Self-Hosted vs Managed Inference

Deploying a machine learning model for inference comes down to two broad strategies. Self-hosted inference means running the model on infrastructure you control, whether on-premises or in your own cloud account, using serving engines like vLLM or TGI. Managed inference means calling a provider's endpoint, where the vendor runs the model and the hardware. The decision balances control and cost-at-scale against simplicity and speed.

Key Differences

Privacy and control favor self-hosting. When you run the model yourself, data never leaves your environment, which is decisive for regulated, sensitive, or residency-constrained workloads. You also control exactly which model and version run, and you can fine-tune or modify freely. The cost is operational: you provision GPUs, configure autoscaling, monitor latency, and own uptime, all of which require real ML serving expertise.

Managed inference inverts this. A single API call gives you a working endpoint with elastic scaling and provider-backed reliability and service-level agreements, with no infrastructure to run. This makes launch fast and operations trivial, at the cost of sending data to the provider (unless a private deployment is available) and being bounded by the models and options the provider offers.

Cost depends on the traffic shape. Self-hosting is cheaper at high, steady utilization where a well-used GPU beats per-token pricing, while managed endpoints are more economical at low, variable, or spiky volume where you would otherwise pay for idle hardware.

When to Choose Self-Hosted Inference

Choose self-hosted inference when data privacy, residency, or compliance requirements demand that models run in your environment. It is the right call at high, steady request volumes that amortize the fixed cost of GPUs, and when you need full control over model selection, versioning, and customization. It suits teams with the platform expertise to operate serving infrastructure reliably.

When to Choose Managed Inference

Choose managed inference when you want to launch fast with minimal infrastructure, or when traffic is low, variable, or spiky and paying per token avoids idle-hardware waste. It is ideal for small teams without ML serving expertise and for projects where speed to market and operational simplicity outweigh the desire for control. Provider SLAs and elastic scaling remove much of the reliability burden.

Practical Considerations

A hybrid approach is increasingly the norm: managed endpoints for spiky, experimental, or hardest-reasoning workloads, and self-hosted serving for high-volume, latency-sensitive, or privacy-bound paths, all behind a common abstraction. Model the cost honestly, since self-hosting only wins at high, sustained GPU utilization, and an idle accelerator is pure waste, while per-token pricing is predictable but can grow surprisingly fast at scale. On the operational side, self-hosting means owning autoscaling, observability, security patching, and keeping pace with new models, which requires real expertise. For managed inference, scrutinize data-handling and retention policies, regional availability, and rate limits, and keep an exit path so a pricing or policy change does not strand your application.

Verdict

Match the deployment model to your constraints. Self-hosting wins on privacy, deep control, and cost at sustained high volume, provided you can operate it. Managed inference wins on simplicity, speed, and economics for variable or modest traffic. Many organizations adopt a hybrid: managed endpoints for spiky or experimental workloads and the hardest models, self-hosted serving for high-volume, privacy-sensitive paths, all behind a common abstraction so they can route each workload to the most appropriate option.