Serve and scale open-source and custom AI models on the fastest, most reliable inference platform.
Baseten is praised for its efficient AI integration and user-friendly interface, which simplifies deployment for developers. While there are limited detailed complaints available, the repetition of its name in social media might suggest a lack of diverse conversation or content depth about new features or updates. There is minimal discussion about pricing, indicating either neutral sentiment or a less significant emphasis compared to its functionalities. Overall, Baseten seems to maintain a positive reputation, particularly among developers seeking streamlined AI solutions.
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Platforms
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GitHub Stars
1,131
96 forks
Baseten is praised for its efficient AI integration and user-friendly interface, which simplifies deployment for developers. While there are limited detailed complaints available, the repetition of its name in social media might suggest a lack of diverse conversation or content depth about new features or updates. There is minimal discussion about pricing, indicating either neutral sentiment or a less significant emphasis compared to its functionalities. Overall, Baseten seems to maintain a positive reputation, particularly among developers seeking streamlined AI solutions.
Features
Use Cases
Industry
information technology & services
Employees
180
Funding Stage
Venture (Round not Specified)
Total Funding
$585.0M
283
GitHub followers
89
GitHub repos
1,131
GitHub stars
18
npm packages
Pricing found: $0, $1.74, $0.145, $3.48, $0.50
Open-source single-GPU reproductions of Cartridges and STILL for neural KV-cache compaction [P]
I implemented two recent ideas for long-context inference / KV-cache compaction and open-sourced both reproductions: Cartridges: https://github.com/shreyansh26/cartridges STILL: https://github.com/shreyansh26/STILL-Towards-Infinite-Context-Windows The goal was to make the ideas easy to inspect and run, with benchmark code and readable implementations instead of just paper/blog summaries. Broadly: cartridges reproduces corpus-specific compressed KV caches STILL reproduces reusable neural KV-cache compaction the STILL repo also compares against full-context inference, truncation, and cartridges Here are the original papers / blogs - cartridges - https://arxiv.org/abs/2506.06266 STILL - https://www.baseten.co/research/towards-infinite-context-windows-neural-kv-cache-compaction/ Would be useful if you’re interested in long-context inference, memory compression, or practical systems tradeoffs around KV-cache reuse. submitted by /u/shreyansh26 [link] [comments]
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Deep analysis of basetenlabs/truss — architecture, costs, security, dependencies & more
Yes, Baseten offers a free tier. Pricing found: $0, $1.74, $0.145, $3.48, $0.50
Key features include: Rapid image generation, Optimized transcription, SOTA text-to-speech, Performant LLM runtimes, The fastest embeddings, Ultra-low-latency compound AI.
Baseten is commonly used for: Real-time image generation for e-commerce platforms, Automated transcription services for podcasts and webinars, High-quality text-to-speech for accessibility applications, Large language model (LLM) deployment for customer support chatbots, Embedding generation for recommendation systems, Ultra-low-latency AI for financial trading algorithms.
Baseten integrates with: AWS S3 for data storage, Google Cloud Platform for scalable computing, Microsoft Azure for enterprise applications, Slack for team collaboration, Zapier for workflow automation, Jupyter Notebooks for data science projects, Tableau for data visualization, GitHub for version control and collaboration, Salesforce for CRM integration, Twilio for communication services.
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Apr 1, 2026
Baseten has a public GitHub repository with 1,131 stars.
