DEV Community: Jenny Met

How to Access All AI Models with a Single API Key in 2026

Jenny Met — Thu, 02 Apr 2026 08:03:36 +0000

You want to use GPT-5 for general tasks, Claude for coding, Gemini for long documents, and DeepSeek for cheap inference. That means four API keys, four billing accounts, four different SDKs, and four sets of rate limits to manage.

There's a better way. Unified AI API gateways let you access all of these models — and hundreds more — through a single API key and endpoint.

This guide shows you exactly how to set it up in under 5 minutes.

The Problem with Multiple API Keys

If you're calling AI models directly, your setup looks something like this:

# The painful way — managing multiple clients
import openai
import anthropic
import google.generativeai as genai

openai_client = openai.OpenAI(api_key="sk-openai-...")
anthropic_client = anthropic.Anthropic(api_key="sk-ant-...")
genai.configure(api_key="AIza...")

# Different APIs, different formats, different error handling for each

This creates real problems:

Key management overhead — rotating, securing, and tracking 5+ API keys
Billing fragmentation — separate invoices from each provider
Code complexity — different SDKs and response formats per provider
No failover — if OpenAI goes down, your app goes down

The Solution: Unified AI API Gateways

A unified gateway gives you one endpoint that routes to all providers:

from openai import OpenAI

# One client, one key, access to everything
client = OpenAI(
    base_url="https://crazyrouter.com/v1",
    api_key="sk-your-single-key"
)

# Use any model — just change the model name
gpt_response = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

claude_response = client.chat.completions.create(
    model="claude-sonnet-4.6",
    messages=[{"role": "user", "content": "Review this code..."}]
)

gemini_response = client.chat.completions.create(
    model="gemini-2.5-pro",
    messages=[{"role": "user", "content": "Summarize this 500-page PDF..."}]
)

Same client. Same format. Same API key. Just change the model parameter.

Step-by-Step Setup

Option 1: Crazyrouter (627+ Models, Broadest Coverage)

Crazyrouter provides access to 627+ models from 20+ providers including OpenAI, Anthropic, Google, DeepSeek, ByteDance, Alibaba, and xAI.

Step 1: Sign up at crazyrouter.com (you get $0.20 free credit)

Step 2: Copy your API key from the dashboard

Step 3: Use it with the OpenAI SDK:

from openai import OpenAI

client = OpenAI(
    base_url="https://crazyrouter.com/v1",
    api_key="sk-your-crazyrouter-key"
)

# Now you can access any of 627+ models
response = client.chat.completions.create(
    model="gpt-5",  # or claude-sonnet-4.6, gemini-2.5-pro, deepseek-r1, etc.
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Step 4 (optional): Use environment variables for production:

export OPENAI_BASE_URL=https://crazyrouter.com/v1
export OPENAI_API_KEY=sk-your-crazyrouter-key

Now any tool that uses the OpenAI SDK (Cursor, Continue, LangChain, etc.) will automatically route through Crazyrouter.

Bonus: Crazyrouter also supports native Anthropic and Gemini API formats, so you don't have to use the OpenAI compatibility layer if you prefer the native SDKs.

Option 2: OpenRouter (300+ Models, Free Tier)

OpenRouter is the most popular option with a free tier for select models.

Step 1: Sign up at openrouter.ai

Step 2: Get your API key

Step 3: Use it:

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-your-key"
)

response = client.chat.completions.create(
    model="openai/gpt-5",  # OpenRouter uses provider/model format
    messages=[{"role": "user", "content": "Hello!"}]
)

Note: OpenRouter adds a 5% fee on BYOK (bring your own key) usage and has markup on pay-per-token pricing.

Option 3: LiteLLM (Open-Source, Self-Hosted)

If you need to self-host, LiteLLM is the go-to open-source option.

Step 1: Install LiteLLM:

pip install litellm

Step 2: Use it as a Python library:

from litellm import completion

# You still need individual provider keys, but LiteLLM unifies the interface
response = completion(
    model="gpt-5",
    messages=[{"role": "user", "content": "Hello!"}],
    api_key="sk-openai-key"
)

response = completion(
    model="claude-sonnet-4.6",
    messages=[{"role": "user", "content": "Hello!"}],
    api_key="sk-ant-key"
)

Note: LiteLLM unifies the API format but you still manage individual provider keys unless you run the proxy server.

Which Gateway Should You Choose?

Need	Best Option
Most models (627+)	Crazyrouter
Free tier / experimentation	OpenRouter
Self-hosted / open-source	LiteLLM
Enterprise observability	Portkey
Multimodal beyond text	AIMLAPI or Eden AI

Using Your Single API Key with Popular Tools

Once you have a gateway API key, you can use it with most AI-powered tools:

Cursor IDE

// .cursor/config.json
{
  "openai.baseUrl": "https://crazyrouter.com/v1",
  "openai.apiKey": "sk-your-key"
}

LangChain

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://crazyrouter.com/v1",
    api_key="sk-your-key",
    model="gpt-5"
)

cURL

curl https://crazyrouter.com/v1/chat/completions \
  -H "Authorization: Bearer sk-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4.6",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Node.js

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://crazyrouter.com/v1',
  apiKey: 'sk-your-key'
});

const response = await client.chat.completions.create({
  model: 'gemini-2.5-pro',
  messages: [{ role: 'user', content: 'Hello!' }]
});

Cost Comparison: Direct vs. Gateway

Using a gateway doesn't have to cost more. Some gateways actually offer lower prices than going direct:

Model	Official Price (per 1M tokens)	Crazyrouter	OpenRouter
GPT-5	$1.25 / $10.00	Below official	~$1.38 / $11.00
Claude Sonnet 4.6	$3.00 / $15.00	Below official	~$3.30 / $16.50
Gemini 2.5 Pro	$1.25 / $10.00	Below official	~$1.38 / $11.00
DeepSeek R1	$0.55 / $2.19	Below official	~$0.60 / $2.41

Prices are approximate and change frequently. Check each platform for current rates.

FAQ

Q: Is there latency overhead?
A: Minimal. Most gateways add 10-100ms depending on your location. Crazyrouter's 7 global edge nodes keep overhead under 50ms for most regions.

Q: What happens if a provider goes down?
A: Good gateways have automatic failover. Your request gets rerouted to an alternative provider transparently.

Q: Can I use my own provider API keys?
A: Some gateways support BYOK (bring your own key). OpenRouter charges 5% on BYOK usage. Crazyrouter uses its own pooled keys with below-official pricing.

Q: Is it secure?
A: Your requests are proxied through the gateway. Choose a gateway with a clear privacy policy and data handling practices.

Summary

Accessing all AI models with a single API key is straightforward in 2026:

Pick a gateway (Crazyrouter for broadest coverage, OpenRouter for free tier, LiteLLM for self-hosting)
Sign up and get your API key
Replace your base URL and API key in your existing code
Access 300-627+ models through one endpoint

The migration takes under 5 minutes and eliminates multi-vendor complexity entirely.

Last updated: April 2026.

Top 10 AI API Aggregator Platforms in 2026: One Key, Hundreds of Models

Jenny Met — Thu, 02 Apr 2026 07:18:44 +0000

Managing separate API keys for OpenAI, Anthropic, Google, and DeepSeek is one of the biggest pain points for AI developers in 2026. AI API aggregator platforms solve this by providing a single endpoint and API key to access hundreds of models.

I tested 10 platforms over the past month across three criteria: model coverage, pricing transparency, and developer experience. Here's the ranking.

What Is an AI API Aggregator?

An AI API aggregator (also called an AI API gateway or unified AI API) sits between your application and multiple AI providers. You get:

One API key for all models
One billing account instead of managing 5-10 provider accounts
Unified API format (usually OpenAI-compatible)
Automatic failover when a provider goes down

The 10 Best AI API Aggregator Platforms (Ranked)

1. Crazyrouter

Website: crazyrouter.com

Crazyrouter provides access to 627+ models across 102 model families from 20+ providers. It stands out for having the broadest model coverage of any aggregator I tested, including hard-to-find models from ByteDance (Doubao, Seedance), Alibaba (Qwen3), and xAI (Grok).

Feature	Detail
Models	627+ across 102 families
API Format	OpenAI, Anthropic, and Gemini compatible
Pricing	Pay-as-you-go, below official rates
Free Credit	$0.20 on signup
Global Nodes	7 regions (US, Japan, Korea, UK, HK, PH, RU)
Capabilities	Chat, vision, image gen, video gen, music, TTS, STT, embeddings

Why it ranks #1: Broadest model coverage, multi-format API support (not just OpenAI-compatible), and consistently lower pricing than going direct. The 7 global edge nodes also give it the best latency profile for international teams.

Quick start:

from openai import OpenAI
client = OpenAI(
    base_url="https://crazyrouter.com/v1",
    api_key="sk-your-crazyrouter-key"
)
response = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "Hello"}]
)

2. OpenRouter

Website: openrouter.ai

The most well-known aggregator with 300+ models. Strong community, good documentation, and OAuth support for user-facing apps.

Feature	Detail
Models	300+
API Format	OpenAI-compatible
Pricing	Pay-per-token + 5% BYOK fee
Free Models	Yes (limited selection)

Strengths: Large community, free tier, well-documented.
Weaknesses: 10-30% markup on popular models, US-only infrastructure, no native Anthropic/Gemini format support.

3. AIMLAPI

Website: aimlapi.com

Offers 400+ models covering chat, image, video, audio, and 3D generation. Good for teams that need multimodal capabilities beyond text.

Feature	Detail
Models	400+
API Format	OpenAI-compatible
Pricing	Pay-as-you-go
Capabilities	Chat, image, video, audio, voice, 3D

Strengths: Broad multimodal coverage, competitive pricing.
Weaknesses: Less established community, documentation could be better.

4. Portkey

Website: portkey.ai

Enterprise-focused gateway with strong observability, prompt management, and compliance features. Best for teams that need production-grade monitoring.

