Model serving
Select a serving stack, optimize batching and concurrency, manage model versions and plan rollout safety.
Self-hosted LLM
Self-hosted LLM Guide
Decide when self-hosted LLM infrastructure makes sense compared with managed APIs.
Executive Summary
Self-hosting an LLM means the team takes responsibility for the model serving layer, infrastructure, security, scaling, monitoring, upgrades and cost management. It can be the right choice when data control, deployment flexibility, predictable volume or governance requirements outweigh the speed of a managed API.
It is not automatically more private, cheaper or more reliable. Those outcomes depend on architecture and operations. A managed API may be the better answer for early products, variable demand and teams without infrastructure capacity. A dedicated managed endpoint can be a middle path when a team needs more control than a shared API but does not want to operate the full stack.
GPU infrastructure
Choose cloud GPUs, reserved capacity, colocation or owned hardware based on utilization and governance needs.
Security
Control network access, secrets, image provenance, audit logging, data retention and incident response.
Operations
Monitor latency, throughput, errors, saturation, queue depth, model quality and deployment health.
Cost management
Track utilization, idle capacity, storage, data movement, support and engineering time.
Governance
Document model sources, licensing considerations, evaluation results, data flows and approval processes.
Deployment Model Comparison
| Model | Best for | Control | Operational burden |
|---|---|---|---|
| Managed API | Fast product development and uncertain demand | Lower | Low |
| Dedicated managed endpoint | Production workloads needing isolation or predictable latency | Moderate | Medium |
| Cloud GPU self-hosting | Custom models, private networks and stable workloads | High | High |
| Owned hardware | Long-horizon utilization with strong physical control needs | Very high | Very high |
Reference Architecture
Pros
- More control over model selection, deployment topology and data flows.
- Potentially better economics for stable, high-volume workloads.
- Can support custom models, private networks and stricter governance requirements.
Cons
- Requires infrastructure, reliability, security and model-serving expertise.
- Can create idle capacity and operational overhead if demand is uneven.
- Needs disciplined monitoring, evaluation, patching and incident response.
Related Guides
FAQ
When does self-hosting make sense?
It can make sense for stable workloads with privacy, residency, customization or cost-control requirements and a team that can operate infrastructure.
When are APIs better?
Managed APIs are usually better for early products, small teams, fast iteration and workloads without strict infrastructure control requirements.
Does self-hosting guarantee lower cost?
No. It can lower unit cost for some stable high-volume workloads, but operations, reliability, security and staff time must be included.
Is self-hosting required for data residency?
Not always. Some teams can meet requirements with approved cloud regions, private deployments or dedicated managed inference, depending on policy and contracts.