Is SBproxy a reverse proxy, an AI gateway, or a model server?

All three, from one binary. It terminates TLS and handles HTTP/1.1, HTTP/2, WebSocket, and gRPC like nginx or Envoy. It routes LLM traffic to 200+ hosted models across 72 providers with guardrails, budgets, and MCP federation. And it serves open-weight models on your own GPUs by supervising vLLM or llama.cpp underneath. One config file drives all of it, and the AI features sit on a runtime that was already a production-grade proxy.

SBproxy is the open-source enterprise gateway for API, MCP, and AI model traffic. Outbound, it routes to 200+ hosted models across 72 providers through one OpenAI-compatible endpoint, picking models by prompt difficulty and rerouting on live cost-per-success, and serves open-weight models on your own GPUs. Inbound, it gates the crawlers, signed agents, and MCP clients that hit your APIs, with controls like Pay Per Crawl and Web Bot Auth. Both directions share one policy of virtual keys, budgets, and guardrails. Underneath sits a production-grade reverse proxy running auth, TLS, WAF, rate limiting, transforms, and caching. Single static binary, Apache License 2.0.

Can SBproxy serve models on my own GPUs?

Yes, on any GPU. A provider whose entire body is a serve: block boots to a first token: SBproxy resolves the weights, fits an engine and quantization to your card (bigger cards take FP8, smaller ones fall back to an int4 GGUF), runs vLLM or llama.cpp as a supervised subprocess, and routes to it. Run sbproxy doctor first and it reports what the host can serve. Every governance plane, meaning virtual keys, budgets, guardrails, and the usage ledger, applies to the local model unchanged, and a hosted provider placed after the local lane catches overflow under policy.

How is SBproxy different from vLLM or Ollama?

vLLM and Ollama serve a model. SBproxy stands up the environment around it: the governed OpenAI-compatible endpoint, virtual keys, budgets, guardrails on prompt and response, failover to hosted providers, and a verifiable usage ledger. It runs vLLM or llama.cpp underneath as supervised engines, so you keep the engine and gain the gateway.

How is SBproxy different from Cloudflare Pay Per Crawl?

Cloudflare runs at its own edge. SBproxy runs in your VPC, region, Kubernetes cluster, or air-gapped environment. Pricing is tier-aware, per route, per shape, and per agent rather than domain-wide. Inbound monetization, agent identity, content negotiation, and outbound AI governance live on one policy graph instead of separate Cloudflare products. Settlement supports both x402 and Stripe MPP, and you can act as your own Merchant of Record.

How is SBproxy different from other AI gateways like LiteLLM, Helicone, Portkey, or Kong AI Gateway?

Most AI gateways stop at routing calls to hosted providers. SBproxy also serves the weights: the same binary runs open models on your GPUs and applies the same keys, budgets, and guardrails to local and hosted traffic alike. It is also a real reverse proxy underneath, so auth, WAF, and rate limiting ride along instead of needing a second gateway. Helicone is excellent for LLM observability. LiteLLM is a Python library that grew a server, and sbproxy config import-litellm converts its config in an afternoon. Kong is a traditional API gateway with AI bolt-ons. None of them serve the model.

Where does SBproxy sit in my stack?

At the network hop between your apps or agents and the models they call, and between inbound AI traffic and your APIs. SDK gateways live inside one app's code, hosted edges govern from someone else's network, and agent platforms ask you to build on them; SBproxy is standalone infrastructure that deploys like nginx, works under any agent framework or platform, and fronts any hosted provider or your own GPUs. It terminates TLS with certificates it issues and renews from Let's Encrypt, so the governed endpoint can face the public internet behind virtual keys you mint and revoke at runtime.

How does SBproxy handle API keys and state across a fleet of instances?

SBproxy ships a dynamic key plane. Mint, rotate, and revoke virtual keys at runtime through the admin API with no reload, and a revoke takes effect on the next request. Keys are hashed at rest with HMAC-SHA256 and a server pepper, and upstream provider credentials are sealed with AES-256-GCM. Across a fleet of replicas, a built-in mesh keeps the keys, budgets, and per-key rate counters coherent using SWIM gossip and CRDT counters, so the cluster coordinates itself without an external Redis. LiteLLM and most AI gateways need a separate Redis to share that state across instances.

Where does SBproxy deploy?

Single binary on a server, Kubernetes operator on a fleet, or air-gapped install on a regulated network. Same policy graph in every shape. No phone-home. No SaaS edge. Bring your own Stripe, your own SSO, your own observability, and your own audit sink. Suitable for EU, India, Saudi Arabia, US public sector, defense, healthcare, and finance workloads with sovereignty requirements.

