Baseten is a production-grade AI inference platform for ML engineering teams that want to skip GPU orchestration work. Deploy any custom model using the open-source Truss framework, get auto-scaling endpoints billed per GPU minute (from $0.63/hr for T4 to $9.98/hr for B200), and run across 18+ cloud providers without vendor lock-in. SOC 2 Type II and HIPAA certified. Free tier available.
Baseten is a serverless AI inference platform founded in 2019 that lets engineering teams deploy custom ML models as auto-scaling HTTPS endpoints. GPU compute starts at $0.63/hr for T4 instances and reaches $9.98/hr for B200s. The platform reached $600M in annualized revenue by March 2026, powering inference for Cursor, Notion, Clay, and Abridge via its Truss packaging framework and multi-cloud capacity management across 18+ cloud providers.
Frequently Asked Questions
What is Baseten and what does it offer?
Baseten is a San Francisco-based AI inference platform founded in 2019 by Tuhin Srivastava, Philip Howes, Amir Haghighat, and Pankaj Gupta. The platform lets ML engineering teams deploy custom and fine-tuned machine learning models as auto-scaling HTTPS endpoints without managing GPU infrastructure directly. Engineers use Baseten's open-source Truss framework to package model code, then push to Baseten to receive a production-ready API endpoint with autoscaling, caching, and observability built in. The platform supports any Python-based model framework including PyTorch, TensorRT-LLM, vLLM, and Triton Inference Server. Baseten also provides Model APIs for popular open-source LLMs billed per token, and a Chains SDK for orchestrating multi-step compound AI pipelines. By March 2026, the company reached $600M in annualized revenue, powered by customers including Cursor, Notion, Clay, Abridge, and HeyGen.
How much does Baseten cost in 2026?
Baseten offers three pricing tiers in 2026: a free Basic plan, a Pro plan with custom pricing for reserved capacity, and an Enterprise plan for large organizations. The free Basic plan covers variable workloads with pay-as-you-go GPU compute: T4 instances start at $0.63/hr, A10G at $1.21/hr, A100 80GB at $4.00/hr, H100 at $6.50/hr, and NVIDIA B200 at $9.98/hr. Model APIs for open-source LLMs are billed per token starting at $0.14 per 1M tokens, with discounted cached input token pricing available since April 2026. The Pro plan requires contacting sales and is designed for teams needing predictable high-volume inference with reserved GPU capacity. Enterprise adds BYOC deployment, private VPCs, SSO with Okta or Azure AD, and dedicated SLAs, also priced via sales. There is no published monthly flat fee; all billing is usage-based on GPU minutes consumed.
What are the main features of Baseten?
Baseten's core features are model packaging, multi-cloud routing, a weight delivery network, compound AI orchestration, autoscaling, and compliance. The open-source Truss framework packages any ML model into a production container without requiring Docker expertise, supporting PyTorch, TensorRT-LLM, vLLM, and Triton. Multi-Cloud Capacity Management (MCM) pools GPUs across 18+ cloud providers into one elastic resource layer, automatically routing traffic to the best-available region for latency and uptime. The Baseten Delivery Network (BDN) caches model weights at three tiers (node NVMe, in-cluster peer cache, mirrored origin) to keep cold starts fast even during traffic spikes. The Chains SDK orchestrates multi-step pipelines where each component runs on its own hardware with independent autoscaling, achieving under 400ms end-to-end latency. Scale-to-zero autoscaling means teams pay only for active GPU minutes. Baseten Training, GA since October 2025, adds fine-tuning to the same platform teams use for inference.
Is Baseten free to use?
Yes, Baseten has a free Basic plan that lets teams get started with model serving on pay-as-you-go GPU compute. The free tier does not impose a hard monthly compute cap but charges at standard GPU rates ($0.63/hr for T4) whenever a model replica is active. Scale-to-zero means deployments cost nothing when not handling requests, so occasional or development workloads with scale-to-zero enabled can stay within low budgets. Baseten does not offer a time-limited free trial with a fixed credit; the free tier is simply the Basic plan with no base fee. Model APIs for popular open-source LLMs are also accessible on the free plan at per-token rates from $0.14 per 1M tokens. Teams that want reserved capacity with predictable costs will need to contact sales for the Pro plan. Overall, the free entry point is real but costs scale linearly with GPU usage.
What are the best alternatives to Baseten in 2026?
