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It takes a little sweat, but you can wire Claude Code into n8n by running Claude through Docker, SSH, or local command execution from inside a workflow. That means n8n can route a request to Claude as one of its LLM backends. See: https://n8n.io/. - Source: Hacker News / 18 days ago
n8n โ open source, self-hostable. The hosted cloud tier exists if you'd rather not run it, but self-hosting is the whole point here. - Source: dev.to / about 2 months ago
You'll need an n8n instance (cloud or self-hosted), an FFmpeg Micro API key from ffmpeg-micro.com, and a cloud storage folder your team already uses for video files. - Source: dev.to / 2 months ago
n8n and similar visual workflow tools were on the list for one specific reason: leadership likes seeing the boxes-and-arrows. But the LLM nodes aren't first-class โ you'd be wrapping every model call in HTTP, and the graph is in a database, not in code that's reviewable in a PR. Auditability and reproducibility are both worse than the LangGraph + bus path. (n8n is a great fit for non-LLM SOAR-style automations,... - Source: dev.to / 2 months ago
Sign up at n8n.io (free cloud version is perfect to learn). - Source: dev.to / 2 months ago
Setup time matters too. The delta between Runpod and bare-metal providers like Lambda Labs is large. Reaching an equivalent setup on a bare VM requires provisioning the instance, configuring the OS and CUDA drivers, installing Docker, setting up your orchestration layer (Kubernetes or Slurm), deploying your inference container, configuring autoscaling rules, and wiring up your load balancer. Thatโs a realistic... - Source: dev.to / 5 months ago
Let's do the math for a representative setup: GPT-OSS-120B via Together.ai ($0.15/$0.60) vs self-hosting on H100s from Lambda Labs at $2.99/hr ($2,183/mo). A single H100 running a 70B model produces roughly 50 tokens/second on average, which works out to about 130M tokens per month. - Source: dev.to / 6 months ago
How does this compare to https://lambdalabs.com/. - Source: Hacker News / about 3 years ago
Another option is to pay for AWS server with a beefy GPU and enough RAM. It's not too cheap, but isn't expensive either if you aren't planning to run it 24/7. Or get a GPU cluster from a company that offers stuff for ML specifically, it might be easier to set up compared to AWS and in some cases cheaper. Like, for example, lambdalabs that offers H100 gpu for 2 bucks per hour. Source: about 3 years ago
I used some of the cloud GPUs on Vast.ai, but I also tried Lambda Labs, and these days I have my own docker container setup which can be deployed to a VM on Google Cloud and used more programatically. Source: over 3 years ago
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Mattermost - Mattermost is an open source alternative to Slack.
Make.com - Tool for workflow automation (Former Integromat)
Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.
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ipinfo.io - Simple IP address information.