Is SBproxy open source?

SBproxy is open source under the Apache License 2.0. Free for any use, including production and commercial, with no field-of-use restriction. Read every line on GitHub. Verify every binary.

What is Pay Per Crawl and how does SBproxy support it?

Pay Per Crawl is the pattern of charging AI crawlers for access to content using HTTP 402 Payment Required. SBproxy ships the ai_crawl_control policy: AI crawlers that arrive without a valid payment token receive a 402 response with a JSON challenge. The crawler retries with the issued token and the proxy validates it through a pluggable ledger. Settlement supports x402 stablecoin and Stripe MPP. Tokens can be priced per route, per shape, and per agent.

What is Web Bot Auth and how does SBproxy verify AI agents?

Web Bot Auth is the IETF pattern for verifying signed AI crawlers and agents. SBproxy implements it through the bot_auth provider. Agents sign each request with an Ed25519 key under RFC 9421 HTTP Message Signatures and advertise their key id in the Signature-Input header. The gateway looks up the matching public key in its directory and verifies the signature. Agents that pass come through. Everything else gets 401.

Does SBproxy support MCP?

Yes. SBproxy ships an MCP gateway that federates internal tools under one identity model. Authentication supports OAuth2 with DPoP, PKCE, and DCR. Federated servers can be wrapped from any OpenAPI service, with per-server prefix and RBAC. The same policy graph that governs API and AI traffic also governs MCP tool invocations.

What does SBproxy replace?

A hosted model router (OpenRouter), an LLM proxy (LiteLLM, Portkey, Helicone), the hand-rolled shim in front of vLLM or Ollama, a traditional API gateway (Kong, nginx, Envoy), a guardrails service, a bot management product, and a payment rail for crawlers (TollBit, Skyfire). One runtime instead of six vendors stitched together, one policy plane instead of six control planes, one audit log instead of six disconnected feeds.

How many AI providers does SBproxy support?

SBproxy reaches 200+ models behind one OpenAI-compatible API through 72 native providers, including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Azure AI Foundry, Groq, Mistral, Cohere, Together AI, Fireworks, xAI Grok, OpenRouter, Cerebras, NVIDIA NIM, Databricks, Snowflake Cortex, IBM watsonx, and Oracle GenAI, plus the models it serves itself on your GPUs via vLLM or llama.cpp and external self-hosted runtimes like Ollama and TGI. Routing supports fallback chains, weighted, round-robin, latency-based, cost-optimized, and outcome-aware strategies.

Is SBproxy safe to run in front of production traffic?

SBproxy ships as a single static Rust binary. Releases are cut from tagged commits in a public repository on GitHub. Dependencies are pinned and checksummed in Cargo.lock. The runtime does not fetch code from a public package registry when it starts. The deployment shape matches nginx, HAProxy, and Envoy. Hot reload is supported without dropping connections.

Guardrail Mesh

Collect every verdict, fuse on a quorum, redact-and-continue, with a verdict cache

Overview

The serial guardrail chain blocks on the first detector that flags. The mesh runs the input detectors together, collects every verdict, and fuses them into one decision under a rule you set. It can block on a quorum rather than any single flag, mask a flagged prompt and let it through, run the cheap detectors first under a latency budget, and cache verdicts so a repeated prompt skips the work. It is off by default; with no mesh block the serial block-on-any behavior is unchanged.

Configuration

guardrails:
  input:
    - type: injection
    - type: pii
      patterns: [email]
    - type: regex_guard
      action: block
      config:
        deny: [forbidden-term]
  mesh:
    block_threshold: 2     # block only when >= 2 detectors flag (1 = block-on-any)
    redact_on_flag: true   # below the threshold, mask the prompt and continue
    cache: true            # reuse a verdict for a repeated prompt
    latency_budget_ms: 50  # stop launching expensive detectors past the budget

Fusion

block_threshold is the quorum: the request is blocked when flagged_count >= block_threshold. 1 reproduces the serial behavior; 0 never blocks on the count. With redact_on_flag, a request flagged below the threshold is masked by the PII redactor and continues. The full label set is published to the policy plane's ai.guardrails.* namespace, so a CEL rule can fuse the verdicts further.

Cascade and cache

Detectors run cheap-first. With latency_budget_ms set, expensive classifiers are skipped once the budget is spent, so the mesh degrades gracefully under load. With cache on, a verdict is cached by a combined hash of the prompt and the guardrail set, so a repeated prompt skips re-running the detectors.

← PreviousOutcome-Aware Routing Next →AI Policy Plane