Feature	Detail
Models	100+ via provider integrations
API Format	OpenAI-compatible
Pricing	Free tier + paid plans
Key Feature	Built-in observability and prompt versioning

Strengths: Enterprise governance, detailed analytics, prompt management.
Weaknesses: Smaller model catalog than pure aggregators, enterprise pricing can be steep.

5. LiteLLM

Website: litellm.ai

Open-source Python library that provides a unified interface to 100+ LLMs. Can be self-hosted or used as a managed proxy.

Feature	Detail
Models	100+
API Format	OpenAI-compatible
Pricing	Free (open-source) or managed plans
Key Feature	Self-hosting option

Strengths: Open-source, self-hostable, no vendor lock-in.
Weaknesses: Requires infrastructure management if self-hosted, higher latency overhead than compiled alternatives.

6. Eden AI

Website: edenai.co

Specializes in multimodal AI APIs beyond just LLMs — OCR, document parsing, image analysis, translation, and more.

Feature	Detail
Models	500+ (including specialized AI services)
API Format	Custom unified API
Pricing	Pay-as-you-go with automatic cost optimization

Strengths: Broadest multimodal coverage including non-LLM services.
Weaknesses: Not OpenAI-compatible format, steeper learning curve.

7. Helicone

Website: helicone.ai

Primarily an observability platform that also functions as an AI gateway. Best for teams that prioritize logging and cost tracking.

Feature	Detail
Models	Routes to major providers
API Format	OpenAI-compatible proxy
Pricing	Free tier available
Key Feature	Request logging and cost analytics

Strengths: Free observability, easy integration as a proxy layer.
Weaknesses: Not a full aggregator — you still need provider keys.

8. Bifrost (by Maxim AI)

Website: getmaxim.ai

High-performance open-source gateway built in Go. Achieves extremely low latency overhead (11 microseconds at 5,000 RPS).

Feature	Detail
Models	15+ providers
API Format	OpenAI-compatible
Pricing	Open-source (free)
Key Feature	Ultra-low latency, automatic failover

Strengths: Best raw performance, open-source, self-hostable.
Weaknesses: Smaller model catalog, requires infrastructure setup.

9. SiliconFlow

Website: siliconflow.com

All-in-one AI cloud platform combining model hosting, inference optimization, and API aggregation. Strong in the Asian market.

Feature	Detail
Models	Major LLMs + open-source models
API Format	OpenAI-compatible
Pricing	Competitive, especially for Asian providers

Strengths: Optimized inference, good Asian model coverage.
Weaknesses: Less established in Western markets.

10. Unified.to

Website: unified.to

Provides unified APIs across multiple categories (not just AI), including CRM, HRIS, and accounting integrations alongside generative AI.

Feature	Detail
Models	Major LLM providers
API Format	Custom unified API
Pricing	Subscription-based

Strengths: Broad integration coverage beyond AI.
Weaknesses: AI is one of many features, not the primary focus.

Comparison Table

Platform	Models	OpenAI Compatible	Self-Host	Free Tier	Best For
Crazyrouter	627+	✅ + Anthropic + Gemini	❌	$0.20 credit	Broadest model access, international teams
OpenRouter	300+	✅	❌	✅	Community, free models
AIMLAPI	400+	✅	❌	✅	Multimodal (image/video/audio)
Portkey	100+	✅	❌	✅	Enterprise observability
LiteLLM	100+	✅	✅	✅ (OSS)	Self-hosted deployments
Eden AI	500+	❌	❌	✅	Non-LLM AI services
Helicone	Proxy	✅	✅	✅	Logging and cost tracking
Bifrost	15+ providers	✅	✅	✅ (OSS)	Ultra-low latency
SiliconFlow	Major LLMs	✅	❌	✅	Asian market, inference optimization
Unified.to	Major LLMs	❌	❌	❌	Multi-category integrations

How to Choose

Maximum model coverage: Crazyrouter (627+ models) or AIMLAPI (400+)
Free experimentation: OpenRouter (free tier with select models)
Enterprise compliance: Portkey (governance + observability)
Self-hosting required: LiteLLM or Bifrost
Beyond text (OCR, translation): Eden AI
Best performance: Bifrost (lowest latency) or Crazyrouter (global edge nodes)
Cost tracking: Helicone (free observability layer)

Getting Started

Most aggregators support the OpenAI SDK format. Here's the general pattern — just swap the base URL and API key:

from openai import OpenAI

# Works with Crazyrouter, OpenRouter, AIMLAPI, Portkey, LiteLLM, etc.
client = OpenAI(
    base_url="https://<aggregator-endpoint>/v1",
    api_key="your-aggregator-key"
)

response = client.chat.completions.create(
    model="gpt-5",  # or claude-sonnet-4.6, gemini-2.5-pro, etc.
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

The migration from direct provider APIs typically takes under 5 minutes.

Last updated: April 2026. Pricing and model counts change frequently — check each platform's website for current details.

2026'da En İyi 10 AI API Proxy: Geliştiriciler İçin Kapsamlı Karşılaştırma

Jenny Met — Mon, 30 Mar 2026 07:45:06 +0000

2026'da En İyi 10 AI API Proxy: Geliştiriciler İçin Kapsamlı Karşılaştırma

Ticari, açık kaynak ve kendi sunucunuzda barındırabileceğiniz 10 AI API proxy'sini gerçek fiyatlarla, çalışan kod örnekleriyle ve özellik matrisiyle karşılaştırdık.

AI API Proxy Nedir?

AI API proxy, uygulamanız ile AI sağlayıcıları (OpenAI, Anthropic, Google vb.) arasında yer alan bir ara katmandır. Kimlik doğrulama, yönlendirme, hız sınırlama, önbellekleme ve yedekleme (failover) işlemlerini yönetir. Her sağlayıcı için ayrı API anahtarı ve SDK yönetmek yerine tek bir endpoint kullanırsınız.

Neden ihtiyacınız var:

Tek anahtar, tüm modeller — 5 farklı sağlayıcı hesabıyla uğraşmayın
Maliyet optimizasyonu — Basit görevler için ucuz modellere yönlendirin, tekrar eden sorguları önbellekleyin
Güvenilirlik — Sağlayıcı çöktüğünde otomatik geçiş (düşündüğünüzden sık oluyor)
Gözlemlenebilirlik — Tüm sağlayıcılardaki maliyet, gecikme ve token kullanımını takip edin

Hızlı Karşılaştırma

Proxy	Tür	Model Sayısı	Fiyat	Kendi Sunucu	En İyi Kullanım
Crazyrouter	Ticari	627+	Resmi fiyatın ~%55'i	❌	En ucuz çok modlu erişim
LiteLLM	Açık Kaynak	100+	Ücretsiz	✅	Kendi sunucunuzda proxy
OpenRouter	Ticari	300+	Resmi + %10-30	❌	Hızlı prototipleme
Portkey	Ticari	1.600+ (BYOK)	Ücretsiz–$49/ay	✅	Kurumsal yönetişim
One API	Açık Kaynak	40+	Ücretsiz	✅	Yönetim paneli
Helicone	Ticari	BYOK	Ücretsiz–$20/ay	✅	Gözlemlenebilirlik
NGINX AI Proxy	Açık Kaynak	Yapılandırma	Ücretsiz	✅	NGINX kullanıcıları
Cloudflare AI GW	Ticari	BYOK	Ücretsiz	❌	Edge önbellekleme
Kong AI Gateway	Ticari	Eklenti bazlı	Kurumsal	✅	Kong kullanıcıları
Unify AI	Ticari	80+	Kullandıkça öde	❌	Akıllı yönlendirme

Ticari Proxy'ler

1. Crazyrouter — En Ucuz Çok Modlu API Proxy

Web sitesi: crazyrouter.com

Crazyrouter, resmi API fiyatlarının yaklaşık %55'ine 627+ model sunuyor. Onu benzersiz kılan şey: LLM + görüntü + video + müzik üretimini tek bir endpoint'ten sunan tek gateway olması.

Desteklenen modeller:

LLM: GPT-5/5.2, Claude Opus 4.6/Sonnet 4.6, Gemini 3 Pro, DeepSeek V3.2, Grok 4, Qwen 3
Görüntü: DALL-E 3, Midjourney, Flux Pro, Stable Diffusion 3.5
Video: Sora 2, Kling V2.6, Veo 3, Runway Gen4
Müzik: Suno V4

Fiyat karşılaştırması (Türk Lirası'yla düşünüldüğünde fark çok büyük):

Model	Resmi Fiyat	Crazyrouter	Tasarruf
GPT-5.2	$3 / $12 (1M token)	~$1,65 / $6,60	%45
Claude Opus 4.6	$15 / $75	~$8,25 / $41,25	%45
Claude Sonnet 4.6	$3 / $15	~$1,65 / $8,25	%45
Gemini 3 Pro	$1,25 / $10	~$0,69 / $5,50	%45

İki satır kod değişikliğiyle geçiş:

from openai import OpenAI

client = OpenAI(
    base_url="https://crazyrouter.com/v1",  # ← Sadece burayı değiştirin
    api_key="your-crazyrouter-key"           # ← Ve burayı
)

response = client.chat.completions.create(
    model="gpt-5-mini",
    messages=[{"role": "user", "content": "AI API proxy nedir? Bir cümleyle açıkla."}]
)
print(response.choices[0].message.content)

Gerçek test sonucu (Mart 2026):

{
  "model": "gpt-5-mini",
  "usage": {"prompt_tokens": 17, "completion_tokens": 37, "total_tokens": 54}
}

✅ En ucuz fiyat (~%55), 627+ model (görüntü/video/müzik dahil), OpenAI + Anthropic + Gemini uyumlu
❌ Kendi sunucunuzda barındırma yok

2. OpenRouter — En Popüler Varsayılan Seçenek

300+ model ve bazı modeller için ücretsiz katman. Ancak resmi fiyatların üzerine %10-30 ek ücret alıyor. Prototipleme için önemli değil, ölçekte ise ciddi fark yaratıyor.

