Azure API Management (GenAI) Alternatives 2025: The Best Azure GenAI Gateway Replacements (and When to Switch)

Updated September 2025

Developers and platform teams love Azure API Management (APIM) because it offers a familiar API gateway with policies, observability hooks, and a mature enterprise footprint. Microsoft has also introduced “AI gateway capabilities” tailored for generative AI—think LLM-aware policies, token metrics, and templates for Azure OpenAI and other inference providers. For many organizations, that’s a solid baseline. But depending on your priorities—latency SLAs, multi-provider routing, self-hosting, cost controls, deep observability, or BYOI (Bring Your Own Infrastructure)—you may get a better fit with a different GenAI gateway or model aggregator.

This guide breaks down the top Azure API Management (GenAI) alternatives, including when to keep APIM in the stack and when to route GenAI traffic somewhere else entirely. We’ll also show you how to call a model in minutes, plus a comparison table and a long-tail FAQ (including a bunch of “Azure API Management vs X” matchups).

Table of contents

• What Azure API Management (GenAI) does well (and where it may not fit)
• How to choose an Azure GenAI gateway alternative
• Best Azure API Management (GenAI) alternatives — quick picks
• Deep dives: top alternatives
  • ShareAI (our pick for builder control + economics)
  • OpenRouter
  • Eden AI
  • Portkey
  • Kong AI Gateway
  • Orq.ai
  • Unify
  • LiteLLM
• Quickstart: call a model in minutes
• Comparison at a glance
• FAQs (long-tail “vs” matchups)

What Azure API Management (GenAI) does well (and where it may not fit)

What it does well

Microsoft has extended APIM with GenAI-specific gateway capabilities so you can manage LLM traffic similarly to REST APIs while adding LLM-aware policies and metrics. In practical terms, that means you can:

• Import Azure OpenAI or other OpenAPI specs into APIM and govern them with policies, keys, and standard API lifecycle tooling.
• Apply common auth patterns (API key, Managed Identity, OAuth 2.0) in front of Azure OpenAI or OpenAI-compatible services.
• Follow reference architectures and landing zone patterns for a GenAI gateway built on APIM.
• Keep traffic inside the Azure perimeter with familiar governance, monitoring, and a developer portal engineers already know.

Where it may not fit

Even with new GenAI policies, teams often outgrow APIM for LLM-heavy workloads in a few areas:

• Data-driven routing across many model providers. If you want to route by cost/latency/quality across dozens or hundreds of third-party models—including on-prem/self-hosted endpoints—APIM alone typically requires significant policy plumbing or extra services.
• Elasticity + burst control with BYOI first. If you need traffic to prefer your own infra (data residency, predictable latency), then spill over to a broader network on demand, you’ll want a purpose-built orchestrator.
• Deep observability for prompts/tokens beyond generic gateway logs—e.g., per-prompt cost, token usage, caching hit rates, regional performance, and fallback reason codes.
• Self-hosting an LLM-aware proxy with OpenAI-compatible endpoints and fine-grained budgets/rate limits—an OSS gateway specialized for LLMs is usually simpler.
• Multi-modality orchestration (vision, OCR, speech, translation) under one model-native surface; APIM can front these services, but some platforms offer this breadth out of the box.

How to choose an Azure GenAI gateway alternative

• Total cost of ownership (TCO). Look beyond per-token price: caching, routing policy, throttling/overage controls, and—if you can bring your own infrastructure—how much traffic can stay local (cutting egress and latency) vs. burst to a public network. Bonus: can your idle GPUs earn when you’re not using them?
• Latency & reliability. Region-aware routing, warm pools, and smart fallbacks (e.g., only retry on 429 or specific errors). Ask vendors to show p95/p99 under load and how they cold-start across providers.
• Observability & governance. Traces, prompt+token metrics, cost dashboards, PII handling, prompt policies, audit logs, and export to your SIEM. Ensure per-key and per-project budgets and rate limits.
• Self-host vs. managed. Do you need Docker/Kubernetes/Helm for a private deployment (air-gapped or VPC), or is a fully managed service acceptable?
• Breadth beyond chat. Consider image generation, OCR/document parsing, speech, translation, and RAG building blocks (reranking, embedding choices, evaluators).
• Future-proofing. Avoid lock-in: ensure you can swap providers/models quickly with OpenAI-compatible SDKs and a healthy marketplace/ecosystem.

