Compare serverless and dedicated LLM inference with Polyaxon

Polyaxon lets a team evaluate a managed model API and a dedicated inference service through the same experiment workflow. Package the request harness as a component, run it against a fixed evaluation manifest, and compare quality, latency, failures, and estimated cost alongside the configuration that produced each result.

That workflow makes the hosting decision specific to your application. A support assistant with occasional traffic may benefit from usage-based inference, while a sustained batch workload may use dedicated GPU capacity efficiently. Polyaxon organizes the workloads and evidence; the chosen provider, serving software, and cluster determine model scaling and billing behavior.

Keep the benchmark independent of the endpoint

Build one evaluator that accepts a hosting mode, model revision, request manifest, and client concurrency. Its adapters should normalize requests and responses without hiding meaningful differences such as provider-side filtering, token limits, or unsupported sampling parameters.

Package that evaluator as a Polyaxon job component. Include the client libraries, evaluator code, and dependencies in a versioned image. Supply credentials through the configured connections required by that run. Log endpoint identifiers and configuration, while excluding credentials from parameters and artifacts.

Keep the manifest and evaluation criteria fixed across candidates. If the provider and dedicated endpoint serve different model revisions, describe the experiment as a comparison of complete serving configurations. It cannot isolate the effect of hosting alone.

Run dedicated inference as an explicit service

A dedicated endpoint needs a serving implementation that loads the model, handles requests, and reports readiness. Polyaxon services can run your chosen container and declare its HTTP ports. Use the image and command recommended for that serving implementation; Polyaxon does not replace its batching, cache, or GPU memory management.

Place CPU, memory, and GPU requests in the workload or an approved scheduling preset. Keep the model revision, resource shape, replica count, and startup policy with the experiment record. If your setup uses sharding or specialized networking, verify those requirements in the serving stack and cluster configuration.

Declared HTTP service ports use Polyaxon's authenticated service proxy. Decide how the benchmark client will authenticate and reach the endpoint. Readiness should include successful model loading and a representative request; a running pod alone does not prove that inference is ready.

The managed candidate can remain external. Its benchmark job calls the provider's API, while the dedicated candidate's job calls the configured service endpoint. Both report measurements through the same Polyaxon tracking interface.

Sweep a small, interpretable comparison

Suppose benchmark.yaml defines your evaluator component with a string input named hosting_mode and an integer input named client_concurrency. This operation runs both hosting modes at two request concurrency levels:

version: 1.1
kind: operation
name: inference-hosting-comparison
pathRef: ./benchmark.yaml
matrix:
  kind: grid
  concurrency: 1
  params:
    hosting_mode:
      kind: choice
      value: [managed, dedicated]
    client_concurrency:
      kind: choice
      value: [1, 8]

The evaluator must implement both hosting modes and use its configured manifest. The matrix's concurrency controls how many benchmark operations Polyaxon starts together; client_concurrency is an application input controlling requests inside each job. Keeping the former at one helps prevent simultaneous benchmark jobs from competing for the same dedicated endpoint.

Use grid search for a manageable set of choices, then repeat promising configurations across representative traffic windows. Add burst patterns, larger contexts, or different batch sizes only when they answer a specific question.

Record quality and latency together

Measure request queueing, time to first token, full completion latency, accepted-task rate, failures, retries, and output length. Preserve per-request results in an artifact so aggregate percentiles can be traced back to actual cases.

Your evaluator can report its computed measurements through Polyaxon tracking. For example, use a helper like this inside the benchmark job; its arguments must come from the measured run:

from polyaxon import tracking


def record_summary(accepted_fraction, p95_seconds, estimated_cost):
    tracking.log_metrics(
        accepted_fraction=accepted_fraction,
        p95_latency_seconds=p95_seconds,
        estimated_total_cost=estimated_cost,
    )

Keep warmup policy explicit. Report model loading and startup separately from steady-state request latency, then evaluate how often startup occurs under the intended operating policy. A warm endpoint and an endpoint that starts for every batch have different capacity costs as well as different response times.

Include the capacity you pay to keep available

For an external API, record the pricing revision and the usage categories used by your estimate. For dedicated infrastructure, include the allocated interval, idle capacity between requests, storage, and relevant data transfer. Record the observation window so a ten-minute benchmark does not imply a full-day utilization level.

Polyaxon run duration and resource configuration help organize this calculation, but they do not automatically reproduce the cloud bill. Your cost model must account for the cluster's allocation and billing rules. Keep raw usage and the calculation assumptions as artifacts alongside the derived metric.

Use the comparison view to filter configurations that meet the quality and latency requirements, then compare cost among those candidates. Inspect failed and rejected requests before promoting an apparently cheaper option.

Reuse the decision workflow as traffic changes

Publish the evaluator in the component hub and rerun it when a model, runtime, quota, or traffic pattern changes. A hybrid application can benchmark its fallback route as another candidate, including any quality or data-handling differences.

When the experiment ends, stop dedicated services created only for the comparison after saving their evidence. The result is a repeatable Polyaxon workflow for choosing inference infrastructure, with the endpoint implementation, workload definition, and measured tradeoffs visible to the team.