✅ En büyük topluluk, bazı ücretsiz modeller
❌ %10-30 ek ücret, sadece LLM (görüntü/video yok)

3. Portkey — Kurumsal Kontrol Paneli

SOC 2 uyumluluğu, RBAC, denetim günlükleri ve koruma bariyerleri (PII algılama, içerik filtreleme) gereken ekipler için.

✅ En kapsamlı yönetişim, SOC 2
❌ BYOK (token indirimi yok), karmaşık kurulum

4. Helicone — Gözlemlenebilirlik Odaklı

AI API çağrılarınız için Datadog. Tek satır entegrasyon, 100K istek/ay ücretsiz.

✅ En iyi AI gözlemlenebilirliği
❌ Model toplayıcı değil (BYOK)

5-7. Diğer Ticari Seçenekler

Proxy	Odak	Kimin İçin
Cloudflare AI GW	Ücretsiz edge önbellekleme	Cloudflare kullanıcıları
Unify AI	Benchmark bazlı otomatik model seçimi	Deney yapan ekipler
Kong AI GW	Kong API Gateway'e AI eklentisi	Zaten Kong kullananlar

Açık Kaynak / Kendi Sunucunuzda

8. LiteLLM — Açık Kaynak Birincisi (18K+ Yıldız)

GitHub: github.com/BerriAI/litellm

En popüler açık kaynak AI API proxy. Python tabanlı, 100+ sağlayıcı, OpenAI uyumlu endpoint.

pip install litellm && litellm --config config.yaml

✅ MIT lisans, verileriniz sunucunuzdan çıkmaz
❌ Altyapıyı siz yönetiyorsunuz, BYOK

9. One API — En İyi Yönetim Paneli (20K+ Yıldız)

GitHub: github.com/songquanpeng/one-api

Web yönetim paneli, çoklu kiracı desteği, token kotası, kanal dengeleme. Docker ile tek tıkla kurulum.

✅ En iyi yönetim arayüzü, çoklu kiracı
❌ İngilizce topluluğu küçük

10. NGINX AI Proxy — NGINX Deneyimlilere

NGINX artık AI'ya özel proxy yapılandırmalarını destekliyor — SSE akışı, istek dönüşümü, yük dengeleme.

✅ Mevcut NGINX deneyimini kullanır
❌ Manuel yapılandırma, maliyet takibi yok

Özellik Matrisi

Özellik	Crazyrouter	LiteLLM	OpenRouter	Portkey	One API	Helicone
Model	627+	100+	300+	1.600+	40+	BYOK
Fiyat indirimi	~%45	Yok	-%10-30	Yok	Yok	Yok
Görüntü/Video/Müzik	✅	❌	❌	❌	❌	❌
Kendi sunucu	❌	✅	❌	✅	✅	✅
Yönetim paneli	Var	✅	❌	✅	✅	✅
Akıllı yönlendirme	❌	Temel	❌	✅	❌	❌
OpenAI uyumlu	✅	✅	✅	✅	✅	✅

Nasıl Seçmeli?

3 soru ile karar verin:

S1: En çok neye önem veriyorsunuz?

Maliyet tasarrufu → Crazyrouter (~%45 indirim, TL ile düşünüldüğünde büyük fark)
Veri güvenliği → LiteLLM veya One API (kendi sunucunuzda)
Kurumsal uyumluluk → Portkey (SOC 2)

S2: Görüntü/video/müzik üretimi de gerekiyor mu?

Evet → Crazyrouter (tek çok modlu gateway)
Hayır → Herhangi biri

S3: Yönetim paneli istiyor musunuz?

Evet → One API (en iyi arayüz) veya Portkey
Hayır → LiteLLM (en hafif)

SSS

AI API proxy ile AI API gateway arasındaki fark nedir?

Pratikte birbirinin yerine kullanılıyor. Teknik olarak "proxy" istekleri iletir, "gateway" ise yönetim özellikleri (kimlik doğrulama, hız sınırlama, analitik) ekler. Bu listedeki tüm araçlar her ikisini de yapıyor.

Birden fazla AI proxy birlikte kullanılabilir mi?

Evet. Yaygın kalıp: model erişimi için Crazyrouter (en ucuz tokenler) + gözlemlenebilirlik için Helicone. Veya kendi sunucunuzda LiteLLM çalıştırıp, maliyet tasarrufu için Crazyrouter'a yönlendirme.

AI proxy'ler gecikme ekler mi?

Minimum — ticari proxy'ler için genellikle 5-20ms. Kendi sunucunuzdaki proxy'ler (LiteLLM, One API) aynı bölgede konuşlandırılırsa neredeyse sıfır gecikme ekler.

Üçüncü taraf proxy üzerinden API anahtarı göndermek güvenli mi?

Ticari proxy'ler (Crazyrouter, OpenRouter, Portkey) anahtarları sizin adınıza yönetir — sağlayıcı anahtarlarınızı onlar üzerinden göndermezsiniz. Maksimum güvenlik için LiteLLM veya One API ile kendi sunucunuzda barındırın.

Son güncelleme: Mart 2026. Fiyatlar ve model sayıları sık değişir — güncel bilgi için her platformun web sitesini kontrol edin.

Top 10 AI API Proxies & Reverse Proxies for Developers in 2026

Jenny Met — Mon, 30 Mar 2026 07:45:04 +0000

Top 10 AI API Proxies & Reverse Proxies for Developers in 2026

A hands-on comparison of 10 AI API proxies — commercial gateways, open-source solutions, and self-hosted options — with real pricing data, working code examples, and a feature matrix.

What Is an AI API Proxy?

An AI API proxy sits between your application and AI providers (OpenAI, Anthropic, Google, etc.), handling authentication, routing, rate limiting, caching, and failover. Instead of managing separate API keys and SDKs for each provider, you hit one endpoint.

Think of it as NGINX for AI models — but purpose-built for the unique challenges of LLM traffic: streaming responses, token-based billing, model-specific quirks, and multi-provider failover.

Why you need one:

One API key, many models — Stop juggling 5 different provider accounts
Cost optimization — Route to cheaper models for simple tasks, cache repeated queries
Reliability — Automatic failover when a provider goes down (it happens more than you think)
Observability — Track costs, latency, and token usage across all providers
Security — Centralize API key management instead of scattering keys across services

Quick Comparison

Proxy	Type	Models	Pricing	Self-Host	Best For
Crazyrouter	Commercial	627+	~55% of official	❌	Cheapest multi-modal access
LiteLLM	Open Source	100+ providers	Free	✅	Self-hosted LLM proxy
OpenRouter	Commercial	300+	Official + 10-30%	❌	Quick prototyping
Portkey	Commercial	1,600+ (BYOK)	Free–$49/mo	✅	Enterprise governance
One API	Open Source	40+ providers	Free	✅	Chinese dev community
Helicone	Commercial	BYOK	Free–$20/mo	✅	Observability layer
NGINX AI Proxy	Open Source	Config-based	Free	✅	Existing NGINX users
Cloudflare AI GW	Commercial	BYOK	Free	❌	Edge caching
Kong AI Gateway	Commercial	Plugin-based	Enterprise	✅	Existing Kong users
Unify AI	Commercial	80+	Pay-per-token	❌	Benchmark-driven routing

Commercial Proxies

1. Crazyrouter — Cheapest Multi-Modal API Proxy

Website: crazyrouter.com

Crazyrouter is the most aggressive on pricing: roughly 55% of official API prices across 627+ models. But what makes it unique among proxies is multi-modal coverage — it's the only gateway that handles LLM, image generation, video generation, and music generation through a single endpoint.

Models covered:

LLMs: GPT-5/5.2, Claude Opus 4.6/Sonnet 4.6, Gemini 3 Pro, DeepSeek V3.2/R1, Grok 4, Qwen 3
Image: DALL-E 3, Midjourney, Flux Pro, Stable Diffusion 3.5
Video: Sora 2, Kling V2.6, Veo 3, Runway Gen4
Music: Suno V4

Pricing comparison:

Model	Official	Crazyrouter	Savings
GPT-5.2	$3.00 / $12.00 per 1M tokens	~$1.65 / $6.60	45%
Claude Opus 4.6	$15.00 / $75.00	~$8.25 / $41.25	45%
Claude Sonnet 4.6	$3.00 / $15.00	~$1.65 / $8.25	45%
Gemini 3 Pro	$1.25 / $10.00	~$0.69 / $5.50	45%

Drop-in replacement — change two lines:

from openai import OpenAI

client = OpenAI(
    base_url="https://crazyrouter.com/v1",
    api_key="your-crazyrouter-key"
)

response = client.chat.completions.create(
    model="gpt-5-mini",
    messages=[{"role": "user", "content": "What is an AI API proxy? One sentence."}]
)
print(response.choices[0].message.content)

Tested response (March 2026):

{
  "model": "gpt-5-mini",
  "choices": [{
    "message": {
      "content": "An AI API proxy is an intermediary service that routes and transforms requests and responses between applications and AI providers while handling authentication, security, rate limiting, caching, logging, and policy enforcement."
    },
    "finish_reason": "stop"
  }],
  "usage": {"prompt_tokens": 17, "completion_tokens": 37, "total_tokens": 54}
}

Also natively supports Anthropic SDK format and Google Gemini format — no forced conversion to OpenAI format.

✅ Cheapest pricing (~55% of official), 627+ models including image/video/music, OpenAI + Anthropic + Gemini compatible, 7 global regions
❌ No self-hosting, smaller community than OpenRouter

2. OpenRouter — The Popular Default

Website: openrouter.ai

OpenRouter is the most widely known AI API proxy. 300+ models, a free tier for some models, and a large community. It's the "safe default" choice.

The catch: 10-30% markup on top of official prices. For prototyping this doesn't matter. At scale, it adds up fast — a $10,000/month API bill becomes $11,000-$13,000.

✅ Largest community, free tier for some models, easy to start
❌ 10-30% markup, LLM only (no image/video), no self-hosting

3. Portkey — Enterprise Control Plane

Website: portkey.ai

Portkey positions itself as the "control plane for AI apps." If your team needs SOC 2 compliance, RBAC, audit logs, and guardrails (PII detection, content filtering), Portkey is the enterprise answer.

