Cerebrium Analysis: $500K Raised
What is Cerebrium?
Cerebrium provides serverless GPU infrastructure for deploying real-time AI workloads, including voice agents, video models, and large language models. The platform emphasizes sub-second cold starts, instant autoscaling, and pay-per-second pricing, while supporting WebSocket and REST endpoints, private Docker images, and distributed storage. YC lists the company as founded in 2021, based in New York, and part of Winter 2022.
Location
251 Little Falls Dr, Wilmington, Delaware 19808, United States
Employees
11-50
Founded
2021
Revenue
$1M
Investors
- Y Combinator
- Authentic Ventures
Product Features & Capabilities
- Build, test, and deploy AI applications, serverless infrastructure, fast cold starts, low latency, 99.999% uptime, SOC 2 & HIPAA compliance, real-time logging, cost management, observability tools, effortless autoscaling, GPU variety, free credits for new users.
Pricing
- Estimated Monthly Cost:
- GPU Cost: $0.000306/s
- CPU Cost: $0.000019/s
- Memory Cost: $0.000053/s
- Total Cost: $0.000378/s
- Free Credits:
- $30 free credit (no credit card required)
- Up to $1,000.00 in free credits for companies trying out AI
How much Cerebrium raised
Pre-Seed - $500,000
January 2022Lead Investor: Y Combinator
Who are the founders of Cerebrium
ML
Michael Louis
Founder
JI
Jonathan Irwin
Co-founder & CTO
Market Focus
Cerebrium primarily focuses on the artificial intelligence (AI) industry, specifically providing a serverless infrastructure platform for building, testing, and deploying AI applications.
Business Model
Bootstrapped growth with a focus on serverless infrastructure and subscription-based revenue model.
Competitors Overview
Cerebrium operates in the serverless infrastructure market for AI applications, facing competition from several notable companies. Here are the main competitors and their distinguishing features:
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Amazon SageMaker: A fully managed service that simplifies the machine learning workflow, allowing users to build, train, and deploy models quickly. Its comprehensive toolset and integration capabilities make it a strong competitor.
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Vertex AI: Recognized as the best overall alternative, it offers a managed machine learning platform that simplifies the building, training, and deployment of ML models, providing a comprehensive ML workflow.
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Labelbox: Focused on creating and managing high-quality training data for AI models, it offers powerful image and video labeling tools, which are crucial for teams needing precise data for training machine learning models.
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Lightning AI: This platform provides a cloud-based environment for creating AI products, focusing on ease of use and rapid deployment, allowing users to concentrate on the science of AI rather than the engineering aspects.
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vishwa.ai: An AutoOps platform specializing in fine-tuning and monitoring large language models, offering a no-code interface for creating LLM workflows, making it accessible for users without extensive programming knowledge.
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LM-Kit.NET: A toolkit designed for integrating generative AI into .NET applications, emphasizing on-device inference to reduce latency and enhance security.
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Botpress: Focuses on creating AI-powered chatbots, providing tools for conversational AI development.
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Saturn Cloud: A data science platform that supports scalable Python analytics, catering to data scientists and machine learning engineers.
Each competitor has unique strengths, such as SageMaker's comprehensive ML workflow tools and Labelbox's emphasis on data management, which may appeal to different segments of the market.
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