How does pricing work?

Pricing is based on GPU resource consumption (e.g., per-second billing for dedicated deployments) and per-request for Model APIs. The platform is designed for growth with costs scaling with usage. Contact sales for detailed pricing.

Can I deploy any open-source model?

Yes, you can deploy most open-source models. For supported architectures (like standard transformers), you can use a simple config.yaml. For others, you can use a Python model class or a custom Docker container to package your serving code.

What are inference engines and which should I use?

Inference engines are specialized runtimes optimized for specific model types. Engine-Builder-LLM uses TensorRT-LLM for dense text models. BIS-LLM is for large mixture-of-experts models. BEI is for embeddings and classifiers. Baseten can auto-select one, or you can specify it in your config.

How does multi-cloud capacity management work?

Baseten's platform abstracts GPU provisioning across multiple cloud providers (AWS, GCP) and regions. It automatically provisions capacity to meet your scaling needs and availability requirements, helping avoid spot instance outages and region-specific limits.

Is there a way to connect my coding agent like Cursor?

Yes. You can install the Baseten skill and MCP servers to let your coding agent (like Cursor) directly manage your Baseten workspace, deploy models, and interact with the platform from your IDE.

What training options are available?

Two main paths: 1) Loops, a managed SDK for fine-tuning and RL on supported base models. 2) Training Jobs, which run your own framework and code in a custom container on dedicated GPU clusters. Both store checkpoints deployable to the inference stack.

Directory/Productivity/AI Infrastructure/Baseten

Baseten

Baseten is a cloud platform for deploying, managing, and scaling AI model inference and training. It provides optimized runtimes for open-source, custom, and fine-tuned models, with features like auto-scaling, multi-cloud deployment, and observability. Targeted at ML engineers, developers, and AI founders building production-grade applications, it supports various modalities including LLMs, embeddings, image generation, and text-to-speech. The platform offers Model APIs for instant access to hosted models via an OpenAI-compatible interface, dedicated inference for custom deployments on purpose-built infrastructure, and training capabilities via Loops (for fine-tuning) or custom Training Jobs. It handles containerization, GPU capacity management, scaling, and production operations like logging and metrics.

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Added on

Sep 22, 2026

Multi-Cloud DeploymentCustom DockerTensorRT-LLM EngineMoE InferenceOpenAI-Compatible APITraining & Fine-Tuning

Product information

Everything worth knowing about Baseten

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Model APIs (Hosted Models)

Call a curated set of hosted models (like GLM-5.3, DeepSeek V4, Kimi K3) via an OpenAI-compatible API for instant prototyping and evaluation without deployment.

Dedicated Inference Deployments

Deploy your own open-source, fine-tuned, or custom models on dedicated GPU infrastructure with full control over hardware, autoscaling, and release lifecycle.

Multi-Cloud & Hybrid Hosting

Scale workloads globally across any cloud provider. Options include fully-managed Baseten Cloud, single-tenant clusters, or self-hosted deployments in your own VPCs.

Optimized Inference for Key Modalities

Get out-of-the-box performance optimizations for core Gen AI workloads: rapid image generation, optimized transcription, state-of-the-art text-to-speech, and high-performance LLM runtimes.

Technical capabilities

Purpose-Built Inference Engines

Utilize specialized engines for supported architectures: Engine-Builder-LLM (TensorRT-LLM compilation), BIS-LLM (MoE models), and BEI (embeddings/reranking).

Training (Loops & Jobs)

Train models using either Loops (for fine-tuning/RL on supported models) or Training Jobs (run your own framework code on dedicated GPU clusters) and deploy checkpoints.

Production Observability & Ops

Built-in logs, metrics, request traces, autoscaling, and environment promotion (dev/staging/prod) with export options to Datadog, Prometheus, Grafana, or New Relic.

Must watch videos

59:56

Tutorial59:56

Executive Briefing on GLM-5.3

In this live webinar, Philip Kiely, Author of Inference Engineering at Baseten, and Declan Jackson, Member of Technical Staff at Artificial Analysis, break down GLM-5.3 — the new leading open-weight model — and how to decide whether it belongs in your stack. GLM-5.3 scores 45 on the Artificial Analysis Intelligence Index, ahead of every other open-weight model, while its sibling GLM-5.3 Flash sits on the cost-per-task Pareto frontier at roughly a quarter the price of closed models at the same intelligence level. Philip and Declan walk through the benchmark methodology behind those numbers, where each model is strong and weak, what it actually costs to run them, and how to use the two together in production. 0:00 Welcome & GLM-5.3 Overview 2:59 Introductions: Baseten & Artificial Analysis 4:39 The Open-Weight Model Adoption Curve 6:05 GLM-5.3 and 5.3 Flash: Release, License & Size 8:54 Two Architectures & Z.ai's Next Move 12:15 How Artificial Analysis Benchmarks the AI Stack 14:30 Intelligence Index: GLM-5.3 Leads Open Weights 15:32 What Changed in Intelligence Index v4.3 17:22 The 3-to-9 Month Gap to the Closed Frontier 19:14 Cost Per Task & the Pareto Frontier 22:43 Time Per Task: Why Your Provider Matters 25:12 Agentic Strengths: Knowledge Work & Coding 27:23 Where GLM-5.3 Is Weak: Knowledge & Vision 30:18 Inside AA Briefcase: Rubrics, Analysis & Presentation Elo 34:41 Using GLM-5.3 and Flash Together 36:01 Comparing Real Outputs: 5.2 vs 5.3 vs Flash 37:06 Why Elo Works for Subjective Quality 39:44 Cost Per Task in Practice: 87 Cents vs $6 41:46 Field Use Cases: Coding, Post-Training & Scale 44:18 Inference Engineering: Fast Mode & Grafted Vision 46:38 Audience Poll & Live Q&A Begins 49:17 Interpreting the AA Intelligence Index 51:22 What a Pareto Frontier Actually Tells You 53:17 Best Tasks for Flash vs Max 55:05 Out-There Uses: 3D, Blender & Voice Agents 56:08 Does the Harness Change Model Scores? 57:59 How to Try and Fine-Tune GLM-5.3 58:37 Closing & Where to Follow Up Try GLM-5.3 on Baseten now: https://www.baseten.co/library/glm-5-3 Explore the benchmarks on Artificial Analysis: https://artificialanalysis.ai Baseten is the fastest way to build, deploy, and scale performant AI-powered applications. Check us out here: https://www.baseten.co

