GitLab AI Gateway Alternatives 2025 — Top 10

Updated November 2025

If you’re evaluating GitLab AI Gateway alternatives, this guide maps the landscape like a builder would. First, we clarify what GitLab’s AI Gateway lane is—egress governance (centralized credentials/policies), an LLM-aware control layer, and observability—then we compare the 10 best alternatives. We place ShareAI first for teams that want one API across many providers, a transparent marketplace with price / latency / uptime / availability before routing, instant failover, and people-powered economics (70% of every dollar flows back to providers—community or company).

What GitLab AI Gateway is (and isn’t)

What it is. A governance-first layer focused on routing AI traffic with policies, key management, and observability—so application teams can control LLM usage with the same discipline they bring to any production API.

What it isn’t. A neutral marketplace that helps you choose providers/models based on real-time price, latency, uptime, and availability or automatically fail over across multiple providers. Gateways standardize control; aggregators optimize choice and resilience.

Aggregators vs Gateways vs Agent platforms (quick primer)

• LLM aggregators. One API across many models/providers with pre-route transparency (price, latency, uptime, availability, provider type), plus smart routing/failover.
• AI gateways. Policy/governance at the edge (credentials, rate limits, guardrails), plus observability. You bring your providers. GitLab AI Gateway lives here.
• Agent/chatbot platforms. Packaged UX, memory/tools, channels—great for end-user assistants, not provider-agnostic routing.

How we evaluated the best GitLab AI Gateway alternatives

• Model breadth & neutrality. Proprietary + open; switch providers with no rewrites.
• Latency & resilience. Routing policies, timeouts, retries, instant failover.
• Governance & security. Key handling, scopes, regional routing, guardrails.
• Observability. Logs/traces and cost/latency dashboards.
• Pricing transparency & TCO. Compare real costs before you route.
• Developer experience. Docs, SDKs, quickstarts; time-to-first-token.
• Community & economics. Whether your spend grows supply (incentives for GPU owners; fair revenue share).

Top 10 GitLab AI Gateway alternatives

#1 — ShareAI (People-Powered AI API)

What it is. A multi-provider API with a transparent marketplace and smart routing. With one integration, browse a large catalog of models and providers, compare price, latency, uptime, availability, provider type, and route with instant failover. Economics are people-powered: 70% of every dollar flows to providers (community or company) who keep models online.

Why it’s #1 here. If you want provider-agnostic aggregation with pre-route transparency and resilience, ShareAI is the most direct fit. Keep a gateway if you need org-wide policies; add ShareAI for marketplace-guided routing and “always-on” availability across providers.

• One API → 150+ models across many providers; no rewrites, no lock-in.
• Transparent marketplace: choose by price, latency, uptime, availability, provider type.
• Resilience by default: routing policies + instant failover.
• Fair economics: 70% of spend goes to providers, returning value to the community.

Quick links — Browse Models · Open Playground · Create API Key · API Reference · User Guide · Releases

For providers: earn by keeping models online
Anyone can become a ShareAI provider—Community or Company. Onboard via Windows, Ubuntu, macOS, or Docker. Contribute idle-time bursts or run always-on. Choose your incentive: Rewards (money), Exchange (tokens / AI Prosumer), or Mission (donate a % to NGOs). As you scale, set your own inference prices and gain preferential exposure. Provider Guide · Sign in / Sign up

#2 — Kong AI Gateway

Enterprise AI/LLM gateway—strong policies/plugins, analytics, and observability for AI traffic. It’s a control plane rather than a marketplace.

#3 — Portkey

AI gateway emphasizing observability, guardrails, and governance—popular where compliance is strict.

#4 — OpenRouter

Unified API over many models; excellent for fast experimentation across a wide catalog.

#5 — Eden AI

Aggregates LLMs plus broader AI (vision, translation, TTS), with fallbacks/caching and batching.

#6 — LiteLLM

Lightweight SDK + self-hostable proxy exposing an OpenAI-compatible interface to many providers.

#7 — Unify

Quality-oriented routing and evaluation to pick better models per prompt.

#8 — Orq AI

Orchestration/collaboration platform to move experiments → production with low-code flows.

#9 — Apigee (with LLMs behind it)

Mature API management/gateway you can place in front of LLM providers to apply policies, keys, and quotas.

#10 — NGINX

DIY path: build custom routing, token enforcement, and caching for LLM backends if you prefer tight control.

