Baseten is an AI training and inference platform for turning open-source, custom, and fine-tuned models into production APIs. The platform handles containerization, optimized runtimes, autoscaling, observability, and GPU scheduling across multiple clouds, while also offering OpenAI-compatible Model APIs for teams that want to start serving supported models without a deployment step.
Baseten is built around high-performance inference for production GenAI workloads. Its portfolio spans dedicated inference, managed cloud deployment, self-hosted and hybrid deployment options, compound AI orchestration, model training, white-labeled gateway capabilities for AI labs, and a specialized embeddings runtime for high-throughput search, reranking, and classification workloads.
Offerings, Capabilities, and Integrations
Baseten’s core value proposition is performance and operational control. The platform supports managed model access through OpenAI-compatible endpoints, dedicated deployments for custom models, multi-node training, autoscaling across clouds and regions, and deployment choices that range from fully managed infrastructure to customer VPCs with overflow into Baseten Cloud. Baseten also emphasizes built-in security, compliance-oriented deployment controls, and production observability through logs, metrics, and request tracing.
On the integration side, Baseten supports popular AI application frameworks and developer tools including LangChain, LlamaIndex, LiteLLM, LiveKit, Vercel, Cline, HumanLayer, and Roo Code. It also supports exporting metrics to Prometheus, Datadog, Grafana, and New Relic, automating deployments through GitHub Actions, and pulling model weights or data from Hugging Face, Amazon S3, Google Cloud Storage, and Azure Blob Storage.
Products and Services
- Dedicated deployments: Single-tenant inference deployments for open-source, fine-tuned, custom, and compound AI workloads, with cross-cloud autoscaling, hands-on engineering support, observability, and sensitive-workload controls such as region locking.
- Model APIs: OpenAI-compatible endpoints that provide instant access to supported high-performance LLMs on Baseten-managed infrastructure, with features such as tool calling, structured outputs, reasoning, vision, and streaming.
- Training: Training infrastructure for single-node and multi-node jobs with checkpoint management, persistent caching, direct deployment from checkpoints, and data loading through Baseten Delivery Network and common cloud storage sources.
- Frontier Gateway: A managed, white-labeled gateway for AI labs that want to serve their own hosted models under their own brand, with API key management, authentication, usage limits, billing and metering, and branded URLs.
- Chains: A Python-based framework for orchestrating multi-model inference workflows, letting teams assign separate hardware and autoscaling settings to chain components for lower latency and better GPU utilization in compound AI systems.
- Baseten Cloud: Baseten’s fully managed deployment option for production inference across cloud providers, with multi-cloud capacity management, global scaling, active-active reliability, and support for region-locked and single-tenant deployments.
- Baseten Self-hosted: A self-hosted deployment model that runs in a customer’s VPC while preserving Baseten’s developer experience, autoscaling, and performance optimization, aimed at workloads requiring tighter control over data residency, compliance, IP, or existing cloud commitments.
- Baseten Hybrid: A hybrid deployment model that keeps sensitive inference in a customer VPC while enabling overflow to Baseten Cloud for burst capacity, SLA protection, and multi-cloud routing flexibility.
- Baseten Embeddings Inference (BEI): A specialized embeddings runtime for embedding, reranking, and classification models, designed for very high throughput and low latency with OpenAI-compatible access and support across cloud, self-hosted, and hybrid deployments.
Target Customers
Baseten targets engineering, machine learning, and infrastructure teams that are moving AI workloads from experimentation into production. Its platform is positioned for organizations deploying open-source, custom, or fine-tuned models that need predictable latency, high throughput, autoscaling, and operational visibility rather than shared API endpoints alone.
The company also aligns strongly with AI labs and enterprises that need branded model access, isolated resources, granular access control, and compliance-sensitive deployment patterns. Its customer and solution materials point to demand from healthcare, voice and transcription, image generation, search and retrieval, and other mission-critical AI product teams, including organizations with strict data residency or security requirements.
Cloud Integrations and Marketplace
- AWS Marketplace: Baseten has a verified AWS Marketplace listing for its machine learning infrastructure platform, supporting procurement through AWS Marketplace.
- Google Cloud Marketplace: Baseten states that it is available on Google Cloud Marketplace, enabling Google Cloud customers to procure and use the platform within their existing cloud environment.
- Microsoft Azure: Baseten supports Azure integration through deployment workflows that can pull model weights from Azure Blob Storage alongside Amazon S3 and Google Cloud Storage.
Key People
- Tuhin Srivastava: CEO, Co-Founder
- Dannie Herzberg: President
- Amir Haghighat: CTO, Co-Founder
- Phil Howes: Co-Founder
- Pankaj Gupta: Co-Founder
- Sameer Paranjpye: Head of Engineering
- Joey Zwicker: Head of Forward Deployed Engineering
- Philip Kiely: Head of AI Education
Key Facts
- Headquarters: San Francisco, California, United States
- Employees: 243
- Annual Revenue: Undisclosed
- Parent Company: None
- Subsidiaries: None
- Publicly Listed: Private
