Infrastructure and AI capacity without the excess spend
We set up servers, Kubernetes, AI models and custom services for small teams. Architecture is matched to your real workload, so you don't pay for capacity that sits idle.
We work on your own infrastructure or provide a managed environment — whichever makes more sense for your project.
How a request flows
A simplified path from your application's request to compute, storage and monitoring. Hover or tap a node for details.
Select a node on the diagram
Why teams work with DVO
We cover the technical side so you can focus on the product and your clients.
No separate DevOps team
You get infrastructure engineering when you need it, without hiring a full-time specialist.
Architecture for the real workload
We look at the load profile first and pick resources second. No oversized servers bought "just in case".
Shared GPU capacity
If a dedicated GPU server would idle most of the time, a share of a common compute pool can replace it.
From idea to a working setup
Design, launch, documentation and handover — in sequential stages with a visible result at each one.
Transparency and control
Infrastructure is described as code, access and monitoring are configured, and the documentation stays with you.
Automation and APIs
File handling, data processing and routine operations move into services that integrate with your tools.
What we do
Start with one area and expand the scope as the project grows.
End-to-end DevOps
Infrastructure design, containerisation, CI/CD, monitoring, backups and security.
- Kubernetes and K3s
- CI/CD and GitOps
- Monitoring and backups
Managed infrastructure
Three engagement models: on your servers, fully managed, or inside the shared DVO infrastructure.
- Your infrastructure
- Full support
- Shared resources
AI and GPU
Running models on GPUs, ComfyUI, job queues, API access and automatic scaling.
- ComfyUI workflows
- GPU pools
- Model APIs
Custom automation
Internal services, Telegram bots, integrations, bulk file processing and fast MVPs.
- API integrations
- Data processing
- MVP in weeks
Media Processing
A ready platform for image, video and audio processing — with a UI and an API.
- Conversion
- Subtitles
- Bulk processing
Cost optimisation
An audit of current bills, resource rebalancing, and the right plans and instance types.
- Spend audit
- Right-sizing
- Migration
DVO Media Processing
One platform for image, video and audio processing
Instead of a pile of separate utilities and scripts — a single service where files are processed manually through the interface or automatically through an API. Built for agencies, content teams and products that handle a lot of media.
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- File compression
- Resolution changes
- Format conversion
- Cropping
- Background removal
- Text and logos
- Subtitles
- Video for TikTok, Reels, Shorts
- Large files
- Bulk processing
- API for automation
- Custom features
Shared infrastructure: how it works
A dedicated server isn't always necessary. Quite often the workloads of several teams fit comfortably into a single compute pool.
On the client's infrastructure
We configure your servers and services, describe the setup as code, prepare the documentation and hand the finished solution to your team.
Best for
Teams that already have their own cloud accounts or hardware and want to keep full control.
- All resources and credentials stay with you
- Infrastructure as code plus documentation
- Knowledge transfer to your team
- Optional ongoing support on request
A simple example
Instead of renting a dedicated GPU server at roughly $400–500 per month, several clients can use a shared compute pool and pay only for the share of resources they need.
Important
We don't promise a fixed saving. The final cost always depends on the volume of jobs, the type of processing and your latency requirements — which is why it is calculated individually.
How a shared GPU pool is split
An illustration of the principle: one GPU can serve several projects as long as none of them saturates it continuously.
Dedicated server for one project
Shared pool across several projects
Project A · B · C · D
How we work together
Clear stages with an agreed result at every step.
- 01
Intro and scope
We discuss what exists today, the goals and the constraints. If infrastructure is already running, we review the bills and the configuration.
- 02
Architecture and estimate
We propose a solution, explain exactly what drives the cost, and point out the cheaper alternatives.
- 03
Launch
We set up the environment, CI/CD, monitoring and backups — and migrate the project if a move is needed.
- 04
Handover or support
We hand over documentation and access, or take the infrastructure into ongoing operations.
- 05
Growth
We scale resources as the load grows, add new services and review the cost on a regular basis.
On your own vs. with DVO
A comparison of both approaches for a team without an in-house infrastructure engineer.
Getting infrastructure up
On your own
A developer is pulled off the product and learns DevOps alongside their real work
With DVO
An engineer designs and launches the infrastructure while the team stays on the product
Choosing resources
On your own
It's easy to take a server with headroom and pay for capacity nobody uses
With DVO
The configuration matches the load profile and is revisited as things change
GPUs for AI workloads
On your own
A rented GPU is billed around the clock, even when there are no jobs
With DVO
Start in a shared pool and move to a dedicated GPU once that becomes the better deal
CI/CD and releases
On your own
Manual deploys, environments that drift apart, painful rollbacks
With DVO
Automated pipelines, identical environments, predictable rollback
Monitoring
On your own
Users are usually the ones reporting the outage
With DVO
Metrics, logs and alerts are in place before launch
Backups
On your own
Taken irregularly, restore never tested
With DVO
A schedule, off-server storage and verified restores
System knowledge
On your own
Lives in one person's head
With DVO
Configuration as code and documentation stay with you
What people come to us with
Pick the situation closest to yours and see where to start.
We need an MVP live quickly
We roll out a minimal working environment: a server or K3s cluster, domain, SSL, CI/CD and basic monitoring. The infrastructure then grows with the product.
Learn moreTechnologies
We work with tools that are actively maintained and straightforward to operate.
service areas
from DevOps to media processing
technologies in the stack
proven tools, no exotics
engagement models
your server, managed, or shared resources
working languages
Ukrainian, English, Russian
Orchestration and containers
- Kubernetes
- AWS EKS
- K3s
- Docker
- Helm
- Argo CD
- Horizontal Pod Autoscaler
- KEDA
- Kubernetes RBAC
CI/CD and delivery
- GitLab CI/CD
- GitHub Actions
- GitOps
- GitLab Runner
- Kaniko
- AWS ECR
- Zero-downtime deploy
- Rollback
Cloud and compute
- Amazon EC2
- Amazon S3
- Amazon EBS
- AWS NLB
- AWS CloudFront
- Amazon SES
- AWS IAM
- Hetzner Cloud
- GPU g4dn
Infrastructure as code
- Terraform
- Terraform modules
- Infrastructure as Code
- Ansible
- DNS as code
- Cloudflare API
Networking, ingress and security
- NGINX Ingress
- Traefik
- Cloudflare
- Cloudflare WAF
- cert-manager
- Let's Encrypt
- TLS / SSL
- Network Policies
- Load balancing
Monitoring and logs
- Prometheus
- VictoriaMetrics
- Grafana
- Loki
- Grafana Alloy
- Alertmanager
- kube-prometheus-stack
- Sentry
Databases and storage
- PostgreSQL
- MySQL / MariaDB
- ClickHouse
- Redis
- S3 / MinIO
- Prisma
- Backup & restore
AI and media processing
- ComfyUI
- FFmpeg
- GPU workers
- Shared GPU pools
- RunPod
- Vast.ai
- Speech-to-text
- Task queues
Systems and development
- Linux
- Ubuntu
- Bash
- Python
- Node.js
- REST API
- Webhooks
- Telegram Bot API
- Git
Frequently asked questions
If your question isn't here, write to us and we'll answer specifically.
Tell us about your project
Describe the task and we'll propose an infrastructure option and show what the cost is made of.