
LLMSafe — Zero-Trust AI Security Gateway
A zero-trust security layer between your apps and LLMs
LLMSafe — Zero-Trust AI Security Gateway launched on January 6, 2026 with 8 votes, #1,339 of 14,584 launches that month and more than 90% of that year's launches.
What it does
LLMSafe is a zero-trust security & governance gateway that sits between your applications and LLMs. Every prompt and response passes through firewall checks, normalization, policy enforcement, data protection and governance filtering — reducing risks like prompt injection, data exfiltration, unsafe outputs and compliance violations. Designed for teams that want to adopt AI with confidence, control and auditability.
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TrustMRRNov 2025 · trustmrr.com · ▲833LLM Gateway is a b2b artificial intelligence based in United States, founded in 2025. $259,539/month in verified Stripe revenue. $78,764 MRR. Ahrefs Domain Rating 52/100. growing 55% month-over-month. Track LLM Gateway's verified metrics on TrustMRR.
TrueFoundry AI GatewayDec 2025 · truefoundry.com · ▲315Many teams connecting LLMs to external tools eventually encounter the same architectural issue: as more tools and agents are added, the integration pattern becomes an N×M mesh of direct connections. Each agent implements its own auth, retries, rate limiting, and logging; each tool needs credentials distributed to multiple places and observability becomes fragmented. We built LLM gateway with this goal to provide a single place to manage authentication, authorization, routing, and observability for MCP servers, with a path toward a more general agent-gateway architecture in the future. The…

LLM Gateway2025 · llmgateway.io · ▲296I built an OpenAI-compatible LLM gateway that routes requests to OpenAI, Anthropic, Ollama, vLLM, llama-server, SGLang... anything that speaks /v1/chat/completions. Single Go binary, one YAML config file, no infrastructure. It does the things you'd expect from this kind of gateway... semantic routing via a three-layer cascade (keyword heuristics, embedding similarity, LLM classifier) that picks the best model when clients omit the model field, weighted round-robin load balancing across local inference servers with health checks and failover. The part I think is most…


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