Key features:

1,600+ LLMs (BYOK — Bring Your Own Key)
Guardrails: PII detection, input/output validation
Distributed tracing, cost dashboards, latency monitoring
Prompt management with A/B testing
Automatic failover between providers

Pricing: Free (10K requests/month) → Pro $49/month → Enterprise custom

✅ Most comprehensive governance, SOC 2, open-source core
❌ BYOK (no token cost savings), complex setup, overkill for simple projects

4. Helicone — Observability-First Proxy

Website: helicone.ai

Helicone isn't a model aggregator — it's Datadog for AI API calls. One-line integration (change your base URL), and you get full request/response logging, cost tracking, semantic caching, and latency monitoring.

Pricing: Free (100K requests/month) → Pro $20/month

✅ Best AI observability, 100K free requests/month, one-line setup
❌ Not a model aggregator (BYOK), adds a proxy hop

5. Cloudflare AI Gateway — Edge Caching

Website: developers.cloudflare.com/ai-gateway

Free proxy layer running on Cloudflare's edge network. Provides caching, rate limiting, and basic analytics. If you're already using Cloudflare, this is a no-brainer addition.

✅ Free, global edge network, edge caching reduces repeated query costs
❌ BYOK (no cost savings), basic analytics, no smart routing

6. Unify AI — Benchmark-Driven Routing

Website: unify.ai

Unify's unique angle: instead of you picking the model, it automatically routes to the optimal model based on benchmarks, cost, and latency.

✅ Intelligent routing, data-driven decisions
❌ Only 80+ models, benchmark scores may not match your use case

7. Kong AI Gateway — For Kong Shops

Website: konghq.com

AI plugins for the popular Kong API Gateway. Natural extension if your org already uses Kong for API management.

✅ Reuses existing Kong infrastructure, enterprise-grade
❌ Pointless without Kong, complex AI-specific configuration

Open-Source / Self-Hosted Proxies

8. LiteLLM — The Go-To Open Source Proxy

GitHub: github.com/BerriAI/litellm (18K+ stars)

LiteLLM is the most popular open-source AI API proxy. Python-based, supports 100+ providers, OpenAI-compatible endpoint. If you want full control over your AI infrastructure with data staying on your servers, LiteLLM is the standard choice.

pip install litellm
litellm --config config.yaml

model_list:
  - model_name: gpt-5
    litellm_params:
      model: openai/gpt-5
      api_key: sk-xxx
  - model_name: claude-opus
    litellm_params:
      model: anthropic/claude-opus-4-6
      api_key: sk-ant-xxx

Features: virtual keys, budget management, rate limiting, load balancing, spend tracking.

✅ MIT license, data never leaves your infra, active community, cost tracking
❌ You manage the infrastructure, BYOK (no token discounts), LLM only

9. One API — Popular in Chinese Dev Community

GitHub: github.com/songquanpeng/one-api (20K+ stars)

One API is the most widely used open-source AI proxy in Chinese-speaking developer communities. It provides a web-based admin panel for managing multiple API keys, channels, and token quotas. Think of it as LiteLLM + admin dashboard, with a focus on key management and reselling scenarios.

Key features:

Web admin panel (manage keys, channels, quotas)
Multi-tenant support (create sub-accounts with token limits)
Channel balancing (distribute requests across multiple keys)
40+ providers supported
Docker one-click deployment

✅ Best admin UI among open-source options, multi-tenant, 20K+ GitHub stars
❌ Less active English community, some providers lag behind LiteLLM

10. NGINX AI Proxy — For NGINX Veterans

Blog: blog.nginx.org/blog/using-nginx-as-an-ai-proxy

NGINX now supports AI-specific proxy configurations — streaming SSE responses, request/response transformation for different providers, and load balancing across AI backends. If your team already runs NGINX, adding AI proxy capabilities is a natural extension.

✅ Leverages existing NGINX expertise, no new dependencies, battle-tested infrastructure
❌ Manual configuration, no built-in model routing or cost tracking, steep learning curve for AI-specific features

Feature Matrix

Feature	Crazyrouter	LiteLLM	OpenRouter	Portkey	One API	Helicone	CF GW
Models	627+	100+	300+	1,600+(BYOK)	40+	BYOK	BYOK
Price discount	~45%	None	-10-30%	None	None	None	None
Image/Video/Music	✅	❌	❌	❌	❌	❌	❌
Self-host	❌	✅	❌	✅	✅	✅	❌
Admin UI	Dashboard	✅	❌	✅	✅	✅	✅
Guardrails	❌	❌	❌	✅	❌	❌	❌
Smart routing	❌	Basic	❌	✅	❌	❌	❌
Caching	❌	✅	❌	✅	❌	✅	✅
OpenAI compatible	✅	✅	✅	✅	✅	✅	✅

How to Choose

Decision tree:

"I need the cheapest token prices" → Crazyrouter (~45% off official prices, widest model coverage)
"I need data to stay on my servers" → LiteLLM (open source, MIT, most providers) or One API (if you want an admin panel)
"I need enterprise governance" → Portkey (SOC 2, guardrails, RBAC, audit logs)
"I need to understand my AI spending" → Helicone (best observability, 100K free requests)
"I also need image/video/music generation" → Crazyrouter (only gateway covering full multi-modal spectrum)
"I'm already using Cloudflare/Kong/NGINX" → Add their AI gateway plugins to your existing stack

FAQ

What's the difference between an AI API proxy and an AI API gateway?

In practice, they're used interchangeably. Technically, a "proxy" forwards requests to AI providers, while a "gateway" adds management features (auth, rate limiting, analytics, routing). Every tool in this list does both — the distinction is marketing, not technical.

Can I use multiple AI proxies together?

Yes. A common pattern: use Crazyrouter for model access (cheapest tokens) + Helicone for observability (track what's happening). Or self-host LiteLLM as your proxy layer, routing to Crazyrouter for cost savings. See our cost optimization guide for architecture patterns.

Do AI proxies add latency?

Minimal — typically 5-20ms for commercial proxies (Crazyrouter, OpenRouter). Self-hosted proxies (LiteLLM, One API) add almost no latency if deployed in the same region as your app. Cloudflare AI Gateway can actually reduce latency through edge caching. For a deep dive, see our latency optimization guide.

Which proxy is best for production use?

Depends on your constraints. For cost: Crazyrouter. For control: LiteLLM. For enterprise: Portkey. For observability: Helicone. Many production systems use 2-3 of these together. Check our load balancing guide for production architecture patterns.

Is it safe to route API keys through a third-party proxy?

Commercial proxies (Crazyrouter, OpenRouter, Portkey) manage keys on your behalf — you don't send your provider keys through them. For BYOK proxies (Helicone, Cloudflare), your keys pass through their infrastructure — check their security practices. For maximum security, self-host with LiteLLM or One API.

Last updated: March 2026. Prices and model counts change frequently — check each platform's website for the latest.

OpenClaw Tutorial: Build Your Own Private AI Assistant from Scratch

Jenny Met — Mon, 30 Mar 2026 07:41:46 +0000

OpenClaw Tutorial: The Complete Guide to Building Your Private AI Assistant

Ever wanted your own AI assistant — not a web-based ChatGPT wrapper, but something running on your server, always on, talking to you through Telegram? Something you actually control?

That's exactly what OpenClaw does. It's an open-source AI assistant framework you can deploy on a VPS, Raspberry Pi, or even your laptop. Connect it to Telegram, WhatsApp, Discord — whatever you use. It supports 627+ AI models, the config is flexible, and getting started is easier than you'd think.

Let's walk through it step by step.

Step 1: Installation & Environment Setup (Node.js 22+, One-Line Install)

System Requirements

Before we start, make sure your environment checks these boxes:

OS: Linux (Ubuntu 22.04+ recommended), macOS, or WSL2
Node.js: 22.0 or higher (hard requirement)
RAM: 512MB minimum, 1GB+ recommended
Network: Internet access (for AI model API calls)

If you don't have Node.js 22+ yet, grab it with nvm:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash
source ~/.bashrc
nvm install 22
node -v  # Should output v22.x.x

Why Node.js 22+?

OpenClaw relies on Node.js 22 features like native WebSocket support and improved ESM modules. Older versions will just throw errors. Trust me on this one.

One-Line Install

Environment ready? Install OpenClaw with a single command:

curl -fsSL https://openclaw.ai/install.sh | bash

This script will:

Detect your system environment and Node.js version
Install OpenClaw globally via npm
Create the default config directory ~/.openclaw/
Generate initial configuration files

After installation, run the onboarding wizard:

openclaw onboard

This interactive guide walks you through basic setup — workspace directory, default model, etc. If you prefer manual config (like me), skip it and edit the config file directly.

Step 2: Configure Your First AI Model (API Keys & 627+ Models via Crazyrouter)

Understanding the Config File

All OpenClaw configuration lives in one file:

~/.openclaw/openclaw.json

Despite the .json extension, it actually supports JSON5 — meaning you can write comments, use trailing commas, and single quotes. Developer-friendly from the start.

Here's a minimal working config:

{
  // OpenClaw minimal config
  agents: {
    defaults: {
      // Working directory for SOUL.md and other persona files
      workspace: "~/.openclaw/workspace",

      // AI model config
      model: {
        primary: "openai/gpt-4o",  // Format: provider/model
      },
    },
  },
}

Workspace Files Explained

Your workspace directory contains several key files that define your AI assistant's "soul":

File	Purpose
`SOUL.md`	Core personality and behavioral guidelines
`AGENTS.md`	Workflow and session rules
`USER.md`	Information about you (the user)
`TOOLS.md`	Tool usage notes and API keys
`IDENTITY.md`	Assistant identity (name, personality, etc.)

Edit these freely to customize your assistant. Write "be concise, no fluff" in SOUL.md and your assistant will keep things short.

Access 627+ Models via Crazyrouter

OpenClaw has built-in providers: openai, anthropic, google, openrouter, deepseek. You can plug in API keys directly.