Best Azure API Management (GenAI) alternatives — quick picks

ShareAI (our pick for builder control + economics) — One API for 150+ models, BYOI (Bring Your Own Infrastructure), per-key provider priority so your traffic hits your hardware first, then elastic spillover to a decentralized network. 70% of revenue flows back to GPU owners/providers who keep models online. When your GPUs are idle, opt in so the network can use them and earn (Exchange tokens or real money). Explore: Browse Models • Read the Docs • Try in Playground • Create API Key • Provider Guide

OpenRouter — Great one-endpoint access to many models with routing and prompt caching where supported; hosted only.

Eden AI — Multi-modal coverage (LLM, vision, OCR, speech, translation) under one API; pay-as-you-go convenience.

Portkey — AI Gateway + Observability with programmable fallbacks, rate limits, caching, and load-balancing from a single config surface.

Kong AI Gateway — Open-source gateway governance (plugins for multi-LLM integration, prompt templates, data governance, metrics/audit); self-host or use Konnect.

Orq.ai — Collaboration + LLMOps (experiments, evaluators, RAG, deployments, RBAC, VPC/on-prem options).

Unify — Data-driven router that optimizes for cost/speed/quality using live performance metrics.

LiteLLM — Open-source proxy/gateway: OpenAI-compatible endpoints, budgets/rate limits, logging/metrics, retry/fallback routing; deploy via Docker/K8s/Helm.

Deep dives: top alternatives

ShareAI (our pick for builder control + economics)

What it is. A provider-first AI network and unified API. With BYOI, organizations plug in their own infrastructure (on-prem, cloud, or edge) and set per-key provider priority—your traffic hits your devices first for privacy, residency, and predictable latency. When you need extra capacity, the ShareAI decentralized network automatically handles overflow. When your machines are idle, let the network use them and earn—either Exchange tokens (to spend later on your own inference) or real money. The marketplace is designed so 70% of revenue goes back to GPU owners/providers that keep models online.

Standout features

• BYOI + per-key provider priority. Pin requests to your infra by default; helps with privacy, data residency, and time-to-first-token.
• Elastic spillover. Burst to the decentralized network without code changes; resilient under traffic spikes.
• Earn from idle capacity. Monetize GPUs when you’re not using them; choose Exchange tokens or cash.
• Transparent marketplace. Compare models/providers by cost, availability, latency, and uptime.
• Frictionless start. Test in the Playground, create keys in the Console, see Models, and read the Docs. Ready to BYOI? Start with the Provider Guide.

Ideal for. Teams that want control + elasticity—keep sensitive or latency-critical traffic on your hardware, but tap the network when demand surges. Builders who want cost clarity (and even cost offset via idle-time earning).

Watch-outs. To get the most from ShareAI, flip provider priority on the keys that matter and opt in to idle-time earning. Your costs drop when traffic is low, and capacity rises automatically when traffic spikes.

Why ShareAI instead of APIM for GenAI? If your primary workload is GenAI, you’ll benefit from model-native routing, OpenAI-compatible ergonomics, and per-prompt observability rather than generic gateway layers. APIM remains great for REST governance—but ShareAI gives you GenAI-first orchestration with BYOI preference, which APIM doesn’t natively optimize for today. (You can still run APIM in front for perimeter control.)

Pro tip: Many teams put ShareAI behind an existing gateway for policy/logging standardization while letting ShareAI handle model routing, fallback logic, and caches.

OpenRouter

What it is. A hosted aggregator that unifies access to many models behind an OpenAI-style interface. Supports provider/model routing, fallbacks, and prompt caching where supported.

Standout features. Auto-router and provider biasing for price/throughput; simple migration if you’re already using OpenAI SDK patterns.

Ideal for. Teams that value a one-endpoint hosted experience and don’t require self-hosting.

Watch-outs. Observability is lighter vs. a full gateway, and there’s no self-hosted path.

Eden AI

What it is. A unified API for many AI services—not only chat LLMs but also image generation, OCR/document parsing, speech, and translation—with pay-as-you-go billing.

Standout features. Multi-modal coverage under one SDK/workflow; straightforward billing mapped to usage.