GitLab AI Gateway vs ShareAI (tl;dr):
Need one API over many providers with marketplace transparency and instant failover? Choose ShareAI.
Need egress governance—centralized credentials, policy, observability—and you already picked your providers? GitLab AI Gateway fits that lane. Many teams pair them: gateway for org policy + ShareAI for marketplace-guided routing.

Quick comparison (at a glance)

Platform	Who it serves	Model breadth	Governance & security	Observability	Routing / failover	Marketplace transparency	Provider program
ShareAI	Product/platform teams needing one API + fair economics	150+ models, many providers	API keys & per-route controls	Console usage + marketplace stats	Smart routing + instant failover	Yes (price, latency, uptime, availability, provider type)	Yes — open supply; 70% to providers
GitLab AI Gateway	Teams wanting egress governance	BYO providers	Centralized credentials/policies	Metrics/tracing	Conditional routing via policies	No (infra tool, not a marketplace)	n/a
Kong AI Gateway	Enterprises needing gateway-level policy	BYO	Strong edge policies/plugins	Analytics	Proxy/plugins, retries	No	n/a
Portkey	Regulated/enterprise teams	Broad	Guardrails & governance	Deep traces	Conditional routing	Partial	n/a
OpenRouter	Devs wanting one key to many models	Wide catalog	Basic API controls	App-side	Fallbacks	Partial	n/a
Eden AI	LLM + other AI services	Broad	Standard controls	Varies	Fallbacks/caching	Partial	n/a
LiteLLM	DIY/self-host proxy	Many providers	Config/key limits	Your infra	Retries/fallback	n/a	n/a
Unify	Quality-driven teams	Multi-model	Standard API security	Platform analytics	Best-model selection	n/a	n/a
Orq	Orchestration-first teams	Wide support	Platform controls	Platform analytics	Orchestration flows	n/a	n/a
Apigee / NGINX	Enterprises / DIY	BYO	Policies	Add-ons / custom	Custom	n/a	n/a

Pricing & TCO: compare real costs (not just unit prices)

Raw $/1K tokens hides the real picture. TCO shifts with retries/fallbacks, latency (which affects usage), provider variance, observability storage, and evaluation runs. A transparent marketplace helps you choose routes that balance cost and UX.

TCO ≈ Σ (Base_tokens × Unit_price × (1 + Retry_rate))
      + Observability_storage
      + Evaluation_tokens
      + Egress

• Prototype (~10k tokens/day): Optimize for time-to-first-token (Playground, quickstarts).
• Mid-scale (~2M tokens/day): Marketplace-guided routing/failover can trim 10–20% while improving UX.
• Spiky workloads: Expect higher effective token costs from retries during failover—budget for it.

Migration playbooks: moving to ShareAI

From GitLab AI Gateway

Keep gateway-level policies where they shine. Add ShareAI for marketplace routing + instant failover. Pattern: gateway auth/policy → ShareAI route per model → measure marketplace stats → tighten policies.

From OpenRouter

Map model names, verify prompt parity, then shadow 10% of traffic and ramp 25% → 50% → 100% as latency/error budgets hold. Marketplace data makes provider swaps straightforward.

From LiteLLM

Replace the self-hosted proxy on production routes you don’t want to operate; keep LiteLLM for dev if desired. Compare ops overhead vs. managed routing benefits.

From Unify / Portkey / Orq / Kong

Define feature-parity expectations (analytics, guardrails, orchestration, plugins). Many teams run hybrid: keep specialized features where they’re strongest; use ShareAI for transparent provider choice and failover.

Developer quickstart (copy-paste)

The following use an OpenAI-compatible surface. Replace YOUR_KEY with your ShareAI key—get one at Create API Key.

#!/usr/bin/env bash
# cURL — Chat Completions
# Prereqs:
#   export SHAREAI_API_KEY="YOUR_KEY"

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": "Give me a short haiku about reliable routing." }
    ],
    "temperature": 0.4,
    "max_tokens": 128
  }'

// JavaScript (Node 18+/Edge runtimes) — Chat Completions
// Prereqs:
//   process.env.SHAREAI_API_KEY = "YOUR_KEY"

async function main() {
  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: "Give me a short haiku about reliable routing." }
      ],
      temperature: 0.4,
      max_tokens: 128
    })
  });

  if (!res.ok) {
    console.error("Request failed:", res.status, await res.text());
    return;
  }

  const data = await res.json();
  console.log(JSON.stringify(data, null, 2));
}

main().catch(console.error);

Next steps: Open Playground · Create API Key · API Reference

Security, privacy & compliance checklist (vendor-agnostic)

• Key handling: rotation cadence; minimal scopes; environment separation.
• Data retention: where prompts/responses are stored, for how long; redaction defaults.
• PII & sensitive content: masking; access controls; regional routing for data locality.
• Observability: prompt/response logging; ability to filter/pseudonymize; propagate trace IDs consistently.
• Incident response: escalation paths and provider SLAs.

