Baseten
Production ML infrastructure — deploy any model with custom serving, autoscaling, and GPU selection
Baseten is a production ML infrastructure platform for deploying custom and open-weight models with full control over serving configuration, GPU selection, autoscaling, and cold-start behavior. Unlike managed inference APIs, Baseten lets you bring your own model weights, custom preprocessing, and serving logic. It supports Truss (Baseten's open-source model packaging format), vLLM, TensorRT-LLM, and custom Python serving code. Ideal for teams that need more control than a managed API but less overhead than raw Kubernetes.
The production ML platform that gives you managed-API convenience with custom-serving flexibility — deploy any model with your own preprocessing logic, GPU selection, and autoscaling without managing Kubernetes.
- Full control over model serving: custom preprocessing, postprocessing, and business logic
- Bring your own weights — deploy any HuggingFace model or custom fine-tune
- GPU selection: H100, A100, A10G, T4 with per-second billing
- Autoscaling including scale-to-zero for cost efficiency
- Truss open-source packaging format for reproducible model deployments
- More setup required than managed APIs — not a drop-in OpenAI replacement
- Cold starts on scale-to-zero deployments (configurable warm pool available)
- Pricing is higher than shared inference APIs for low-volume workloads
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Full comparison tableBaseten LLM pricing overview
Baseten is an inference API provider that hosts open-weight and third-party large language models. Common use cases include Custom model deployment, Fine-tuned models, ML infrastructure, Production serving. Headquartered in San Francisco, CA, founded 2019. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Baseten vs other LLM providers
Baseten competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Use the LLM provider comparison tool to see Baseten token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Understanding Baseten token pricing
Baseten charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Baseten's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Baseten context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Baseten makes sense
Managed inference APIs like Baseten eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Baseten API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Baseten token price data is available in the LLM price history charts.
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