Ideal for. Teams whose roadmap extends beyond text and want breadth without stitching vendors.

Watch-outs. If you need fine-grained gateway policies (e.g., code-specific fallbacks or complex rate-limit strategies), a dedicated gateway might be a better fit.

Portkey

What it is. An AI operations platform with a Universal API and configurable AI Gateway. It offers observability (traces, cost/latency) and programmable fallback, load-balancing, caching, and rate-limit strategies.

Standout features. Rate-limit playbooks and virtual keys; load balancers + nested fallbacks + conditional routing; caching/queuing/retries with minimal code.

Ideal for. Product teams needing deep visibility and policy-driven routing at scale.

Watch-outs. You get the most value when you embrace the gateway config surface and monitoring stack.

Kong AI Gateway

What it is. An open-source extension of Kong Gateway that adds AI plugins for multi-LLM integration, prompt engineering/templates, data governance, content safety, and metrics/audit—with centralized governance in Kong.

Standout features. No-code AI plugins and centrally managed prompt templates; policy & metrics at the gateway layer; integrates with the broader Kong ecosystem (including Konnect).

Ideal for. Platform teams that want a self-hosted, governed entry point for AI traffic—especially if you already run Kong.

Watch-outs. It’s an infra component—expect setup/maintenance. Managed aggregators are simpler if you don’t need self-hosting.

Orq.ai

What it is. A generative AI collaboration platform spanning experiments, evaluators, RAG, deployments, and RBAC, with a unified model API and enterprise options (VPC/on-prem).

Standout features. Experiments to test prompts/models/pipelines with latency/cost tracked per run; evaluators (including RAG metrics) for quality checks and compliance.

Ideal for. Cross-functional teams building AI products where collaboration and LLMOps rigor matter.

Watch-outs. Broad surface area → more configuration vs. a minimal “single-endpoint” router.

Unify

What it is. A unified API plus a dynamic router that optimizes for quality, speed, or cost using live metrics and configurable preferences.

Standout features. Data-driven routing and fallbacks that adapt to provider performance; benchmark explorer with end-to-end results by region/workload.

Ideal for. Teams that want hands-off performance tuning backed by telemetry.

Watch-outs. Benchmark-guided routing depends on data quality; validate with your own prompts.

LiteLLM

What it is. An open-source proxy/gateway with OpenAI-compatible endpoints, budgets/rate limits, spend tracking, logging/metrics, and retry/fallback routing—deployable via Docker/K8s/Helm.

Standout features. Self-host quickly with official images; connect 100+ providers under a common API surface.

Ideal for. Teams that require full control and OpenAI-compatible ergonomics—without a proprietary layer.

Watch-outs. You’ll own operations (monitoring, upgrades, key rotation), though the admin UI/docs help.

Quickstart: call a model in minutes

Create/rotate keys in Console → API Keys: Create API Key. Then run a request:

# cURL
curl -X POST "https://api.shareai.now/v1/chat/completions" \
  -H "Authorization: Bearer $SHAREAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-70b",
    "messages": [
      { "role": "user", "content": "Summarize Azure API Management (GenAI) alternatives in one sentence." }
    ]
  }'

// JavaScript (fetch)
const res = await fetch("https://api.shareai.now/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SHAREAI_API_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "llama-3.1-70b",
    messages: [
      { role: "user", content: "Summarize Azure API Management (GenAI) alternatives in one sentence." }
    ]
  })
});

const data = await res.json();
console.log(data.choices?.[0]?.message);

Tip: Try models live in the Playground or read the API Reference.

