Choose an AI inference platform by matching it to your workload, security and operating constraints, then test shortlisted options against the same traffic pattern and service objectives. There is no universally best platform established by the available product documentation. Your first major decision is whether to use managed endpoints or operate a serving stack yourself.
What an inference platform needs to do
A production inference platform is more than a model server. It includes the serving engine and the systems that make model predictions available and manageable in production: APIs and endpoints, scheduling and routing, scaling, model artifacts, observability, validation, and security.
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Assess the whole deployment path. A serving engine may support your model and hardware but still require substantial work to integrate with your identity, networking, deployment, and monitoring systems.
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Choose between managed endpoints and self-managed serving
Managed endpoints can reduce the infrastructure work your team owns. Self-managed serving gives teams a deployment path when they are prepared to operate the serving stack and its supporting infrastructure, such as Kubernetes.
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| Approach | What your team operates | Best fit when | What to verify |
|---|---|---|---|
| Managed endpoint service | The provider manages endpoint infrastructure and offers serving-related features such as scaling, security, and monitoring; you still configure and operate your model deployment. | You want a provider-managed endpoint and its available controls fit your architecture and policies. | Endpoint type, network and identity controls, region, logging, scaling behavior, costs, and service limitations. |
| Self-managed serving stack | Your team deploys and operates the serving engine and the infrastructure around it, including orchestration where applicable. | You need a deployment path based on a serving engine and have the expertise and capacity to run it. | Model and hardware support, deployment complexity, upgrades, monitoring, failure handling, and who provides operational support. |
Neither approach automatically guarantees lower cost or better performance. Those depend on the workload, configuration, service level, and amount of operational effort involved.
Shortlist platforms by fit, not by a universal ranking
The following options are examples to evaluate, not a head-to-head ranking. Their documented features do not establish which performs best for your workload.
| Option | What its documentation establishes | Questions to answer for your deployment |
|---|---|---|
| NVIDIA Triton | An open-source server for multiple frameworks and CPU, GPU, or other targets, with configurable scheduling and batching, health endpoints, and utilization, throughput, and latency metrics. | Does it support your framework and target hardware? Does its batching behavior suit your traffic? Can your team handle the integration and ongoing operations? |
| vLLM | Project documentation provides a Kubernetes deployment path for its serving engine. | Does it support your model? How does it perform on your chosen hardware? Who will manage deployment and provide operational support? |
| Azure Machine Learning managed online endpoints | A managed endpoint path with serving, scaling, security, and monitoring features. Microsoft documents compute and networking charges and contrasts managed endpoints with customer-managed Kubernetes. | Does it meet your cloud, network, identity, monitoring, and scaling requirements? What will the required compute and networking cost for your workload? |
| Google Cloud Vertex AI online prediction | Online endpoint types differ in networking, isolation, traffic, and feature characteristics. Documented autoscaling and monitoring signals include CPU or GPU metrics and endpoint latency or response counts; some options have preview status or limitations. | Which endpoint type and region meet your connectivity needs? Are required features generally available for your intended use, and do its scaling signals and logging fit your service? |
| Amazon SageMaker AI hosting | AWS guidance covers managed inference hosting, autoscaling, multi-Availability-Zone deployment, and instance-family selection. | Does it fit your existing AWS architecture and availability design? Which instance family and scaling setup suit your measured workload? |
Define the workload and service objectives before testing
Write down the traffic and deployment conditions a candidate must handle. Record:
- The model, serving framework, backend, model size, hardware target, and software versions.
- Request and response sizes and distributions, including prompt and output distributions for language models.
- Whether inference is synchronous, streaming, or batch, plus expected concurrency and peak traffic.
- Deployment geography, required regions, and any network boundaries.
Set measurable service objectives that reflect how users experience the system: latency percentiles, throughput, availability, error budget, and acceptable scale-up delay. For LLM workloads, include time to first token and inter-token latency; a single overall latency figure can hide a slow first response or uneven token delivery.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a representative, controlled comparison
Use the same model, representative request mix, concurrency, hardware class where comparable, backend, and software versions across candidates. Record any differences that cannot be held constant: otherwise, the results describe different test setups rather than a fair platform comparison.
Capture the measurements needed to judge both responsiveness and capacity:
- Time to first token, inter-token latency, and request latency.
- Output throughput, concurrency, and error rate.
- Model size, prompt and output distributions, backend, GPU type, and software versions.
Measure behavior at typical and peak load, not just during a short low-concurrency run. Observe how the system responds to bursts and scaling events, and whether it meets the stated objectives while under load. Keep the configuration and workload record with the results so that a later deployment or version change can be compared meaningfully.
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Compare total cost at the service level you measured
Estimate the cost of delivering the same workload at the same service level. Include compute and networking, storage, idle capacity, reserved capacity where applicable, scaling headroom, and engineering and operations effort. A low compute price alone does not establish a lower production cost if the deployment needs more capacity or more staff time to meet its objectives.
Managed endpoint charges depend on provider rates, region, configuration, and usage; Microsoft documents compute and networking charges for Azure Machine Learning managed online endpoints. AWS recommends using metrics to evaluate instance-family price-performance. Calculate costs using your actual workload and current rates rather than relying on a generic break-even claim.
Check security and operating responsibilities before committing
Capabilities vary by endpoint type, configuration, and provider. Confirm that the specific deployment meets your requirements for:
- Identity, authentication, and who can access or invoke the endpoint.
- Network boundaries and private connectivity, where required.
- Logging and data handling, including what is recorded and who can access it.
- Deployment region and applicable policy requirements.
- Scaling, overload behavior, retries, and failure handling.
- Observability, upgrades, rollout and rollback, incident response, and support ownership.
Resolve hard requirements before performance testing. A fast candidate is not a viable production choice if its endpoint type or configuration cannot meet your security or operational requirements.
Make the production decision
Choose the candidate that satisfies hard requirements and meets the service objectives on your representative workload at an acceptable total cost. Before committing, verify that the production configuration can handle scaling and failure scenarios, that rollout and rollback responsibilities are clear, and that the team has the observability and support arrangements needed to operate it.
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