Open-Source · Technologies
Docker
Package it once, run it anywhere, keep it isolated.
Docker packages software into containers that run the same on a laptop, a server or the cloud. For AI stacks it is the difference between a pile of fragile installs and a tidy estate of isolated, restorable services. We containerise AI workloads so they deploy cleanly, stay separated and survive a rebuild.
CONTAINER ESTATE · EXAMPLE HOST
SERVICE
STACK
ISOLATION
STATUS
model-server
Local models
Own container
RUNNING
agent-stack
Agents and tools
Own network
RUNNING
vector-db
Memory store
Own volume
RUNNING
monitoring
Metrics and logs
Partial
REVIEW
installed-by-hand
Direct on host
None
RISK
Sample host
running · review · risk
In plain terms
It works on my machine is not a deployment strategy.
Containers make software portable, isolated and rebuildable. Here is what changes.
Without it
- Software installed by hand, differently every time
- One broken upgrade taking the whole server with it
- AI experiments and production tangled on one box
- A rebuild that takes a week and a lot of guessing
With it
- Every service packaged, versioned and repeatable
- Upgrades and rollbacks that touch one container, not the host
- Experiments isolated from the stacks that matter
- A rebuild that is a checkout and a start command
What CG TECH can do with Docker
The work, broken into the parts that matter.
Every service in its own box
Model servers, agent runtimes, databases and dashboards each run in their own container with defined networks and volumes, so nothing steps on anything else.
services that stay in their lane
The stack, written down
A compose file describes the whole stack in one place: what runs, how it connects, where data lives. It is deployment and documentation in the same artefact.
the stack, described in one file
Blast radius by design
Agents and experiments run in containers with only the access they are given, which contains mistakes and limits what a compromised service can reach.
mistakes that stay contained
Recovery as a routine
With state on volumes and stacks in files, backup means copying data and rebuild means starting containers, turning disaster recovery from a project into a procedure.
rebuilds measured in minutes
How an engagement runs
From hand built to containerised, step by step.
01
Map
We inventory what runs, how it was installed and what depends on it.
02
Design
Stacks, networks, volumes and backup designed as one system.
03
Migrate
Services containerised and cut over, one at a time, safely.
04
Handover
Compose files, runbooks and habits your team owns.
Questions we hear a lot
Common questions about Docker
What is Docker?
Docker packages software into containers that run the same way on a laptop, a server or the cloud. For AI stacks it is the difference between a pile of fragile installs and a tidy set of isolated services you can rebuild at any time.
What does Docker actually solve for us?
Consistency and isolation. Software runs the same everywhere it is deployed, and each service is contained, so upgrades, rollbacks and rebuilds stop being frightening.
Is Docker only for developers?
No. Any self hosted stack benefits: AI tools, dashboards, databases and internal apps all run cleaner as containers, and the operational payoff lands on whoever maintains the server.
What about Kubernetes?
Usually later, sometimes never. For most self hosted AI estates, Docker with compose is the right amount of machinery. We recommend the simplest setup that meets your scale, honestly.
Can containers hold our AI data safely?
Yes, with volumes designed and backed up deliberately. Data outlives containers by design, and we set the storage and backup patterns as part of every build.
What is a container, in plain terms?
A container is the application plus everything it needs to run, sealed in one box. Move the box to another machine and it behaves the same, because it is not relying on whatever happens to be installed there.
Does Docker slow things down?
Barely. Containers share the machine operating system rather than running a whole second one, so the overhead is small. For AI work the hardware you give them matters far more.
What happens if the server dies?
You rebuild it from the same definitions and start the containers again. That is the main reason we containerise AI workloads: recovery becomes a routine task instead of a rescue mission.
Ready when you are
Taming a self hosted stack? Let us talk.
A discovery session maps your services, your risks and your quick wins. You keep the plan either way.
What to expect
- A consultant replies within 4 business hours
- Session booked to understand your requirements
- We will provide you with a fixed price quote