Comparison at a glance

Platform	Hosted / Self-host	Routing & Fallbacks	Observability	Breadth (LLM + beyond)	Governance/Policy	Notes
Azure API Management (GenAI)	Hosted (Azure); self-hosted gateway option	Policy-based controls; LLM-aware policies emerging	Azure-native logs & metrics; policy insights	Fronts any backend; GenAI via Azure OpenAI/AI Foundry and OpenAI-compatible providers	Enterprise-grade Azure governance	Great for central Azure governance; less model-native routing.
ShareAI	Hosted + BYOI	Per-key provider priority (your infra first); elastic spillover to decentralized network	Usage logs; marketplace telemetry (uptime/latency per provider); model-native	Broad catalog (150+ models)	Marketplace + BYOI controls	70% revenue to GPU owners/providers; earn via Exchange tokens or cash.
OpenRouter	Hosted	Auto-router; provider/model routing; fallbacks; prompt caching	Basic request info	LLM-centric	Provider-level policies	Great one-endpoint access; not self-host.
Eden AI	Hosted	Switch providers in a unified API	Usage/cost visibility	LLM, OCR, vision, speech, translation	Central billing/key mgmt	Multi-modal + pay-as-you-go.
Portkey	Hosted & Gateway	Policy-driven fallbacks/load-balancing; caching; rate-limit playbooks	Traces/metrics	LLM-first	Gateway-level configs	Deep control + SRE-style ops.
Kong AI Gateway	Self-host/OSS (+ Konnect)	Upstream routing via plugins; cache	Metrics/audit via Kong ecosystem	LLM-first	No-code AI plugins; template governance	Ideal for platform teams & compliance.
Orq.ai	Hosted	Retries/fallbacks; versioning	Traces/dashboards; RAG evaluators	LLM + RAG + evaluators	SOC-aligned; RBAC; VPC/on-prem	Collaboration + LLMOps suite.
Unify	Hosted	Dynamic routing by cost/speed/quality	Live telemetry & benchmarks	LLM-centric	Router preferences	Real-time performance tuning.
LiteLLM	Self-host/OSS	Retry/fallback routing; budgets/limits	Logging/metrics; admin UI	LLM-centric	Full infra control	OpenAI-compatible endpoints.

FAQs (long-tail “vs” matchups)

This section targets the queries engineers actually type into search: “alternatives,” “vs,” “best gateway for genai,” “azure apim vs shareai,” and more. It also includes a few competitor-vs-competitor comparisons so readers can triangulate quickly.

What are the best Azure API Management (GenAI) alternatives?

If you want a GenAI-first stack, start with ShareAI for BYOI preference, elastic spillover, and economics (idle-time earning). If you prefer a gateway control plane, consider Portkey (AI Gateway + observability) or Kong AI Gateway (OSS + plugins + governance). For multi-modal APIs with simple billing, Eden AI is strong. LiteLLM is your lightweight, self-hosted OpenAI-compatible proxy. (You can also keep APIM for perimeter governance and put these behind it.)

Azure API Management (GenAI) vs ShareAI — which should I choose?

Choose APIM if your top priority is Azure-native governance, policy consistency with the rest of your APIs, and you mostly call Azure OpenAI or Azure AI Model Inference. Choose ShareAI if you need model-native routing, per-prompt observability, BYOI-first traffic, and elastic spillover across many providers. Many teams use both: APIM as the enterprise edge + ShareAI for GenAI routing/orchestration.

Azure API Management (GenAI) vs OpenRouter

OpenRouter provides hosted access to many models with auto-routing and prompt caching where supported—great for speedy experimentation. APIM (GenAI) is a gateway optimized for enterprise policy and Azure alignment; it can front Azure OpenAI and OpenAI-compatible backends but isn’t designed as a dedicated model router. If you’re Azure-centric and need policy control + identity integration, APIM is the safer bet. If you want hosted convenience with broad model choice, OpenRouter is appealing. If you want BYOI priority and elastic burst plus cost control, ShareAI is stronger still.

Azure API Management (GenAI) vs Portkey

Portkey shines as an AI Gateway with traces, guardrails, rate-limit playbooks, caching, and fallbacks—a strong fit when you need policy-driven reliability at the AI layer. APIM offers comprehensive API gateway features with GenAI policies, but Portkey’s surface is more model-workflow native. If you already standardize on Azure governance, APIM is simpler. If you want SRE-style control specifically for AI traffic, Portkey tends to be faster to tune.

Azure API Management (GenAI) vs Kong AI Gateway

Kong AI Gateway adds AI plugins (prompt templates, data governance, content safety) to a high-performance OSS gateway—ideal if you want self-host + plugin flexibility. APIM is a managed Azure service with strong enterprise features and new GenAI policies; less flexible if you want to build a deeply customized OSS gateway. If you’re already a Kong shop, the plugin ecosystem and Konnect services make Kong attractive; otherwise APIM integrates more cleanly with Azure landing zones.