2:45

Tutorial2:45

Baseten presents Notion

What if all your meetings were automatically recorded in the same place your knowledge base lives? Notion AI Meeting notes transcribes meetings and generates summaries automatically. Baseten built a custom, streaming transcription and diarization pipeline tailored to Notion’s exact workload. Try Notion now: www.Notion.com Baseten is the fastest way to build, deploy, and scale performant AI-powered applications. Check us out here: https://www.baseten.co

58:28

Tutorial58:28

What is Kimi K3? Executive briefing

In this live webinar, Joey Zwicker, Head of Forward Deployed Engineering, and Philip Kiely, Author of Inference Engineering, present a tactical briefing on everything you need to know about the latest frontier open model, Kimi K3. Kimi K3 is the most intelligent open model to date, but it is also the largest, at 2.8T parameters. Joey and Philip show how to apply proven strategies for evaluating and implementing Kimi K3 including: serving the model, benchmarking its capabilities, and evaluating per-task cost in the specific context of your workloads and applications. 0:00 Welcome & Kimi K3 Overview 2:37 Agenda & How to Evaluate Frontier Models 4:38 The Evolution of Open-Weight LLMs 8:27 Why Kimi K3 Matters 12:29 Benchmark Performance & Model Comparisons 14:29 How Enterprises Should Evaluate AI Models 18:05 Live Demo: Kimi K3 in Action 22:38 Cost, Efficiency & Choosing the Right Model 28:11 Multi-Model Routing & Production Strategies 31:44 Live Q&A Begins 33:30 Enterprise AI Use Cases Beyond Coding 37:41 KDA Attention Explained 40:06 Future Kimi Roadmap 42:20 Building Multi-Model AI Systems 43:37 Inference Performance, KV Cache & NVIDIA Dynamo 46:03 Fine-Tuning & Quantization 49:02 Common Implementation Mistakes 51:24 What Makes AI Products Successful? 53:56 Enterprise Adoption & Open Models 57:22 Final Takeaways & Closing Try Kimi K3 on Baseten now: https://www.baseten.co/library/kimi-k3 Baseten is the fastest way to build, deploy, and scale performant AI-powered applications. Check us out here: https://www.baseten.co

Who uses it

Real use cases, no hype

Production LLM Serving

Deploy fine-tuned or open-source language models (like Qwen, DeepSeek, GLM) with optimized runtimes for high throughput and low latency.

Real-time Transcription

Power speech-to-text applications with the fastest, most accurate Whisper-based inference and sub-300ms latency.

High-Volume Embeddings

Scale vector search and RAG systems with Baseten Embeddings Inference (BEI), offering 2x higher throughput than alternatives.

Custom Model Deployment

Containerize and deploy proprietary or custom models (image gen, TTS, etc.) on dedicated GPUs with full control over autoscaling.

Model Fine-tuning & RL

Use the Loops SDK for supervised fine-tuning or reinforcement learning on supported base models, then deploy checkpoints directly.

Compound AI Orchestration

Chain multiple models or steps with Baseten Chains for granular hardware control, achieving 6x better GPU utilization.

Prototyping with Model APIs

Instantly test and evaluate the latest hosted models (like Kimi K3) via an OpenAI-compatible API without deployment.

What's great

Blazing-fast cold starts & 99.99% uptime
Seamless OpenAI-compatible API for hosted models
Multi-cloud flexibility (AWS, GCP, self-hosted)
SOC 2 Type II & HIPAA compliance baked in

Technical strengths

BIS-LLM engine for optimized MoE model routing
Baseten Embeddings Inference (BEI) for 2x embedding throughput
Automatic TensorRT-LLM compilation via Engine-Builder-LLM

Where it falls short

Pricing not transparent on homepage
Primary focus is AI inference, not general compute
Self-hosted option may require significant operational overhead

Technical limitations

Model library curated, not open for arbitrary frameworks without custom Docker
Advanced performance optimizations are tied to their proprietary engine stack
Loops training SDK is limited to supported base models

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

Model APIs are pre-optimized, hosted endpoints for a curated set of models you call via API without managing infrastructure. Dedicated Inference is for deploying your own custom, fine-tuned, or open-source models on dedicated GPU clusters that you fully control.

Traffic Insights

Monthly Visits

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October 2026

Growth Rate

vs last month

Dominance

in Productivity

Top Country

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Top Countries Breakdown

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Growth Trend

Stable

Traffic has remained stable.

Pricing overview

Freemium Pricing

Baseten Pricing

Baseten uses a freemium pricing structure. Real-time rates, custom enterprise quotas, and plan details are managed directly on their official platform.

Check Pricing on Baseten

Reviews

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Data refreshed weekly. No paid placements.

Trustpilot

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Capterra

Not reviewed yet

Product Hunt

Not reviewed yet

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Everything worth knowing about Baseten

What it actually does

Who uses it

The deep-dive specs

FAQ

Traffic Insights

Baseten Pricing

What the internet actually thinks

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