But if you don't want to juggle multiple API keys, there's a simpler approach — use Crazyrouter as a unified gateway. One API key, 627+ models including GPT-4o, Claude 4, Gemini 2.5, DeepSeek R1, and more.

{
  agents: {
    defaults: {
      workspace: "~/.openclaw/workspace",
      model: {
        // Use Claude via Crazyrouter
        primary: "openai/anthropic/claude-sonnet-4-20250514",
      },
      providers: {
        openai: {
          apiKey: "sk-your-crazyrouter-key",
          baseURL: "https://crazyrouter.com/v1",
        },
      },
    },
  },
}

Multi-Model Fallback Strategy

You can also configure fallback models for automatic switching when the primary is unavailable:

{
  agents: {
    defaults: {
      model: {
        primary: "openai/anthropic/claude-sonnet-4-20250514",
        fallback: "openai/gpt-4o",        // Backup when primary is down
        fast: "openai/gpt-4o-mini",        // Cheap model for simple tasks
      },
    },
  },
}

Your assistant will automatically pick the best model based on task complexity and availability.

Step 3: Connect a Chat Platform (Telegram, WhatsApp, Discord)

Model configured — now let's get your assistant online by connecting it to a chat platform. OpenClaw supports Telegram, WhatsApp, Discord, and more.

Telegram Bot Setup (Recommended)

Telegram is the most recommended platform for OpenClaw — simplest config, most features.

Step 1: Create a Telegram Bot

Find @BotFather on Telegram
Send /newbot and follow the prompts
Copy the Bot Token (looks like 123456:ABC-DEF...)

Step 2: Add to Config

{
  agents: {
    defaults: {
      workspace: "~/.openclaw/workspace",
      model: {
        primary: "openai/anthropic/claude-sonnet-4-20250514",
      },
      providers: {
        openai: {
          apiKey: "sk-your-crazyrouter-key",
          baseURL: "https://crazyrouter.com/v1",
        },
      },
    },
  },
  channels: {
    telegram: {
      token: "your-bot-token",
      allowFrom: ["your-telegram-username"],  // Whitelist — only these users can chat
    },
  },
}

Security Note: Always Set allowFrom

allowFrom is a whitelist. Only listed users can talk to your bot. Without it, anyone can use your bot — and your API credits. Learn from my mistake.

WhatsApp & Discord

WhatsApp uses the WhatsApp Business API:

{
  channels: {
    whatsapp: {
      allowFrom: ["+1234567890"],  // Allowed phone numbers
    },
  },
}

Discord requires an Application and Bot from the Discord Developer Portal:

{
  channels: {
    discord: {
      token: "your-discord-bot-token",
      allowFrom: ["your-discord-user-id"],
    },
  },
}

Same pattern everywhere: get the platform token, add it to config, set the whitelist.

Step 4: Send Your First Message (Gateway & Control UI)

Start the Gateway

Everything configured? Fire up the OpenClaw Gateway:

openclaw gateway start

It starts on port 18789 by default. Check status with:

openclaw gateway status

If you see running, you're good. Port conflict? Change it in the config file.

The Control UI

OpenClaw ships with a web management interface:

openclaw dashboard

Open http://localhost:18789 in your browser. You'll see:

Chat interface: Talk to your AI assistant directly in the browser
Config management: Visual config editor
Logs: Real-time conversation logs and error output
Model switching: One-click model changes

Send Your First Message

Now open Telegram, find your bot, and send:

Hey! Who are you?

If everything's configured correctly, your AI assistant will reply within seconds. Congrats — your private AI assistant is live!

No reply? Troubleshoot in this order:

openclaw gateway status — confirm the service is running
Check the Bot Token in your config
Verify your username is in allowFrom
Check terminal output from openclaw gateway

CLI Quick Reference

Commands you'll use often:

# Service management
openclaw gateway start      # Start the service
openclaw gateway stop       # Stop the service
openclaw gateway restart    # Restart the service
openclaw gateway status     # Check status

# Setup & management
openclaw onboard            # Interactive setup wizard
openclaw dashboard          # Open web management UI

Wrapping Up

That's the whole tutorial: install OpenClaw → fill in the config → start the service. Three steps to your own private AI assistant.

For AI model access, if you don't want to deal with multiple API keys, give Crazyrouter a try. One key for 627+ models — OpenAI, Anthropic, Google, DeepSeek, all the major providers. Transparent pricing, pay-as-you-go, and free credits to get started. For solo devs and small teams, it eliminates the hassle of managing multiple accounts and pairs perfectly with OpenClaw.

Questions? File an issue on OpenClaw GitHub or join the community.

Happy hacking! 🚀

Tokens vs Bytes in AI: What LLMs Actually See When You Type

Jenny Met — Mon, 30 Mar 2026 07:32:20 +0000

You type "你好 Hello" into GPT-5. That's 7 characters. But the model processes it as 2 tokens — and your bill is based on those tokens, not the characters.

Meanwhile, your computer stores that same text as 12 bytes.

So what's the difference between bytes, characters, and tokens? Why does AI use tokens instead of raw bytes? And why does the same sentence cost more in Chinese than in English?

Start at the Bottom: What Is a Byte?

A byte is the smallest unit of data your computer stores. One byte = 8 bits = a number from 0 to 255.

When you save text to a file, your computer encodes each character into bytes using UTF-8:

Character	UTF-8 Bytes	Byte Count	Hex
`H`	72	1	48
`e`	101	1	65
`你`	228, 189, 160	3	e4 bd a0
`好`	229, 165, 189	3	e5 a5 bd
`🚀`	240, 159, 154, 128	4	f0 9f 9a 80

Key pattern:

English letters: 1 byte each
Chinese/Japanese/Korean: 3 bytes each
Emojis: 4 bytes each

The Four Levels: Bytes → Characters → Words → Tokens

Level	"Hello, World"	Count	Description
Bytes	`48 65 6c 6c 6f 2c 20 57 6f 72 6c 64`	12	Raw storage units
Characters	`H e l l o , ␣ W o r l d`	12	Human-readable letters
Words	`Hello, World`	2	Space-separated units
Tokens	`Hello` `,` `World`	3	What AI actually processes

Tokens are not bytes, not characters, and not words. They're sub-word units that balance vocabulary size with sequence length.

Why Not Just Use Bytes or Words?

Problem with bytes: sequences are too long

The computational cost of transformer attention grows quadratically (O(n²)). Using raw bytes would make sequences 3-4x longer, making AI models unaffordably slow.

Problem with words: vocabulary explodes

English has 170,000+ common words. Add technical terms, names, URLs, code, and other languages — you'd need millions of vocabulary entries.

Tokens: the sweet spot

"unbelievable" → ["un", "bel", "ievable"]    (3 tokens)
"tokenization" → ["Token", "ization"]         (2 tokens)
"Hello"        → ["Hello"]                    (1 token)

Short sequences + small vocabulary (~100K-200K) + no unknown words.

How BPE Tokenization Works

Most LLMs use Byte Pair Encoding (BPE):

Split text into individual characters
Count the most frequent adjacent pair
Merge that pair into a new token
Repeat thousands of times until target vocabulary size

See it yourself with tiktoken

import tiktoken

enc = tiktoken.get_encoding("o200k_base")
text = "你好 Hello"
tokens = enc.encode(text)
token_strings = [enc.decode([t]) for t in tokens]

print(f"Text: {text}")
print(f"UTF-8 bytes: {len(text.encode('utf-8'))}")
print(f"Tokens ({len(tokens)}): {token_strings}")

Output: 12 bytes compressed into just 2 tokens!

Different Models, Different Tokenizers

Text	cl100k_base (GPT-4)	o200k_base (GPT-4o/5)	UTF-8 Bytes
Hello, how are you today?	7	7	25
你好，请用中文解释一下什么是token	15	9	47
こんにちは、トークンとは何ですか？	12	10	51

GPT-5's tokenizer is 40% more efficient for Chinese than GPT-4.

Token Costs by Language

Language	Text	Tokens	Bytes	Bytes/Token
English	"Hello, how are you today?"	7	25	3.6
Chinese	"你好，今天怎么样？"	5	27	5.4
Japanese	"こんにちは"	1	15	15.0
Korean	"안녕하세요"	2	15	7.5

Chinese text costs roughly 50% more than English for the same character count.

Compare models before committing

from openai import OpenAI

client = OpenAI(
    api_key="your-crazyrouter-key",
    base_url="https://crazyrouter.com/v1"
)

for model in ["gpt-5", "gpt-5-mini", "deepseek-v3.2", "claude-sonnet-4"]:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Explain tokenization in 2 sentences"}],
        max_tokens=100
    )
    usage = response.usage
    print(f"{model}: {usage.prompt_tokens} in / {usage.completion_tokens} out")

With Crazyrouter, one API key gives you access to 627+ models.

Quick Reference

Property	Bytes	Characters	Words	Tokens
What is it	Raw storage	Human-readable	Space-separated	Sub-word for AI
"Hello"	5	5	1	1
"你好"	6	2	1	1
AI billing?	No	No	No	Yes

Key relationships:

1 English token ≈ 4 characters ≈ 0.75 words
1,000 tokens ≈ 750 English words

Understanding bytes vs tokens is the first step to controlling your AI costs. Full version with more examples: Crazyrouter Blog

Best AI API Gateway for Developers in 2026: 9 Platforms Tested

Jenny Met — Fri, 27 Mar 2026 07:49:23 +0000

Best AI API Gateway for Developers in 2026: 9 Platforms Tested

If you're building anything with AI in 2026, you've probably hit the same wall: managing API keys for OpenAI, Anthropic, Google, and a dozen other providers. Different SDKs, different rate limits, different billing dashboards. It adds up fast.

AI API gateways solve this by sitting between your application and model providers. One endpoint, one API key, unified billing. But the category has exploded — there are now dozens of options, and they solve very different problems.

Some focus on enterprise governance. Others focus on developer simplicity. Some only handle text. Others handle images, video, and audio too.

We tested 9 platforms across six dimensions to help you pick the right one.