Azure API Management (GenAI) vs Eden AI

Eden AI offers multi-modal APIs (LLM, vision, OCR, speech, translation) with pay-as-you-go pricing. APIM can front the same services but requires you to wire up multiple providers yourself; Eden AI simplifies by abstracting providers behind one SDK. If your goal is breadth with minimal wiring, Eden AI is simpler; if you need enterprise governance in Azure, APIM wins.

Azure API Management (GenAI) vs Unify

Unify focuses on dynamic routing by cost/speed/quality using live metrics. APIM can approximate routing via policies but isn’t a data-driven model router by default. If you want hands-off performance tuning, Unify is specialized; if you want Azure-native controls and consistency, APIM fits.

Azure API Management (GenAI) vs LiteLLM

LiteLLM is an OSS OpenAI-compatible proxy with budgets/rate limits, logging/metrics, and retry/fallback logic. APIM provides enterprise policy and Azure integration; LiteLLM gives you a lightweight, self-hosted LLM gateway (Docker/K8s/Helm). If you want to own the stack and keep it small, LiteLLM is great; if you need Azure SSO, networking, and policy out of the box, APIM is easier.

Can I keep APIM and still use another GenAI gateway?

Yes. A common pattern is APIM at the perimeter (identity, quotas, org governance) forwarding GenAI routes to ShareAI (or Portkey/Kong) for model-native routing. Combining architectures is straightforward with route-by-URL or product separation. This lets you standardize policy at the edge while adopting GenAI-first orchestration behind it.

Does APIM natively support OpenAI-compatible backends?

Microsoft’s GenAI capabilities are designed to work with Azure OpenAI, Azure AI Model Inference, and OpenAI-compatible models via third-party providers. You can import specs and apply policies as usual; for complex routing, pair APIM with a model-native router like ShareAI.

What’s the fastest way to try an alternative to APIM for GenAI?

If your goal is to ship a GenAI feature quickly, use ShareAI:

• Create a key in the Console.
• Run the cURL or JS snippet above.
• Flip provider priority for BYOI and test burst by throttling your infra.

You’ll get model-native routing and telemetry without re-architecting your Azure edge.

How does BYOI work in ShareAI—and why is it different from APIM?

APIM is a gateway; it can route to backends you define, including your infra. ShareAI treats your infra as a first-class provider with per-key priority, so requests default to your devices before bursting outward. That difference matters for latency (locality) and egress costs, and it enables earnings when idle (if you opt in)—which gateway products don’t typically offer.

Can I earn by sharing idle capacity with ShareAI?

Yes. Enable provider mode and opt in to incentives. Choose Exchange tokens (to spend later on your own inference) or cash payouts. The marketplace is designed so 70% of revenue flows back to GPU owners/providers who keep models online.

Which alternative is best for regulated workloads?

If you must stay inside Azure and rely on Managed Identity, Private Link, VNet, and Azure Policy, APIM is the most compliant baseline. If you need self-hosting with fine-grained control, Kong AI Gateway or LiteLLM fit. If you want model-native governance with BYOI and marketplace transparency, ShareAI is the strongest choice.

Do I lose caching or fallbacks if I move off APIM?

No. ShareAI and Portkey offer fallbacks/retries and caching strategies appropriate for LLM workloads. Kong has plugins for request/response shaping and caching. APIM remains valuable at the perimeter for quotas and identity while you gain model-centric controls downstream.

Best gateway for Azure OpenAI: APIM, ShareAI, or Portkey?

APIM offers the tightest Azure integration and enterprise governance. ShareAI gives you BYOI-first routing, richer model catalog access, and elastic spillover—great when your workload spans Azure and non-Azure models. Portkey fits when you want deep, policy-driven controls and tracing at the AI layer and are comfortable managing a dedicated AI gateway surface.

OpenRouter vs ShareAI

OpenRouter is a hosted multi-model endpoint with convenient routing and prompt caching. ShareAI adds BYOI-first traffic, elastic spillover to a decentralized network, and an earning model for idle GPUs—better for teams balancing cost, locality, and bursty workloads. Many devs prototype on OpenRouter and move production traffic to ShareAI for governance and economics.

