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 semanticallyRunning 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.
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.
Cost & security control layer missing for LLM coding agents
Developers running AI coding agents (Claude Code, Cursor, Aider) lack a reliable way to cap API spend and intercept unsafe calls before they hit production LLM endpoints. Without a middleware proxy, agents in retry loops can rack up unexpected costs or exfiltrate sensitive context. The gap is between agent capability and enterprise-grade governance.
Teams need self-hosted AI agents with proper isolation and security, not shared instances
Engineering teams adopting AI assistants need each agent isolated in its own container with separate networks and secrets, but existing solutions collapse everyone into shared instances that create security and privacy risks.
Organizations cannot use cloud AI for data analysis without exposing sensitive data
Enterprises and regulated industries need AI-powered data analysis but cannot send raw sensitive data to cloud LLM providers due to compliance, privacy, or security constraints. Local-first AI processing solves this by keeping data on-device while still leveraging LLM reasoning. Demand is growing as AI adoption meets enterprise data governance requirements.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.