Omniagent looks quite similar to OpenRouter (https://openrouter.ai/). - Source: Hacker News / 10 days ago

It's very easy to use other providers. See https://openrouter.ai/ which also let's you filter by where the provider is hosted and their data retention policy. - Source: Hacker News / about 2 months ago

If you want to try it yourself: grab OpenCode, point it at OpenRouter, select GLM 5.2, and give it a real task instead of a benchmark. The z.ai docs have the rest of the details. - Source: dev.to / 2 months ago

Hosted, minimal ops. You want to be calling models in five minutes and you are fine paying a small fee for it. OpenRouter is the marketplace default — 400+ models, ~5.5% on credits. Vercel AI Gateway and Cloudflare AI Gateway go further and charge 0% markup, billing you at provider list price while adding routing and caching on top. - Source: dev.to / 2 months ago

I use OpenRouter as the single door to a pile of models. Its BYOK (bring-your-own-key) feature has a trap. You add your own OpenAI key for a model, flip on "Always use for this provider," and read that as never spend OpenRouter credits. It doesn't mean that. - 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. That’s 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