How We Evaluated

Dimension	What We Measured
Model Coverage	Number of models, providers supported
Pricing	Cost vs. going direct to providers
API Compatibility	OpenAI / Anthropic / Gemini format support
Multi-Modal	Chat, image, video, audio, music generation
Developer Experience	Time to first API call, documentation quality
Production Features	Fallback, caching, monitoring, rate limiting

Quick Comparison

Gateway	Models	Multi-Modal	Pricing Model	Self-Host	Best For
OpenRouter	343+	Chat only	Pay-per-token (+10-30%)	❌	Community, free models
Portkey	200+ (BYOK)	Chat only	Free 10K req/mo, Pro $49/mo	❌	Enterprise governance
LiteLLM	100+ providers	Chat only	Free (self-host)	✅	Open-source teams
Helicone	BYOK	Chat only	Free 100K req/mo	✅	Observability
Kong AI	BYOK	Chat only	Enterprise pricing	✅	Kubernetes-native teams
Cloudflare AI	Limited	Chat only	Free tier + usage	❌	Edge caching
Bifrost (Maxim)	Major providers	Chat only	Free (self-host)	✅	Raw performance
Crazyrouter	627+	Chat+Image+Video+Audio+Music	Pay-per-token (below official)	❌	Multi-modal, cost savings
TrueFoundry	BYOK	Chat only	Enterprise pricing	✅	Full AI platform

1. OpenRouter — The Community Standard

OpenRouter is the most well-known AI API gateway. It aggregates 343+ models from major providers and has built a strong community around model discovery.

What works:

Largest community and model marketplace
Free models available (with rate limits)
OAuth support for building apps on top
Good documentation and playground

What doesn't:

Prices are 10-30% above official API rates
No image, video, or audio generation
No self-hosting option
Free tier has strict limits

Best for: Developers who want easy model access and don't mind paying a premium. The community and free models make it a good starting point.

2. Portkey — Enterprise LLM Control Plane

Portkey is built for teams that need governance, not just routing. It adds guardrails, prompt management, and cost controls on top of your existing API keys.

What works:

SOC 2 compliant
Prompt versioning and management
Smart routing with automatic fallback
Token-level cost tracking per team

What doesn't:

BYOK only — you still need your own provider keys
Learning curve is steep for simple use cases
Overkill for solo developers or small projects
No multi-modal support beyond text

Best for: Engineering teams running LLMs in production who need audit trails, budget controls, and compliance.

3. LiteLLM — Open-Source Developer Gateway

LiteLLM is the go-to open-source option. It provides a unified OpenAI-compatible API for 100+ providers and is completely free to self-host.

What works:

Truly open-source, no vendor lock-in
Supports 100+ providers including niche ones
Python SDK + proxy server
Active community with frequent updates

What doesn't:

Performance degrades at scale — P99 latency hit 28 seconds at 1,000 concurrent users in independent tests
Requires self-hosting and DevOps effort
YAML configuration doesn't scale well
No built-in UI for non-technical users

Best for: Python teams who want full control and don't need enterprise-scale throughput.

4. Helicone — Observability-First Gateway

Helicone focuses on one thing: making LLM usage visible. It's a proxy that logs every request with token counts, costs, and latency metrics.

What works:

Best-in-class observability dashboard
One-line integration (just change base URL)
Free tier: 100K requests/month
Open-source core

What doesn't:

BYOK — doesn't aggregate models or reduce costs
Limited routing and fallback capabilities
Not a full gateway, more of a logging proxy
No multi-modal support

Best for: Teams that already have provider keys and need visibility into usage, costs, and performance.

5. Kong AI Gateway — Traditional API Gateway + AI Plugins

Kong AI extends the popular Kong API gateway with AI-specific plugins for routing LLM traffic.

What works:

Mature Kubernetes-native ecosystem
Enterprise-grade security and rate limiting
Familiar to platform teams already using Kong
Plugin architecture is extensible

What doesn't:

Treats LLM calls as opaque HTTP requests
No token-level cost visibility
No understanding of prompts or model semantics
No AI-specific routing logic built in

Best for: Platform teams already running Kong who want to add basic AI traffic management without adopting a new tool.

6. Cloudflare AI Gateway — Edge-First Caching

Cloudflare AI Gateway leverages Cloudflare's global edge network to cache and manage AI API traffic.

What works:

Global edge deployment = low latency
Semantic caching reduces redundant calls
Free tier available
Simple setup for Cloudflare users

What doesn't:

Limited model provider support
Basic feature set compared to dedicated gateways
No advanced routing or fallback
No multi-modal support

Best for: Teams already on Cloudflare who want basic caching and rate limiting for AI traffic.

7. Bifrost (Maxim AI) — Performance-First Gateway

Bifrost is a Go-based LLM gateway built for raw speed. In benchmarks, it adds just 11 microseconds of latency at 5,000 requests per second.

What works:

Exceptional performance (11μs overhead)
Open-source and free to self-host
Cluster mode for horizontal scaling
SSO, audit logs, and RBAC included

What doesn't:

Relatively new with a smaller community
Fewer integrations than LiteLLM
No multi-modal support
Documentation is still maturing

Best for: High-traffic, latency-sensitive applications where every millisecond matters.

8. Crazyrouter — Multi-Modal API Gateway

While most gateways focus exclusively on LLM chat, Crazyrouter takes a different approach: one API key for everything — chat, image generation, video generation, audio, and even music.

What works:

627+ models across 15+ providers (largest coverage we found)
Multi-modal: GPT-5, Claude, Gemini for chat + DALL-E, Midjourney, Flux for images + Sora, Kling, Veo for video + Suno for music
Below official API pricing (not a markup — actual savings)
Three SDK formats: OpenAI, Anthropic, and Gemini native — all compatible
Pay-per-use, no monthly fees, no minimum spend

What doesn't:

No self-hosting option
No enterprise governance features (guardrails, prompt management)
Smaller community compared to OpenRouter
No semantic caching at the gateway level

Code example — call GPT-5 in 3 lines:

import openai
client = openai.OpenAI(base_url="https://crazyrouter.com/v1", api_key="sk-your-key")
response = client.chat.completions.create(model="gpt-5", messages=[{"role": "user", "content": "Hello"}])

Generate a video with the same key:

import requests
resp = requests.post("https://crazyrouter.com/v1/video/create",
    headers={"Authorization": "Bearer sk-your-key"},
    json={"model": "kling-v2-6", "prompt": "A cinematic drone shot over Tokyo at night", "duration": 5})
print(resp.json())

Best for: Developers who need access to chat, image, video, and audio models through a single API key — and want to pay less than going direct.

9. TrueFoundry — Full AI Infrastructure Platform

TrueFoundry goes beyond gateway functionality into full AI infrastructure management. It treats models, agents, and services as first-class infrastructure objects.

What works:

Organization-wide AI governance
On-prem and air-gapped deployment support
Model training, fine-tuning, and serving in one platform
Team-level cost attribution and budgets

What doesn't:

Heavy — requires significant setup and commitment
Enterprise pricing (not for individual developers)
Overkill if you just need API routing
Steep learning curve

Best for: Large enterprises that need a complete AI platform with governance, compliance, and multi-team cost controls.

Which AI API Gateway Should You Choose?

The right choice depends on what problem you're actually solving:

Your Need	Best Pick	Why
Enterprise governance & compliance	Portkey or TrueFoundry	Built for audit trails, RBAC, prompt management
Open-source, full control	LiteLLM	Free, self-hosted, 100+ providers
Community + free models	OpenRouter	Largest marketplace, OAuth support
Maximum performance	Bifrost	11μs overhead, Go-based
Best observability	Helicone	One-line setup, detailed logging
Multi-modal + cost savings	Crazyrouter	627 models, chat+image+video+audio, below official pricing
Edge caching	Cloudflare AI	Global CDN, semantic cache
Kubernetes-native	Kong AI	Mature plugin ecosystem
Full AI platform	TrueFoundry	Training + serving + governance

Real Cost Comparison

Here's what 10 million tokens per month actually costs across different approaches:

Model	Direct (Official)	OpenRouter	Crazyrouter
GPT-5 (input)	$12.50	~$14.00 (+12%)	~$6.88 (-45%)
GPT-5 (output)	$100.00	~$112.00 (+12%)	~$55.00 (-45%)
Claude Sonnet 4.6 (input)	$30.00	~$33.00 (+10%)	~$16.50 (-45%)
Claude Sonnet 4.6 (output)	$150.00	~$165.00 (+10%)	~$82.50 (-45%)
Gemini 3 Flash (input)	$0.50	~$0.55 (+10%)	~$0.28 (-45%)

Prices per 10M tokens. Actual savings vary by model. OpenRouter markup estimated from public pricing pages. Crazyrouter pricing from crazyrouter.com/pricing.

For a team spending $500/month on AI APIs, switching from direct provider access to a cost-optimized gateway can save $2,000-3,000 per year.

Frequently Asked Questions

What is the difference between an AI gateway and a traditional API gateway?

A traditional API gateway manages REST and GraphQL traffic with authentication, rate limiting, and routing. An AI gateway adds model-aware capabilities: token-level cost tracking, prompt management, semantic caching, automatic failover between providers, and multi-model routing. Some platforms like Kong bridge both worlds, while others like Portkey and Helicone are purpose-built for AI workloads.

Can I use one API key to access all AI models?

Yes. Gateways like OpenRouter and Crazyrouter provide a single API key that routes to hundreds of models across providers. You don't need separate keys for OpenAI, Anthropic, and Google. The gateway handles authentication with each provider on your behalf.

Which AI API gateway supports video and image generation?

Most AI gateways focus exclusively on LLM chat completions. For multi-modal support (image generation with DALL-E/Midjourney/Flux, video generation with Sora/Kling/Veo, audio with TTS/STT, and music with Suno), Crazyrouter is currently the most comprehensive option with 627+ models across all modalities.

Is OpenRouter the best AI API gateway?

OpenRouter is the most popular and has the largest community, but it's not the cheapest — prices are typically 10-30% above official rates. Whether it's "best" depends on your priorities. For cost savings, gateways with below-official pricing offer better value. For enterprise governance, Portkey or TrueFoundry are stronger. For open-source flexibility, LiteLLM wins.