Portkey vs ShareAI

Portkey is a configurable AI Gateway with strong observability and guardrails; it excels when you want precise control over rate limits, fallbacks, and tracing. ShareAI is a unified API and marketplace that emphasizes BYOI priority, model catalog breadth, and economics (including earning). Teams sometimes run Portkey in front of ShareAI, using Portkey for policy and ShareAI for model routing and marketplace capacity.

Kong AI Gateway vs LiteLLM

Kong AI Gateway is a full-fledged OSS gateway with AI plugins and a commercial control plane (Konnect) for governance at scale; it’s ideal for platform teams standardizing on Kong. LiteLLM is a minimal OSS proxy with OpenAI-compatible endpoints you can self-host quickly. Choose Kong for enterprise gateway uniformity and rich plugin options; choose LiteLLM for fast, lightweight self-hosting with basic budgets/limits.

Azure API Management vs API gateway alternatives (Tyk, Gravitee, Kong)

For classic REST APIs, APIM, Tyk, Gravitee, and Kong are all capable gateways. For GenAI workloads, the deciding factor is how much you need model-native features (token awareness, prompt policies, LLM observability) versus generic gateway policies. If you’re Azure-first, APIM is a safe default. If your GenAI program spans many providers and deployment targets, pair your favorite gateway with a GenAI-first orchestrator like ShareAI.

How do I migrate from APIM to ShareAI without downtime?

Introduce ShareAI behind your existing APIM routes. Start with a small product or versioned path (e.g., /v2/genai/*) that forwards to ShareAI. Shadow traffic for read-only telemetry, then gradually ramp percentage-based routing. Flip provider priority to prefer your BYOI hardware, and enable fallback and caching policies in ShareAI. Finally, deprecate the old path once SLAs are steady.

Does Azure API Management support prompt caching like some aggregators?

APIM focuses on gateway policies and can cache responses with its general mechanisms, but “prompt-aware” caching behavior varies by backend. Aggregators like OpenRouter and model-native platforms like ShareAI expose caching/fallback semantics aligned to LLM workloads. If cache hit rates impact cost, validate on representative prompts and model pairs.

Self-hosted alternative to Azure API Management (GenAI)?

LiteLLM and Kong AI Gateway are the most common self-hosted starting points. LiteLLM is the fastest to stand up with OpenAI-compatible endpoints. Kong gives you a mature OSS gateway with AI plugins and enterprise governance options via Konnect. Many teams still keep APIM or Kong at the edge and use ShareAI for model routing and marketplace capacity behind the edge.

How do costs compare: APIM vs ShareAI vs Portkey vs OpenRouter?

Costs hinge on your models, regions, request shapes, and cacheability. APIM charges by gateway units and usage; it doesn’t change provider token prices. OpenRouter reduces spend via provider/model routing and some prompt caching. Portkey helps by policy-controlling retries, fallbacks, and rate limits. ShareAI can drop total cost by keeping more traffic on your hardware (BYOI), bursting only when needed—and by letting you earn from idle GPUs to offset spend.

Azure API Management (GenAI) alternatives for multi-cloud or hybrid

Use ShareAI to normalize access across Azure, AWS, GCP, and on-prem/self-hosted endpoints while preferring your closest/owned hardware. For organizations standardizing on a gateway, run APIM, Kong, or Portkey at the edge and forward GenAI traffic to ShareAI for routing and capacity management. This keeps governance centralized but frees teams to choose best-fit models per region/workload.

Azure API Management vs Orq.ai

Orq.ai emphasizes experimentation, evaluators, RAG metrics, and collaboration features. APIM centers on gateway governance. If your team needs a shared workbench for evaluating prompts and pipelines, Orq.ai is a better fit. If you need to enforce enterprise-wide policies and quotas, APIM remains the perimeter—and you can still deploy ShareAI as the GenAI router behind it.

Does ShareAI lock me in?

No. BYOI means your infra stays yours. You control where traffic lands and when to burst to the network. ShareAI’s OpenAI-compatible surface and broad catalog reduce switching friction, and you can place your existing gateway (APIM/Portkey/Kong) in front to preserve org-wide policies.

Next step: Try a live request in the Playground, or jump straight to creating a key in the Console. Browse the full Models catalog or explore the Docs to see all options.