Docker Compose is the better starting point for a single-server or pilot on-premise LLM deployment, while Kubernetes is the right choice once an organization needs multiple GPU nodes, high availability or company-wide scale, and most enterprises eventually move from one to the other rather than picking permanently. Docker Compose sets up an inference engine, vector database and chat interface with one configuration file and almost no operational overhead, which suits a proof of concept or a department-level deployment serving a few dozen users on one or two GPU servers. Kubernetes, run with the NVIDIA GPU Operator for driver and device management, adds automatic failover, rolling model updates without downtime, horizontal scaling across nodes, and the ability to schedule multiple models or workloads across a shared GPU pool efficiently. The cost of that capability is real operational complexity, since a Kubernetes-based LLM platform typically needs at least one engineer with cluster experience to run well, whereas Docker Compose does not. A practical pattern is proving the use case on Docker Compose, then migrating the same containers to Kubernetes once user count, GPU count or availability requirements outgrow a single machine. Nanobase AI builds both, starting simple and scaling the architecture as usage grows.
The decision is about triggers, not preference
Rather than treating Kubernetes versus Docker Compose as a stylistic or team-preference choice, it is more useful to define specific triggers that signal a Docker Compose deployment has outgrown itself. The three clearest triggers are needing more than one GPU node, needing automatic failover if a node goes down, and needing to serve enough concurrent users that a single server's GPU capacity is no longer sufficient, and any one of these alone is usually enough to justify the migration. Below all three triggers, Kubernetes adds operational overhead without a corresponding benefit.
Decision table
Most rows in this table resolve to Docker Compose until one specific, identifiable trigger, not a vague sense that the deployment has "grown," appears.
| Situation | Recommended approach |
|---|---|
| Single GPU server, pilot or departmental use | Docker Compose |
| Single server, but need automatic restart and basic health checks | Docker Compose with restart policies, still sufficient |
| Multiple GPU servers, need workload scheduling across them | Kubernetes with the NVIDIA GPU Operator |
| Need high availability with automatic failover | Kubernetes |
| Company-wide scale, hundreds to thousands of users | Kubernetes, likely combined with a scheduler like Slurm for batch or fine-tuning workloads |
| Team has no Kubernetes experience and timeline is short | Start with Docker Compose, plan the Kubernetes migration as a distinct later phase |
Migration path from Compose to Kubernetes
- Containerize each service exactly as it runs in Docker Compose, confirming no service depends on Compose-specific networking or volume behavior that Kubernetes handles differently.
- Install the NVIDIA GPU Operator on the Kubernetes cluster, which automates GPU driver, device plugin, and monitoring setup across nodes, removing much of the manual GPU configuration Compose deployments handle per-server.
- Convert the Compose file into Kubernetes manifests or a Helm chart, mapping volumes to persistent volume claims and environment variables to ConfigMaps and Secrets.
- Deploy to a staging namespace first, running the same validation tests used in the original Compose deployment before cutting over production traffic.
- Set up horizontal scaling rules and multi-replica deployment for the serving layer, which is the main capability Compose could not provide.
- Decommission the original single-server Compose deployment only after the Kubernetes deployment has run successfully under real load for a defined observation period.
Migrating too early costs unnecessary operational complexity; migrating too late means an outage or capacity ceiling forces a rushed migration under pressure, so watching for the specific triggers above, rather than a fixed calendar date, is the better timing signal.
Where Slurm fits alongside Kubernetes
For organizations also running fine-tuning or batch evaluation jobs, Slurm is usually deployed alongside Kubernetes rather than instead of it, with each scheduler handling the workload it fits best. Kubernetes handles the always-on inference serving layer, while Slurm handles scheduled batch and training workloads that benefit from its job-queue model. This split is common enough in GPU infrastructure that it is worth planning for even if only inference is needed initially.
Frequently asked questions
Can Docker Compose handle a second GPU on the same server?
Yes, Docker Compose can address multiple GPUs on a single physical server without needing Kubernetes; the trigger for Kubernetes is specifically multiple servers, not multiple GPUs on one server.
Does moving to Kubernetes require re-architecting the application?
Generally no, if the application was containerized cleanly in Compose; the migration is mostly a configuration and orchestration change, though services with Compose-specific assumptions about networking or local file paths may need adjustment.
Is Kubernetes overkill for a permanent departmental deployment that will never scale further?
If the deployment is genuinely fixed at a single server indefinitely with no HA requirement, Docker Compose can remain the permanent choice; Kubernetes is justified by the triggers above, not by company size alone.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, starts most on-premise LLM deployments on the simplest infrastructure that meets the requirement, then plans and executes the migration to Kubernetes with the NVIDIA GPU Operator once a genuine scaling or availability trigger appears, drawing on the comparison in Kubernetes GPU Operator vs Slurm. The team also sets up Slurm alongside Kubernetes for organizations running fine-tuning or batch workloads in addition to live inference.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.