Self-hosted LLM gateway for small teams
A Show HN post announcing Mantis, a self-hosted LLM gateway deployable to AWS. This is a product launch, not a user problem. No pain point is expressed.
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Similar Problems
surfaced semanticallyManaging Multiple LLM Gateways Without Unified Keys, Budgets, or Audit
Teams running more than one LLM gateway end up with API keys, spend limits, routing rules and audit trails scattered across each tool separately. Platform and infrastructure engineers absorb the resulting operational overhead and lose a single view of cost attribution. This surfaced as a vendor launch post rather than a user complaint, so it carries no independent evidence of demand.
Running Self-Hosted LLM Inference on Cloud Container Infrastructure Is Complex
Developers exploring self-hosted LLM inference find that running models like Gemma on Azure Container Apps requires significant configuration to handle runtime behavior, memory constraints, and scaling. The tooling ecosystem for lightweight self-hosted inference stacks lacks opinionated starter templates that reduce setup time. This gap is growing as cost and privacy concerns drive more teams toward private inference deployments.
I built Stackdome - a self-hosted alternative to platforms like Railway and Render, powered by Kubernetes
A builder announces a self-hosted, Kubernetes-based alternative to PaaS platforms like Railway and Render; no underlying user problem is described beyond the implied desire for self-hosted deployment control.
Fragmented Access Across Multiple AI Model Providers and Self-Hosted Models
This entry is a product listing for ngrok's AI Gateway, which routes requests to public AI providers, custom endpoints, and self-hosted models through one key and URL with observability, access control, and fallbacks. It is marketing copy for an existing product rather than a user-reported problem, though it implicitly points at the friction of managing fragmented AI provider access and exposing self-hosted models without a private network.
Frontier LLM API pricing and rate limits make bulk, low-stakes workloads uneconomical
Developers running high-volume, non-critical LLM workloads (bulk generation, experimentation) find frontier model API pricing and token-tracking overhead prohibitive. This structural cost/quota constraint pushes users toward flat-rate or unmetered alternatives.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.