, security remediations).

Managed Service Lite: 9×5 support with time-to-response SLA and incident/problem management, a lighter option for organizations that handle monitoring and proactive maintenance internally.

No modern AI without a rock-solid platform

We engineer automated, resilient & sovereign infrastructure built to scale.

Build your Platform

Not sure where to start or you are facing a different challenge? Tell us and we will find a solution.

Every enterprise operates on a unique legacy stack and under different regulatory compliance rules. Drop us a brief message below to start a non-binding, strategic conversation with our lead data architects and find the right transformation path for your environment.

FAQs

It refers to the design, deployment and operation of the foundational infrastructure that stores, moves and processes your data, including databases, message brokers, streaming platforms, search engines, AI inference endpoints and the orchestration layer that ties them all together. This layer sits beneath your analytics, BI, and AI workloads.

No, we deploy data platforms on bare metal, VMware vSphere and Kubernetes, depending on your existing infrastructure and requirements. While Kubernetes-based deployments (e.g. with Stackable or KServe) offer the highest level of automation and portability, they are not a prerequisite. VMware Private AI Foundation on VCF is an equally viable option for AI workloads.

VMware DSM provides the same self-service provisioning experience as a cloud database service (like AWS RDS or Azure Database), but everything runs on your own infrastructure. Your data never leaves your data center, and you maintain full control over configurations, security policies, and resource allocation. This is especially relevant for organizations with data residency or sovereignty requirements.

Absolutely. Our services are modular. Many customers start with managed Kafka or RabbitMQ operations and expand the scope over time. You do not need to commit to a full platform transformation to benefit from our expertise.

Stackable is an open-source, Kubernetes-native platform that automates the deployment and management of data services like Spark, Trino, Kafka, and Hive using Operators. It makes sense when you want to build a lakehouse or analytics platform on-premises (or in a private cloud) without depending on a hyperscaler or a proprietary platform like Databricks. It is particularly relevant for organizations in regulated industries or those with strict digital sovereignty requirements.

We build the infrastructure layer on which AI runs. For VMware-based environments, we deploy the Nvidia AI Enterprise and VMware Private AI Services Foundation on VCF, which provides GPU management, model runtimes and RAG pipelines within your data center. In Kubernetes environments, we deploy KServe with NVIDIA NIM integration to enable scalable, autoscaling model inference. Both approaches keep your data and models secure while delivering production-grade AI serving capabilities.

The Nvidia AI Enterprise and VMware Private AI Services Foundation runs on the VMware Cloud Foundation and uses the NVIDIA GPUs in your existing or new servers. If you already use VCF with NVIDIA GPU-capable hardware, you can activate Private AI services without changing your platform. The foundation provides model runtimes, a model store, vector databases and RAG tooling, all within your own infrastructure, while Evoila handles the architecture, deployment and operations.

We offer two tiers. Managed Service Full includes 24/7 or 9–5 support, SLAs for time to resolve issues, active monitoring, proactive maintenance and security remediation. Managed Service Lite provides 9×5 support and incident management, but not active monitoring or proactive services. Both tiers are available for all components that we deploy, including AI inference platforms.