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The main alternatives to a fully managed inference platform are Kubernetes-hosted endpoints and self-managed inference servers such as vLLM, Text Generation Inference (TGI), llama.cpp, Ollama, LiteLLM or NVIDIA Triton. Both give you more control over the serving stack, and in exchange your team takes on more infrastructure and lifecycle work. There is a third, in-between path: managed services that let you bring your own container or inference engine. Which one fits depends on who you want to own operations, which engine your model needs, and what your traffic looks like. It does not depend on a universal “cheaper” or “faster” winner. The official documentation reviewed here does not establish one.
What “managed” actually removes
Before choosing an alternative, it helps to be clear about what you would be taking back. Azure’s documentation describes managed online endpoints as including managed compute provisioning, updates and removal. Its Kubernetes online endpoints leave node provisioning and maintenance to you, and are aimed at users who prefer Kubernetes and can self-manage infrastructure (Microsoft Learn). Hugging Face likewise describes its Inference Endpoints as managed infrastructure, with lifecycle operations such as start, stop, scaling and health and performance monitoring handled through the service (Hugging Face).
Leaving a managed platform therefore means you own some or all of these jobs: provisioning and maintaining nodes, packaging the model and its dependencies, scaling, monitoring, security configuration, upgrades and incident response.
The alternatives, compared
| Path | What you run | What you own | Questions to ask |
|---|---|---|---|
| Kubernetes-hosted endpoint | The model endpoint on a Kubernetes cluster you operate | Node provisioning, maintenance, upgrades, scaling, incident response | Does the team already run Kubernetes in production? |
| Self-managed inference server | An engine such as vLLM, TGI, llama.cpp, Ollama, LiteLLM or Triton on infrastructure you select | Packaging, scaling, observability, security, engine upgrades | Does the engine support your model and hardware? |
| Bring-your-own-container on a managed service | Your own container image, hosted by the provider | Code, dependencies and container stack; the provider keeps the compute operations | How much serving-stack control do you need? |
| Serverless managed inference (for contrast) | A provider endpoint that allocates compute per request | Very little infrastructure, but feature limits apply | Can the workload tolerate cold starts and feature exclusions? |
Option 1: Kubernetes-hosted endpoints
This is the most direct alternative if your organisation already runs Kubernetes. Azure explicitly documents Kubernetes online endpoints as a separate option beside managed online endpoints, intended for teams that prefer Kubernetes and can manage the infrastructure themselves (Microsoft Learn). The attraction is consistency: model serving sits next to your other workloads and follows the same deployment and access practices.
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The cost is that node provisioning and maintenance become your responsibility. If nobody on the team already operates a cluster, this path adds a platform to run before you serve a single prediction.
Option 2: Self-managed inference servers
Here you run serving software directly on machines or clusters you control. These tools are not interchangeable, so group them by what they are built for.
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Language-model engines: vLLM, TGI, SGLang, llama.cpp
Hugging Face’s Inference Endpoints documentation names native support for vLLM, TGI, SGLang, llama.cpp and Text Embeddings Inference (Hugging Face). That is useful for self-hosting too. It shows which engines are established enough for a managed service to build on, and it means you can start on a managed endpoint and later move the same engine to infrastructure you run.
Local and developer-friendly runtimes: Ollama and LiteLLM
The Hugging Face Hub guide documents local endpoint use with llama.cpp, Ollama, vLLM, LiteLLM and Text Generation Inference, alongside its managed service (Hugging Face Hub guide). The same client code can target a managed endpoint or a local server, so you can prototype locally and keep the option of moving between hosting models.
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Multi-framework serving: NVIDIA Triton
Triton Inference Server is open-source serving software for models built with multiple frameworks. AWS documents hosting Triton containers on SageMaker for single-model endpoints, ensembles and multi-model endpoints (AWS). That makes Triton a good example of a portable engine. You can run it yourself, or put the same server inside a managed hosting wrapper.
Option 3: Keep the managed compute, take over the container
You do not have to go fully self-managed to gain control. Azure offers no-code, low-code and bring-your-own-container deployment paths, and they differ in how much code, how many dependencies and how much of the container stack you supply. No-code deployment covers common frameworks including scikit-learn, TensorFlow, PyTorch and ONNX through MLflow and Triton (Microsoft Learn). If the standard path cannot run your model, a custom container may solve the problem without taking on cluster operations.
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Where serverless fits
Serverless inference is still a managed service, but it is often the cheapest way to avoid running anything at all, so it belongs in the comparison. AWS describes SageMaker Serverless Inference as suited to workloads with idle periods that can tolerate cold starts. The same documentation states it does not support several real-time inference features: GPUs, VPC configuration, network isolation, multi-model endpoints, data capture, Model Monitor and inference pipelines (AWS). Service features change, so check the live limitations before committing.
If your model needs a GPU or must sit inside a private network, those exclusions rule serverless out, and you are back to a provisioned managed endpoint or one of the alternatives above.
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How to choose
- Check engine and hardware support. Confirm that the engine (vLLM, TGI, Triton and so on) runs your model on the accelerators you can get.
- Decide who owns operations. Name the people responsible for upgrades, scaling and on-call. If no one can be named, a managed endpoint or a bring-your-own-container path is likely the safer fit.
- List the security and network requirements. Private networking, network isolation and monitoring requirements can exclude some managed options, as the serverless limits above show.
- Characterise the traffic. Steady, high-utilisation traffic and spiky traffic with long idle periods point to different hosting models. Idle-heavy workloads that tolerate cold starts are the documented fit for serverless.
- Measure on your own workload. Run the same model and request mix on each candidate and record latency, throughput and total cost.
Cost and performance: what is not established
The official documentation reviewed here contains no neutral cross-provider price table and no independent workload benchmark. Claims that self-hosting is always cheaper, or that one platform is fastest, are not supported by it. Utilisation, model size, traffic shape, accelerator choice, redundancy, engineering labour and operational overhead all move the result, so treat them as variables to measure.
The one number worth treating with caution is AWS’s statement that SageMaker deployment offers more than 100 instance types, alongside single-model endpoints, multi-model endpoints, serial inference pipelines and serverless inference (AWS). It is a vendor-reported inventory of options. It says nothing about speed or price.
When a managed endpoint is still the better answer
Self-managed paths buy control, not automatic savings. If your priority is reducing operational work, such as a small team, no existing cluster expertise or a need to ship quickly, a managed endpoint remains a legitimate choice, and you can often move to a self-run engine later because many of these engines are common to both models.
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