Alternatives
Products that do what Serverless Inferencing does
AI Journey Starts Here
- 1
- 2SB
Hello HN, I’ve always loved building frontend-only apps—those you can prototype over a weekend, host for free on GitHub Pages, and scale to millions of users. Unfortunately, AI-enabled apps complicate things, as exposing your OpenAI key to the world is obviously a no-go. This also means mobile developers often have to run their own servers. That’s why I built ServerlessAI, an API gateway that lets you securely call multiple AI providers directly from client side using OpenAI-compatible APIs. You can authenticate users through any identity provider, like Google or Apple, and set per-user…
2024 · serverlessai.dev
- 3
- 4
- 5
- 6IP
The stack: two agents on separate boxes. The public one (nullclaw) is a 678 KB Zig binary using ~1 MB RAM, connected to an Ergo IRC server. Visitors talk to it via a gamja web client embedded in my site. The private one (ironclaw) handles email and scheduling, reachable only over Tailscale via Google's A2A protocol. Tiered inference: Haiku 4.5 for conversation (sub-second, cheap), Sonnet 4.6 for tool use (only when needed). Hard cap at $2/day. A2A passthrough: the private-side agent borrows the gateway's own inference pipeline, so there's one API key and one billing relationship…
Mar 2026 · georgelarson.me
- 7
- 8
General Compute▲315AI models that run on an inference cloud optimized for speed
May 2026 · generalcompute.com
- 9
- 10
- 11SM
2016 · blog.leveros.com
- 12
- 13
- 14RL
Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image. We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that. Imagine if you are a…
2023 · lepton.ai
- 15S1
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
- 16SB
2018 · github.com
- 17
- 18SC
2024 · sandboxed.ai
- 19
- 20PI
Deploying vision models is time consuming and tedious. Setting up dependencies. Fixing conflicts. Configuring TRT acceleration. Flashing (and re-flashing) NVIDIA Jetsons. A streamlined, developer-friendly solution for inference is needed. We, the Roboflow team, have been hard at work open sourcing Inference, an open source vision deployment solution. Our solution is designed with developers in mind, offering a HTTP-based interface. Run models on your hardware without having to write architecture-specific inference code. Here's a demo showing how to go from a model to GPU inference on a video…
2023 · github.com
- 21NT
Hello HackerNews! I’m excited to share what we’ve been working on at nCompass Technologies: an AI inference* platform that gives you a scalable and reliable API to access any open-source AI model — with no rate limits. We don't have rate limits as optimizations we made to our AI model serving software enable us to support a high number of concurrent requests without degrading quality of service for you as a user. If you’re thinking, well aren’t there a bunch of these already? So were we when we started nCompass. When using other APIs, we found that they weren’t reliable enough to be able to…
2024 · ncompass.tech
- 22
- 23
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →