All posts tagged

Learn how platform teams govern AI APIs, models, providers, agents, and MCP tools through ownership, policy, runtime controls, evidence, and review.

Design an AI gateway that safely serves accepted policy through control-plane outages, restarts, stale state, shared-service failures, and recovery drills.

Use stable model aliases, contract tests, canary traffic, and rollback gates to migrate LLM providers without coupling applications to constant model churn.

AISIX 1.5.0 extends Model Group routing, retry, failover, balancing, and per-attempt usage records to supported single-request AI endpoints.

Learn how AI gateway rate limiting controls requests, tokens, concurrency, models, providers, users, teams, streaming workloads, retries, and AI spending.

Observe AI gateway traffic across models, providers, tokens, streaming latency, cost, retries, agents, and MCP tools while protecting sensitive data.

Compare MCP gateways, MCP proxies, AI gateways, and API gateways by protocol role, traffic, security, routing, compatibility, and operations.

Connect Dify, n8n, and Open WebUI to AISIX with stable model aliases, protected provider keys, policy, usage attribution, and workload tests.

AISIX 1.4.0 can serve in degraded mode when Redis is unavailable at startup and can retry usage delivery within strict limits when deduplication evidence permits.

Test an OpenAI-compatible AI gateway across auth, model discovery, streaming, tool calls, errors, usage, and provider translation.

AISIX 1.3.0 expands structured output and Responses translation, with explicit guardrail boundaries and clearer records of completed agent requests.

Learn how a private LLM gateway secures vLLM, SGLang, and Ollama with stable model aliases, traffic policy, routing, and observability.

Learn how an MCP Registry catalogs servers, differs from a marketplace and gateway, and supports governed MCP discovery and tool execution.

Learn how an AI Gateway governs Files, Batch, and Fine-tuning APIs with secure routing, credential isolation, job lifecycle control, and usage attribution.

Learn how a multimodal AI Gateway governs image, audio, video, and realtime APIs across identity, policy, provider routing, cost, and telemetry.

Learn how speculative decoding changes LLM throughput, latency, GPU memory, routing, and the metrics an AI Gateway should expose.

AISIX 1.0.0 adds semantic guardrail testing and decision scores, with clearer failure policies and controls for production AI traffic.

Learn how AI Gateway audit logging connects requests, policy outcomes, configuration changes, retention, and exports for AI governance and investigations.

Learn how AI Gateway PII redaction detects, masks, or blocks sensitive data in LLM requests and responses before it crosses a trust boundary.

Learn how A2A Protocol 1.0 uses Agent Cards, HTTP authentication, tenant routing, and signatures, and where a gateway enforces deployment policy.

Use AISIX AI Gateway guardrails safely with monitor and block modes, input and output hooks, scoped policies, streaming controls, and failure handling.

Rotate LLM provider credentials in AISIX Cloud or open-source AISIX while keeping caller API keys and model aliases stable, tested, and recoverable.

Learn how to protect vLLM and other inference servers with gateway controls, patching, isolation, network policy, and explicit limits on the gateway security boundary.

AISIX AI Gateway now serves the stateless MCP 2026-07-28 revision alongside every 2025 revision on one endpoint, with per-server protocol pinning and a one-call-one-execution guarantee.

Use this enterprise AI gateway checklist to evaluate provider abstraction, security, budgets, observability, deployment models, and production readiness.

Scale least-privilege MCP access with caller API keys, environment and team policies, approval workflows, and deny-first controls in AISIX AI Gateway.

Learn how AISIX turns OpenAPI 3.x REST APIs into MCP tools while protecting credentials and applying access controls, guardrails, and telemetry in production.

See how an AI gateway extends enterprise API management with model aliases, provider key control, budgets, telemetry, guardrails, and AI API governance.

Learn how platform teams use an AI gateway to offer self-service LLM access with model aliases, provider key control, budgets, observability, and guardrails.

The July 2026 Hugging Face and OpenAI incident showed why defender workflows need self-hosted fallback, auditable egress, and AI gateway policy tiers.

Managed OAuth helps agents authenticate, but production AI systems still need gateway-side authorization, policy, and audit for tools, MCP servers, and APIs.

Learn how an AI Gateway turns token usage into cost allocation, layered budgets, quotas, showback, and chargeback across teams, models, and providers.

Recent routing and cost signals show why AI platforms need gateway-side visibility into effective cost, caching, latency, and policy instead of relying on model sticker prices alone.

Design a multi-cloud AI Gateway for AWS, Azure, Google Cloud, SaaS providers, and private models with stable aliases, regional policy, and unified telemetry.

API7 Gateway 3.10.4 strengthens AI, identity, network, and configuration boundaries so untrusted context does not silently cross the gateway.

Learn how AISIX uses embeddings, examples, thresholds, and failure policies to route prompts by intent while controlling model cost and risk.

Use the OpenAI Responses API through AISIX AI Gateway, understand direct and bridged provider paths, and test the features that are not portable.

Learn how API7 Gateway 3.10.3 makes semantic AI caching practical with streaming reuse, isolation controls, moderation, and observability.

AI agents are getting real API access, cloud permissions, and tool credentials. Learn why gateway-level identity, short-lived credentials, and audit logs matter.

Learn how an AI Gateway secures LLM traffic with access control, API key management, guardrails, sensitive data protection, and audit logs.

Learn how AI Gateway prompt management supports templates, versioning, guardrails, rollout control, audit logs, and safer LLM operations.

Learn how AI Gateway load balancing routes traffic across models, providers, regions, quotas, and fallback paths for reliable enterprise LLM applications.

Configure AISIX metrics, access logs, response headers, and usage events to investigate AI requests, tokens, latency, routing, and cost.

Plan and operate an AI gateway for production across deployment, request lifecycle, routing, security, observability, cost controls, and governance.

Learn why local-first and shared AI agent memory needs API governance for identity, consent, retention, observability, and secure tool access.

Configure and verify AISIX request, token, and concurrency limits across callers, teams, models, providers, and AISIX Cloud budgets.

Compare open source AI Gateway options and learn how to evaluate AI traffic control, security, cost governance, and enterprise readiness.

API7 Gateway 3.10.2 adds AI caching, prompt protection, richer observability, reliable rate limits, and stronger Developer Portal controls.

Learn how runtime gateways, management control planes, developer portals, and AI gateways work together as an enterprise API delivery platform.

New browser-based AI agent projects show why teams need API boundaries, egress controls, audit logs, and gateway policies for agentic workflows.

Compare LLM Gateway, AI Gateway, and API Gateway architectures, use cases, and enterprise requirements for governing AI traffic.

Learn how MCP Gateway architecture secures AI agent tool calls with identity, policy, rate limits, audit logs, and observability.

Envoy AI Gateway 1.0 and new LLM gateway projects show why production AI traffic needs policy, observability, failover, and cost control.

Learn why autonomous AI agents need API Gateway policies for traffic, cost control, and operational safety.

Learn why AI agents need an AI gateway for model routing, tool calls, rate limits, access control, observability, and enterprise governance.

AISIX model ensembles fan one chat request to a panel of LLMs, then a judge synthesizes one answer — so several low-cost models can rival a frontier model.

Explore an enterprise AI gateway reference architecture, request flow, governance controls, and use cases for LLM and agent traffic.

API7 Gateway 3.10.1 advances AI Gateway operations with LLM observability, reliable traffic controls, API governance, and Developer Portal upgrades.

Claude Fable 5 shows why enterprises need gateway-level control for AI traffic, safety, cost, and observability.

Learn how an AI Gateway secures agent workflows with routing, rate limits, auth, and observability.

API7 Enterprise 3.10.0 simplifies service management, strengthens security defaults, and improves AI traffic reliability for platform teams.

Learn how intelligent caching in AI Gateways reduces API costs by 75% while maintaining performance, inspired by DeepSeek's approach.

Discover how AI Gateways enable secure local AI deployment while maintaining control, privacy, and performance at scale.

Learn what an AI gateway is, how it routes, secures, and observes LLM traffic, and how common architectures connect AI applications with model providers.

Learn why API7 built AISIX from scratch as an AI-native gateway for LLM workloads, with unified model access, observability, and fine-grained traffic control.

Learn how APISIX and API7 API gateways can help scale massive AI models from local development to production environments.

Learn how to manage diverse LLM ecosystems, optimize costs, and ensure high availability by load balancing multiple LLM backends with Apache APISIX.

Learn how an AI Gateway with multi-LLM routing optimizes cost, latency, and reliability across cloud, local, and edge models.

AI-generated code introduces critical security risks. Learn how an AI Gateway with prompt inspection and code analysis blocks vulnerabilities before they reach production.

Unify AI agent management with a control plane. Learn how an AI Gateway provides routing, cost tracking, and security for scalable LLM operations.

Design multi-LLM routing across Qwen and cloud models. Compare routing policies, fallback, observability, cost controls, and AI gateway boundaries.

Learn why Qwen3-Coder-Next and other coding agents benefit from an AI Gateway for routing, fallback, token controls, cost visibility, and hybrid model operations.

Discover how Agent Skills and Claude Code are transforming AI agents, and why AI Gateways are essential for managing costs, security, and multi-LLM workloads.

Design AI gateway policies for offline batch, online inference, and agentic LLM workloads across routing, rate limits, failover, observability, and cost.

Desktop AI agents create an API call explosion that requires an AI Gateway to manage cost, security, and reliability.

Learn why traditional defenses fail and how a multi-layered API Gateway strategy with Apache APISIX protects your service and costs.

Learn how an AI Gateway enforces security boundaries, prevents prompt injection, and ensures auditability for your agents.

Learn how an API Gateway provides the sustainable, compliant, and cost-controlled infrastructure enterprise AI needs.

Learn how prompt injection works and why an AI gateway like Apache APISIX is now mandatory for securing production AI applications.

AI Gateways are evolving from simple proxies into intelligent orchestrators that manage agent swarms, enable carbon-aware routing, and provide predictive cost control for enterprise AI.

Learn how AI gateway compliance controls support PII handling, guardrail evidence, auditability, data minimization, and policy verification for LLM traffic.

Global appliance leader uses APISIX AI Gateway for unified LLM traffic control, hybrid cloud routing, and multi-tenant isolation.