How much can an AI API gateway save on API costs?

It depends on the gateway. Some (like OpenRouter) charge a markup over official prices — you're paying for convenience, not savings. Others offer below-official pricing and can save 30-50% on the same models. For a team spending $500/month, that's $1,800-3,000/year in savings. Additional savings come from features like semantic caching, which reduces redundant API calls.

Last updated: March 2026. Model counts and pricing are subject to change. We recommend verifying current pricing on each platform's website before making a decision.

Claude Code Installation Guide for macOS: Git, Environment Variables, PATH, and Every Common Fix (2026)

Jenny Met — Wed, 18 Mar 2026 06:18:46 +0000

Claude Code Installation Guide for macOS: Git, Environment Variables, PATH, and Every Common Fix (2026)

A lot of "how to install Claude Code" guides stop too early. They show one install command, maybe one login step, and then move on. Real beginners hit the actual problems after that point:

Claude Code installs, but the command is not found
Claude Code opens, but Git is missing
Git exists, but the repo is not initialized
The shell cannot see the API key or auth variables
Homebrew works on one Mac but not another
Apple Silicon and Intel behave slightly differently
zsh, bash, and GUI terminal apps load different profiles

This guide is written for new users who want the full macOS setup path, not the marketing version.

What Claude Code Needs on macOS

Before you even think about the install command, make sure you understand the dependency chain.

Component	Why It Matters
Terminal app	Claude Code runs in the terminal
Node.js / npm	Claude Code is distributed as a CLI package
Git	Many agent workflows assume a Git repo and Git is part of normal coding workflow
Shell config	Your PATH and environment variables live here
Network access	Login, package install, and model access all require outbound access
API or account auth	Claude Code cannot do much if authentication is missing

If one of these is missing, Claude Code may be installed correctly and still feel broken.

macOS Versions, Intel vs Apple Silicon, and Terminal Choice

This guide assumes:

macOS 12+
Terminal.app or iTerm2
zsh as the default shell (which is standard on modern macOS)

Apple Silicon vs Intel

Most steps are identical, but package paths can differ:

Apple Silicon (M1 / M2 / M3 / M4) Homebrew often lives at /opt/homebrew
Intel Mac Homebrew often lives at /usr/local

That difference matters for PATH problems.

Check your architecture

uname -m

Expected:

arm64 → Apple Silicon
x86_64 → Intel

Step 1: Confirm Your Shell and Profile File

On modern macOS, your shell is usually zsh.

echo $SHELL

Common outputs:

/bin/zsh
/bin/bash

If you see zsh, the most important shell files are usually:

~/.zshrc
~/.zprofile

If you see bash, check:

~/.bashrc
~/.bash_profile

For most Claude Code setup work on macOS, ~/.zshrc is the main file to update.

Step 2: Install Homebrew If You Do Not Already Have It

Homebrew is the cleanest way to install Git, Node.js, and other developer tools on macOS.

Check whether it exists:

brew --version

If you get command not found, install Homebrew:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

After installation, Homebrew may ask you to add it to your shell profile.

For Apple Silicon, that usually looks like:

echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zprofile
eval "$(/opt/homebrew/bin/brew shellenv)"

For Intel:

echo 'eval "$(/usr/local/bin/brew shellenv)"' >> ~/.zprofile
eval "$(/usr/local/bin/brew shellenv)"

Verify:

which brew
brew --version

Step 3: Install Git

A surprising number of new users install Claude Code first and only discover later that Git is missing.

Check Git:

git --version

If Git is missing, install it:

brew install git

Verify again:

which git
git --version

Why Git matters for Claude Code

Even if Claude Code can launch without Git, you will quickly run into problems if you are not in a normal repository workflow:

change review is harder
diffs are harder
rollback is harder
some tools assume a Git repo

If you are starting a new folder, initialize it:

cd ~/Projects/my-app
git init

Optional but recommended:

git config --global user.name "Your Name"
git config --global user.email "you@example.com"

Step 4: Install Node.js and npm

Claude Code depends on a working Node.js/npm environment.

Check what you already have:

node --version
npm --version

If either command fails, install Node.js.

Recommended option: Homebrew

brew install node

Verify:

node --version
npm --version
which node
which npm

Better option for advanced users: nvm

If you switch Node versions often, use nvm instead.

Install nvm:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.2/install.sh | bash

Then load it:

export NVM_DIR="$HOME/.nvm"
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"

Install a stable Node version:

nvm install --lts
nvm use --lts

Verify:

node --version
npm --version

Homebrew vs nvm: which should beginners choose?

Choose Homebrew if you want the simplest path
Choose nvm if you already know you will manage multiple Node versions

If you are brand new, Homebrew is usually less confusing.

Step 5: Install Claude Code

Once Git and Node/npm are ready, install Claude Code.

First check whether it already exists:

which claude
claude --version

If not installed, use npm global install:

npm install -g @anthropic-ai/claude-code

Then verify:

which claude
claude --version

If successful, you should see a version such as:

2.1.76 (Claude Code)

Step 6: Fix PATH Problems If `claude` Is Still Not Found

This is one of the most common macOS issues.

You run:

npm install -g @anthropic-ai/claude-code

It appears to succeed. Then:

claude --version

And you get:

zsh: command not found: claude

That usually means your npm global bin directory is not in PATH.

Find the npm global bin directory

npm config get prefix

A common result is something like:

/opt/homebrew
/usr/local
/Users/you/.npm-global

Then check the bin folder inside it.

Examples:

ls "$(npm config get prefix)/bin"

If claude is there, add it to PATH.

Example PATH fix

echo 'export PATH="$(npm config get prefix)/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc

Then verify again:

which claude
claude --version

Homebrew path note

If npm globals are managed under Homebrew, also make sure Homebrew itself is loaded in your profile.

Step 7: Authenticate and Set Environment Variables

Depending on your workflow, Claude Code may require account login and/or environment variables.

First check whether the CLI offers login

claude --help

Look for auth, login, config, or provider-related commands.

Common environment variable scenarios

You may need one or more of these depending on your setup:

Anthropic key
OpenAI-compatible base URL
proxy variables
custom provider settings

Examples:

export ANTHROPIC_API_KEY="your_key_here"
export OPENAI_BASE_URL="https://crazyrouter.com/v1"
export OPENAI_API_KEY="your_crazyrouter_key"

Persist them in ~/.zshrc:

echo 'export ANTHROPIC_API_KEY="your_key_here"' >> ~/.zshrc
echo 'export OPENAI_BASE_URL="https://crazyrouter.com/v1"' >> ~/.zshrc
echo 'export OPENAI_API_KEY="your_crazyrouter_key"' >> ~/.zshrc
source ~/.zshrc

Important caution

Do not put production secrets into screenshots, shared shell history dumps, or public Git repos.

Step 8: Verify the Whole Environment, Not Just the Install

A proper Claude Code setup check should look like this:

which brew || true
brew --version || true

git --version || true
node --version || true
npm --version || true
claude --version || true

echo $SHELL
pwd
git status || true

You want to confirm:

package manager works
Git exists
Node exists
npm exists
Claude Code exists
shell profile loads
current folder is a usable project

Step 9: First Real Test in a Safe Folder

Create a test project:

mkdir -p ~/Projects/claude-code-test
cd ~/Projects/claude-code-test
git init
printf "# test\n" > README.md

Then try:

claude --help

And if supported by your version, a simple non-destructive prompt.

Start with something harmless:

explain this folder
suggest a README improvement
generate a .gitignore

Do not start with --dangerously-skip-permissions style workflows until the basic environment is stable.

Common macOS Problems and Fixes

1. `command not found: claude`

Cause:

npm global bin not in PATH
install failed silently
shell profile not reloaded

Fix:

npm install -g @anthropic-ai/claude-code
export PATH="$(npm config get prefix)/bin:$PATH"
source ~/.zshrc
which claude

2. `git: command not found`

Cause:

Git not installed
Xcode command line tools not configured

Fix:

brew install git

or if macOS prompts for developer tools, allow that install and re-check.

3. `node: command not found` or wrong Node version

Cause:

Node not installed
nvm not loaded in current shell
old system Node shadowing the right one

Fix:

which node
node --version

If using nvm, make sure the nvm.sh load lines are in your shell config.

4. Homebrew works in one terminal but not another

Cause:

Terminal app loads different startup files
Homebrew shellenv added to the wrong profile file

Fix:

Add Homebrew init to ~/.zprofile
Add PATH exports to ~/.zshrc
restart the terminal fully

5. Environment variables exist in one tab but disappear in another

Cause:

variables were exported only for the current session
not persisted to shell config

Fix:

write them into ~/.zshrc
run source ~/.zshrc
open a new terminal tab and test again

6. Corporate network or proxy blocks install/login

You may need proxy environment variables:

export HTTPS_PROXY="http://proxy.example.com:8080"
export HTTP_PROXY="http://proxy.example.com:8080"

If npm fails, also check:

npm config get proxy
npm config get https-proxy

7. Permission denied during global npm install

Avoid random sudo npm install -g unless you understand the ownership issue.

Better fixes:

use Homebrew-managed Node
use nvm
use a user-owned npm prefix

If permissions are already broken, inspect ownership before changing anything.

Recommended macOS Setup for Beginners

If you want the least confusing setup, use this stack:

Terminal.app or iTerm2
Homebrew
Git via Homebrew
Node via Homebrew
Claude Code via npm global install
zsh with variables saved in ~/.zshrc

That path is not the fanciest, but it is the easiest to debug.

FAQ

Do I need Git before installing Claude Code on macOS?

Strictly speaking, the package install may succeed without Git. In practice, most real Claude Code workflows become painful without Git, so yes, install Git first.

Does Claude Code work on Apple Silicon Macs?

Yes. The main difference is package paths, especially for Homebrew, which usually lives under /opt/homebrew on Apple Silicon.

Should I use Homebrew or nvm for Claude Code dependencies?

For beginners, Homebrew is simpler. For developers who juggle multiple Node versions, nvm is more flexible.

