Overview
Baseten is a production-grade inference platform for deploying, scaling, and serving AI models without managing Kubernetes or GPU plumbing yourself. You package any model — a fine-tuned LLM, a vision model, a ComfyUI workflow, or a custom PyTorch/TensorFlow model — using Truss, Baseten’s open-source packaging standard, and the platform handles autoscaling, global availability, and fast cold starts with a 99.99% uptime guarantee. Its Inference Stack applies optimizations out of the box: request batching, paged attention, speculative decoding, and quantization, which the company says can cut cost-per-token versus raw cloud GPUs. Teams can start with instant Model APIs for popular open models (DeepSeek, Kimi, Qwen, GLM) and graduate to dedicated deployments for custom or proprietary weights. Baseten is SOC 2 Type II and HIPAA compliant, and supports fully managed cloud, self-hosted VPC, or hybrid setups. It is aimed at engineering and ML teams shipping latency-sensitive, mission-critical AI products.
Key Features
- Deploy any model via Truss, an open-source packaging standard for Python ML
- Autoscaling with fast cold starts and a 99.99% uptime SLA
- Out-of-the-box inference optimizations: batching, paged attention, speculative decoding
- Instant Model APIs for popular open models plus dedicated deployments for custom weights
- SOC 2 Type II and HIPAA compliant; cloud, VPC self-host, or hybrid
Pricing
| Plan | Price | For |
|---|---|---|
| Basic | $0/mo (pay-as-you-go) | Prototyping, dedicated deployments, Model APIs |
| Pro | Custom quote | Priority GPUs, higher limits, hands-on support |
| Enterprise | Custom quote | VPC self-host, custom SLA, advanced security |
Comparison
Compared to Replicate, Baseten leans harder into production autoscaling and enterprise compliance rather than a pure model marketplace. Against fal, it covers a broader model range beyond media inference. Like Groq, it targets low-latency serving, but Baseten adds custom-model deployment and VPC control.