Inference engines were built for conversational AI. Same compute, same cost for every request. Agentic AI is different: always-on, background workloads, massive context sizes. OpenInfer disaggregates model execution across heterogeneous compute nodes, unlocking hardware conventional stacks cannot use. No high-end GPU dependency. A fundamentally different cost structure. OpenInfer Beta is FREE for background workloads. The inference stack built for agentic AI.
Hey Product Hunt 👋 — Behnam here, founder of OpenInfer. The inference stack was designed for chat. Every AI request gets the same treatment: same compute, same cost, regardless of what the workload actually needs. That works fine when a human is waiting on a response. It's the wrong approach entirely for a background agent running for hours with nobody watching. Here's what that means in practice: 90% of your agent workloads are latency-tolerant, routine, and always-on — but you're paying premium GPU prices for all of them. Every session. Every time. We built OpenInfer to fix this. The idea is simple: route each session to the compute it actually needs. High-SLA sessions — human in the loop, real-time — get premium hardware. Background agents, async tasks, always-on workloads get routed to lower-cost GPUs and leaner infrastructure at a fraction of the price. The routing is automatic. Your model doesn't change. Your code doesn't change. Starting today, background task inference is free. If you're running OpenClaw and getting hit by Anthropic's restrictions — we're a drop-in replacement. Zero code changes. Running on AWS to start. openinfer.io/beta We're a small team moving fast. Honest feedback is very valuable to us. Happy to answer anything in the comments. Also join our community to ask questions, request any features or give us feedback, we love to hear from you: https://discord.gg/sBQSSXue
We've been building on top of Claude for our core product and the OpenClaw restrictions forced us to pause a feature release this week. Is OpenInfer the right fit for us if we're not yet at scale — say 10–50 concurrent agents? Or is this mainly for enterprises running hundreds?
What's the data plane look like? When OpenInfer routes a session to a CPU ring topology, is the KV cache staying on the same nodes throughout the session lifetime, or does it get redistributed if topology changes mid-session?
Just connected our OpenClaw setup to the beta — took about 8 minutes, not 2, but still impressively fast for an infra change of this kind. Agents are running. Will report back on cost numbers after 24 hours.
We run about 200 concurrent background agents doing document processing overnight — exactly the latency-tolerant workload you're describing. Does the free beta have any concurrency limits, or can we actually stress-test this at scale?
CPU decode for background agents sounds good in theory but I'd want to see latency numbers. What's the P99 for a 32K context on the CPU ring topology? And what happens if a 'background' session suddenly needs to respond to a human check-in — how fast is the topology switch? Hours 2–3
What does the setup actually look like? Do I point my OpenClaw config at an OpenInfer endpoint, or is there an SDK involved? Trying to understand how 'zero code changes' works in practice.
Isn't this essentially just a smarter load balancer with model routing on top? What's the fundamental difference between what OpenInfer does and running two vLLM pools with a proxy in front?
We've been running OpenClaw agents in production for 3 months and the Anthropic rate limiting this week genuinely broke two of our workflows. Signed up for the beta — is there a Discord or Slack where we can report issues during the trial?
About OpenInfer on Product Hunt
“Keep your OpenClaw agents running. Free beta, no code change”
OpenInfer was submitted on Product Hunt and earned 20 upvotes and 19 comments, placing #58 on the daily leaderboard. Inference engines were built for conversational AI. Same compute, same cost for every request. Agentic AI is different: always-on, background workloads, massive context sizes. OpenInfer disaggregates model execution across heterogeneous compute nodes, unlocking hardware conventional stacks cannot use. No high-end GPU dependency. A fundamentally different cost structure. OpenInfer Beta is FREE for background workloads. The inference stack built for agentic AI.
OpenInfer was featured in API (98k followers), Developer Tools (511k followers) and Artificial Intelligence (466.2k followers) on Product Hunt. Together, these topics include over 161.9k products, making this a competitive space to launch in.
Who hunted OpenInfer?
OpenInfer was hunted by Behnam B. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how OpenInfer stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Behnam B
Hey Product Hunt 👋 — Behnam here, founder of OpenInfer.
The inference stack was designed for chat. Every AI request gets the same treatment: same compute, same cost, regardless of what the workload actually needs. That works fine when a human is waiting on a response. It's the wrong approach entirely for a background agent running for hours with nobody watching.
Here's what that means in practice: 90% of your agent workloads are latency-tolerant, routine, and always-on — but you're paying premium GPU prices for all of them. Every session. Every time.
We built OpenInfer to fix this. The idea is simple: route each session to the compute it actually needs. High-SLA sessions — human in the loop, real-time — get premium hardware. Background agents, async tasks, always-on workloads get routed to lower-cost GPUs and leaner infrastructure at a fraction of the price. The routing is automatic. Your model doesn't change. Your code doesn't change.
Starting today, background task inference is free.
If you're running OpenClaw and getting hit by Anthropic's restrictions — we're a drop-in replacement. Zero code changes. Running on AWS to start.
openinfer.io/beta
We're a small team moving fast. Honest feedback is very valuable to us. Happy to answer anything in the comments. Also join our community to ask questions, request any features or give us feedback, we love to hear from you: https://discord.gg/sBQSSXue