FAQ — GitLab AI Gateway vs other competitors

GitLab AI Gateway vs ShareAI — which for multi-provider routing?

ShareAI. It’s built for marketplace transparency (price, latency, uptime, availability, provider type) and smart routing/failover across many providers. GitLab AI Gateway is egress governance (centralized credentials, policy, observability). Many teams use both.

GitLab AI Gateway vs OpenRouter — quick multi-model access or gateway controls?

OpenRouter makes multi-model access quick; GitLab centralizes policy and observability. If you also want pre-route transparency and instant failover, ShareAI combines multi-provider access with a marketplace view and resilient routing.

GitLab AI Gateway vs Eden AI — many AI services or egress control?

Eden AI aggregates several AI services (LLM, image, TTS). GitLab centralizes policy/credentials. For transparent pricing/latency across many providers and instant failover, choose ShareAI.

GitLab AI Gateway vs LiteLLM — self-host proxy or managed governance?

LiteLLM is a DIY proxy you operate; GitLab is managed governance/observability for AI egress. If you’d rather not run a proxy and want marketplace-driven routing, choose ShareAI.

GitLab AI Gateway vs Portkey — who’s stronger on guardrails?

Both emphasize governance/observability; depth and ergonomics differ. If your main need is transparent provider choice + failover, add ShareAI.

GitLab AI Gateway vs Unify — best-model selection vs policy enforcement?

Unify focuses on evaluation-driven model selection; GitLab focuses on policy/observability. For one API over many providers with live marketplace stats, use ShareAI.

GitLab AI Gateway vs Orq — orchestration vs egress?

Orq helps orchestrate workflows; GitLab governs egress traffic. ShareAI complements either with marketplace routing.

GitLab AI Gateway vs Kong AI Gateway — two gateways

Both are gateways (policies, plugins, analytics), not marketplaces. Many teams pair a gateway with ShareAI for transparent multi-provider routing and failover.

GitLab AI Gateway vs Traefik AI Gateway — specialized AI middlewares or broader platform?

Traefik’s thin AI layer and specialized middlewares pair well with ShareAI’s marketplace transparency; GitLab provides governance inside the GitLab ecosystem.

GitLab AI Gateway vs Apigee — API management vs AI-specific egress

Apigee is broad API management; GitLab is AI-focused egress governance within your DevOps flow. If you need provider-agnostic access with marketplace transparency, use ShareAI.

GitLab AI Gateway vs NGINX — DIY vs turnkey

NGINX offers DIY filters/policies; GitLab offers a packaged layer. To avoid custom scripting and get transparent provider selection, layer in ShareAI.

OpenRouter vs Apache APISIX — marketplace speed or edge policy?

OpenRouter accelerates model trialing; APISIX is a programmable gateway. If you also want pre-route price/latency transparency with instant failover, use ShareAI.

LiteLLM vs OpenRouter — DIY proxy or hosted aggregator?

LiteLLM gives you a self-host proxy; OpenRouter hosts aggregation. ShareAI adds live marketplace stats + failover and returns 70% of revenue to providers—giving back to the community.

Kong vs Apache APISIX — enterprise plugins or open-source edge?

Both are strong gateways. If you want transparent provider choice and multi-provider resilience, route through ShareAI and keep your gateway for policy.

Portkey vs Unify — guardrails vs quality-driven selection?

Portkey leans into guardrails/observability; Unify into model quality selection. ShareAI brings market transparency and resilient routing to either stack.

NGINX vs Apache APISIX — two DIY paths

Both require engineering investment. If you’d rather delegate multi-provider routing + failover and keep policy at the edge, layer in ShareAI.

Try ShareAI next

Open Playground · Create your API key · Browse Models · Read the Docs · See Releases · Sign in / Sign up