Whether your models are custom-built, open-source, or fine-tuned, Baseten provides the tools and resources to turn them into production-ready APIs. Instead of managing infrastructure, scaling policies, and performance optimization, you can focus on building and iterating on your AI-powered applications.

Baseten’s inference platform is built around four core concepts:

  • Development → Package and optimize models for deployment
  • Deployment → Serve models with robust autoscaling and resource management
  • Inference → Run real-time or batch predictions with powerful execution controls
  • Observability → Monitor performance, optimize latency, and debug issues

These work together to streamline the entire lifecycle of AI models, from packaging and deployment to execution and performance tracking.

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Development

AI development starts with turning a trained model into a deployable artifact. Baseten makes this process easy with Truss, an open-source model packaging framework. With Truss, you can define dependencies, resource requirements, and custom logic, ensuring that your model runs consistently across local environments and production deployments. Whether you’re using a Hugging Face transformer, a TensorFlow model, or a custom PyTorch implementation, Truss provides a standardized way to containerize and deploy it.

For more complex use cases you can use Chains, our SDK for compound AI systems, to orchestrate multiple models and processing steps into a single, cohesive workflow. You can combine model inference with pre-processing logic, post-processing steps, and external API calls, enabling powerful AI pipelines that go beyond simple predictions. With Truss and Chains, you have full control over how your models are structured, executed, and optimized for production use.

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Developing a model

Package and deploy any AI/ML model as an API with Truss or a Custom Server.

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Developing a Chain

Build multi-model workflows by chaining models, pre/post-processing, and business logic.

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Deployment

Once a model is packaged, it needs to be served efficiently. Deployments on Baseten provide the flexibility and performance required for real-world AI applications. Every model runs within a dedicated deployment, which manages resources, versioning, and scaling. Models can be served with autoscaling to handle spikes in traffic, and they can scale to zero when idle to minimize costs.

For production use cases, Environments provide structured model management, ensuring that updates and changes follow a controlled process. You can maintain separate environments for staging, testing, and production, each with its own scaling policies and performance optimizations. Canary deployments allow for gradual traffic shifting, so new model versions can be rolled out safely without disrupting existing users. With built-in infrastructure management, Baseten ensures that every model runs efficiently and reliably, regardless of demand.

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Inference

Serving a model isn’t just about hosting it; it’s about delivering fast, reliable predictions. Baseten’s inference engine is built to maximize performance, supporting synchronous, asynchronous, and streaming inference. For LLMs and generative AI, streamed responses provide sub-second latency, allowing tokens to be returned as they are generated. This makes AI-powered chatbots, content generation tools, and interactive applications feel instantaneous.

For workloads that require efficiency at scale, inference requests can be optimized with concurrency settings and batched execution. Asynchronous inference enables large-scale processing without blocking application threads, allowing you to queue and process thousands of requests without latency bottlenecks. Whether your application needs high-speed responses or large-scale processing, Baseten gives you full control over how inference is handled, ensuring every request is processed with minimal delay.

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Observability

Running AI models in production requires visibility into performance and reliability. Baseten provides built-in monitoring tools to track model health, execution times, and resource usage. With real-time metrics, you can analyze inference times, identify bottlenecks, and optimize performance based on actual usage patterns.

Beyond performance tracking, detailed request and response logs allow for easier debugging and observability. If a model produces unexpected results or fails under certain conditions, you can inspect exact inputs, outputs, and error states to diagnose issues quickly. For deeper insights, Baseten supports exporting metrics to external observability tools like Datadog and Prometheus. With a complete view into model execution, monitoring ensures that AI applications remain performant, cost-effective, and reliable at scale.