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AI Infrastructure & Serving

Baseten

Baseten is cloud infrastructure for deploying and serving open-source, custom and fine-tuned AI models, with inference scaling and performance optimization for production use.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Baseten — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Baseten when an AI application needs hosted model inference, or when a team wants to prototype and evaluate models before using them in an application.

Who it is for

  • Teams building AI products that need a model inference backend.
  • Teams prototyping or evaluating models with pre-optimized runtimes.

Documented uses

  • Deploy custom, fine-tuned or open-source models for production inference.
  • Provide a scalable inference backend for an AI application.
  • Prototype and evaluate models using pre-optimized runtimes.
Before you choose

Requirements & limits

  • Baseten supports deployment in its cloud or a customer’s cloud.
  • The platform covers both production serving and model prototyping or evaluation.
  • Its role is inference infrastructure, not an end-user AI application.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Deploy and call Qwen 2.5 3B Instruct with vLLM

Deploy the documented Qwen model on an L4 using the Baseten CLI, then send a request to its OpenAI-compatible endpoint.

Before you start

  • A Baseten account.
  • The Baseten CLI installed on macOS, Linux, or Windows.
  • A project directory containing the documented config.yaml for Qwen 2.5 3B Instruct, vLLM, and an L4.
  • A personal Baseten API key to call the deployed model.

Steps

  1. Install the Baseten CLI and sign in with baseten auth login --web.

  2. Create a project directory and add the documented config.yaml for the model, vLLM server, pinned weights, L4 accelerator, and prediction settings.

  3. From the project directory, run baseten model push --tail --wait and note the model ID from the output.

  4. Create a personal API key, set it as BASETEN_API_KEY, and call the deployment’s OpenAI-compatible chat completions endpoint using the documented cURL or Python example. Match the request’s model value to --served-model-name in the configuration.

Expected outcome

The model is deployed, and a request to its endpoint can return a chat completion.

Guide limitations

  • The guide covers one specific model and L4 configuration; it does not establish compatibility with other models or hardware.
  • Browser sign-in does not set BASETEN_API_KEY; a personal API key is needed for model calls.
  • Production operation, including environment management, autoscaling, and monitoring, is a separate next stage.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
BasetenDeploy custom, fine-tuned or open-source models for production inference.Cloud
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

baseten.co ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
7 October 2026
Original website title
Inference Platform: Deploy AI models in production | Baseten · Official homepage title · retained in its source language
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