The four most direct alternatives to Baseten in 2026 are Modal, Together AI, Replicate (now under Cloudflare), and Fireworks AI. Modal is a Python-first serverless compute platform with transparent public pricing and a simpler developer experience, making it a strong choice for teams that want infrastructure-as-code without a dedicated ML-serving focus. Together AI is the best pick if your primary need is per-token access to popular open-source LLMs at competitive rates, rather than serving custom or fine-tuned models. Replicate has a catalog of 50,000+ community models accessible via a simple per-prediction API, which suits product teams that want to test models quickly without packaging. Fireworks AI offers fast token-level inference on open-source LLMs with transparent per-token pricing, similar to Together AI but with a focus on speed benchmarks. Baseten differentiates itself from all four through its multi-cloud capacity management, HIPAA and SOC 2 Type II compliance, and full model-serving lifecycle including packaging, training, and observability.
Who is Baseten best for?
Baseten is best for ML engineering teams at Series A or later AI-native startups that need to deploy custom or fine-tuned models with enterprise-grade reliability. Specific ideal users include ML engineers who are comfortable with Python and CLI tools and want to skip GPU cluster management, and platform engineering leads at companies like Cursor or Notion who need to serve high-volume inference with 99.99% uptime SLAs. Healthcare and regulated-industry teams benefit particularly from Baseten's HIPAA compliance and SOC 2 Type II certification, which many GPU infrastructure providers do not offer out of the box. Baseten is less suited to solo developers on tight budgets who want flat-rate affordable GPU access, as Pro and Enterprise pricing requires a sales conversation. It is also not the right tool for non-technical product managers who cannot use Python or a CLI to package and deploy models. Teams that only need access to pre-built open-source LLMs via a simple API are better served by Together AI or Fireworks AI.
How do you get started with Baseten?
Getting started with Baseten begins by installing the Truss CLI with pip: `pip install truss`. From there, run `truss init` in your project directory and add your model code to the generated `model.py` file, then configure dependencies and GPU requirements in `config.yaml`. You push the model to Baseten with `truss push`, which packages the code, builds a container, and creates an auto-scaling endpoint. After deployment, Baseten provides an HTTPS endpoint URL you call with a standard POST request carrying your model inputs. The dashboard at app.baseten.co shows logs, metrics, request volume, and GPU utilization per deployment. A free account requires no credit card to create; GPU usage is billed once you have an active model replica running. For compound AI workflows that chain multiple models, the Chains SDK extends Truss with a Python decorator pattern where each function maps to its own hardware and scaling policy.
How does Baseten compare to Modal in 2026?
Baseten and Modal both solve the serverless GPU inference problem but target different engineering profiles. Modal takes a code-first approach where you decorate Python functions with `@app.function(gpu='H100')` and Modal handles all provisioning, making it faster to get a one-off workload running. Baseten centers on the model-serving lifecycle: packaging with Truss, production endpoints with autoscaling and observability, and multi-cloud routing through MCM across 18+ cloud providers. Modal has a simpler public pricing page and is generally preferred for data processing and one-off batch jobs; Baseten is the choice for teams running high-volume, latency-sensitive inference that needs 99.99% uptime and HIPAA compliance. As of 2026, Modal is reportedly valued at $4.65B with around $300M in ARR, while Baseten has $600M ARR at a $5B valuation and is in talks to raise at $13B. For regulated industries like healthcare, Baseten's HIPAA certification is a decisive advantage Modal does not currently match. Pick Modal for Python-first serverless compute simplicity; pick Baseten for a dedicated ML inference platform with multi-cloud failover and enterprise compliance.
Top Alternatives
- Modal: Pick Modal if you want a code-first Python serverless platform with a simpler pricing page; pick Baseten if you need enterprise compliance (HIPAA/SOC 2), multi-cloud routing, or a dedicated ML model serving stack.
- Together AI: Pick Together AI if you only need per-token API access to popular open-source LLMs at competitive rates; pick Baseten if you need to serve custom or fine-tuned models with dedicated GPU instances.
- Replicate: Pick Replicate if you want a large catalog of community models with a simple pay-per-run API; pick Baseten if you are a production ML team deploying proprietary models that need autoscaling, compliance, and SLAs.
- Fireworks AI: Pick Fireworks AI if fast, cheap token-level inference on open-source LLMs is the main requirement; pick Baseten if you need the full model-serving stack including packaging, multi-cloud routing, and fine-tuning.