Why is Claude Code installed but still not available in zsh?

Usually because the npm global bin directory is not in PATH, or because your shell profile was not reloaded after installation.

Can I use Claude Code with a gateway instead of direct Anthropic setup?

Yes, if your workflow supports OpenAI-compatible or other configurable endpoints. That is especially useful when you want one key for multiple model families and simpler billing.

Final Take

The real Claude Code setup on macOS is not just one install command. It is a chain:

terminal
Homebrew
Git
Node/npm
Claude Code
PATH
environment variables
real project verification

If you fix those in order, almost every beginner setup issue becomes much easier to diagnose.

Claude Code Installation Guide for Windows: Git, PATH, Environment Variables, PowerShell, WSL, and Full Troubleshooting (2026)

Jenny Met — Wed, 18 Mar 2026 06:18:46 +0000

Claude Code Installation Guide for Windows: Git, PATH, Environment Variables, PowerShell, WSL, and Full Troubleshooting (2026)

Windows users usually hit a different set of Claude Code problems than macOS users.

Not because Claude Code is impossible on Windows, but because Windows has more environment combinations:

Command Prompt
PowerShell
Windows Terminal
Git Bash
WSL
Node installed from MSI
Node installed from winget
Git installed but not added to PATH
environment variables set for one shell but not another

That is why this guide goes slower and explains the setup chain from zero.

Before You Install Anything: Choose Your Windows Path

There are two realistic paths.

Option A: Native Windows setup

Use:

Windows Terminal
PowerShell
Git for Windows
Node.js for Windows
npm global install

This is easier for most beginners.

Option B: WSL setup

Use:

WSL2
Ubuntu or Debian inside WSL
Linux install steps inside WSL

This is often cleaner for developers, but it adds an extra layer. If you are completely new, start with native Windows first unless you already use WSL.

Recommended Beginner Setup

For most new users, I recommend:

Windows 11 or updated Windows 10
Windows Terminal
PowerShell
winget for package installs
Git for Windows
Node.js LTS
Claude Code via npm

Step 1: Open the Right Terminal

Install or launch Windows Terminal if possible.

Then open PowerShell.

Check what shell you are in:

$PSVersionTable.PSVersion

If PowerShell opens and works, stay there for the whole setup. Do not mix PowerShell, cmd, Git Bash, and WSL during the initial install unless you know why.

Step 2: Check Whether `winget` Is Available

winget is the easiest way to install Git and Node on modern Windows.

winget --version

If it works, great.

If it does not:

update App Installer from Microsoft Store
or install packages manually from official websites

Step 3: Install Git

Check first:

git --version

If Git is missing, install it with winget:

winget install --id Git.Git -e --source winget

Then close and reopen PowerShell.

Verify:

git --version
where.exe git

Why Git matters on Windows too

Same reason as on macOS:

repo-aware workflow
diffs
safer edits
version rollback
many developer tools assume Git

If you do not have a repo yet:

mkdir $HOME\Projects\claude-code-test -Force
cd $HOME\Projects\claude-code-test
git init

Optional global identity:

git config --global user.name "Your Name"
git config --global user.email "you@example.com"

Step 4: Install Node.js and npm

Check existing versions:

node --version
npm --version

If missing, install Node.js:

winget install --id OpenJS.NodeJS.LTS -e --source winget

Close and reopen PowerShell again.

Verify:

node --version
npm --version
where.exe node
where.exe npm

Step 5: Install Claude Code

Check whether it already exists:

where.exe claude
claude --version

If not, install it globally:

npm install -g @anthropic-ai/claude-code

Then verify:

where.exe claude
claude --version

Step 6: Fix PATH Problems on Windows

The most common Windows complaint is:

npm install says success
but claude is still not recognized

Typical error:

claude : The term 'claude' is not recognized as the name of a cmdlet, function, script file, or operable program.

First find npm's global prefix

npm config get prefix

Usually this points to a directory whose bin or executable location should be discoverable through PATH.

Check where npm installed claude:

npm list -g --depth=0

Also try:

Get-Command claude -ErrorAction SilentlyContinue

Common fix: reopen the shell

A lot of PATH issues are just stale sessions. Close PowerShell fully, open a new one, and try again.

If PATH is still wrong

Inspect the user PATH:

[Environment]::GetEnvironmentVariable("Path", "User")

And machine PATH:

[Environment]::GetEnvironmentVariable("Path", "Machine")

If the npm global executable location is missing, you can add it through the Windows Environment Variables UI or with PowerShell.

Be careful editing PATH programmatically. Back it up first.

Step 7: Set Environment Variables Correctly

Windows users often set variables in one place and assume every shell will see them. That is not always true.

Session-only variable in PowerShell

$env:ANTHROPIC_API_KEY = "your_key_here"
$env:OPENAI_API_KEY = "your_crazyrouter_key"
$env:OPENAI_BASE_URL = "https://crazyrouter.com/v1"

These only last for the current session.

Persistent user-level environment variables

[Environment]::SetEnvironmentVariable("ANTHROPIC_API_KEY", "your_key_here", "User")
[Environment]::SetEnvironmentVariable("OPENAI_API_KEY", "your_crazyrouter_key", "User")
[Environment]::SetEnvironmentVariable("OPENAI_BASE_URL", "https://crazyrouter.com/v1", "User")

Then close and reopen PowerShell.

Verify:

echo $env:ANTHROPIC_API_KEY
echo $env:OPENAI_API_KEY
echo $env:OPENAI_BASE_URL

Step 8: Understand the Difference Between PowerShell, cmd, Git Bash, and WSL

This matters because variables and PATH can behave differently.

Environment	Good for Beginners?	Notes
PowerShell	Yes	Best native Windows choice
Command Prompt	Okay	Less convenient than PowerShell
Git Bash	Mixed	Works, but adds another shell layer
WSL	Good for developers	Best if you want Linux-like behavior

If you installed Claude Code in native Windows PowerShell, do not test it first in WSL and assume the same environment applies.

WSL has its own package system, paths, shell files, and variables.

Step 9: Optional WSL Path

If you want the cleanest long-term developer environment on Windows, install WSL.

Check WSL:

wsl --status

Install if needed:

wsl --install

Then reboot if Windows asks.

After that, open Ubuntu and treat it like a Linux machine:

install Git inside WSL
install Node inside WSL
install Claude Code inside WSL
set environment variables inside WSL shell files

Do not assume your Windows-side Node install automatically covers WSL.

Step 10: Verify Everything End-to-End

Run these checks:

git --version
node --version
npm --version
claude --version
where.exe git
where.exe node
where.exe npm
where.exe claude

Then create a safe test folder:

mkdir $HOME\Projects\claude-code-test -Force
cd $HOME\Projects\claude-code-test
if (-not (Test-Path .git)) { git init }
"# test" | Out-File README.md -Encoding utf8

And test the CLI with non-destructive commands first.

Common Windows Problems and Fixes

1. `claude` is not recognized

Cause:

npm global executable directory not in PATH
shell not restarted
install failed

Fix:

reopen PowerShell
check npm config get prefix
confirm package exists in global npm list
inspect PATH

2. Git installs but PowerShell still cannot find it

Cause:

terminal session opened before install
PATH not refreshed

Fix:

fully close and reopen terminal
verify with where.exe git

3. Node installs, but npm is missing or broken

Cause:

incomplete install
conflicting old Node version

Fix:

uninstall conflicting Node versions if necessary
reinstall LTS cleanly
verify both node --version and npm --version

4. Environment variable is set in PowerShell but not in another terminal

Cause:

variable was session-only

Fix:

use persistent user-level environment variables
reopen terminal after setting them

5. WSL works but PowerShell does not, or vice versa

Cause:

you set up two different environments

Fix:

decide whether you want native Windows or WSL as your main Claude Code environment
complete setup inside that environment fully

6. Corporate proxy blocks npm install

You may need:

npm config set proxy http://proxy.example.com:8080
npm config set https-proxy http://proxy.example.com:8080

And possibly session variables too.

7. Antivirus or security software interferes

Sometimes security tools interfere with freshly installed CLI tools or scripts.

If install logs look normal but executables do not behave normally, test in a clean terminal, confirm the file exists, and check Windows Security or endpoint protection history.

A Safe Default Windows Setup

If you want the simplest path that is easiest to support, use this exact stack:

Windows Terminal
PowerShell
winget
Git for Windows
Node.js LTS
Claude Code via npm global install
persistent user environment variables

That setup is boring, and that is exactly why it is good.

FAQ

Should beginners use PowerShell or WSL for Claude Code?

If you are new, start with PowerShell. If you already prefer Linux tooling or already use WSL daily, WSL may be cleaner long term.

Why did Claude Code install successfully but still not run?

Most often: stale PATH, wrong shell, or npm installed the package into a location your current terminal is not reading.

Do I need Git before using Claude Code on Windows?

For serious use, yes. Even if the CLI starts without Git, normal coding workflows are much smoother with Git installed and configured.

Where should I store Claude Code environment variables on Windows?

For persistence, set them at the user environment level, not just the current shell session.

Is Git Bash a good place to run Claude Code?

It can work, but for beginners it adds more variables. PowerShell is simpler to document and support.

Final Take

The Windows installation story is not hard because Claude Code itself is hard. It is hard because Windows gives you many overlapping environments.

If you keep the setup consistent — PowerShell, winget, Git, Node, npm, Claude Code, then environment variables — the install becomes much easier to debug and teach.

Crazyrouter vs LiteLLM: Managed Gateway vs Self-Hosted Proxy (2026 Comparison)

Jenny Met — Fri, 13 Mar 2026 01:39:34 +0000

Should you use a managed AI API gateway or self-host your own? I've used both Crazyrouter (managed) and LiteLLM (self-hosted) in production. Here's the real trade-off.

The Core Difference

Crazyrouter: Fully managed. Sign up, get API key, start calling 627+ models. Zero infrastructure.
LiteLLM: Open-source proxy. You deploy it on your own servers, bring your own API keys for each provider.

Feature Comparison

Feature	Crazyrouter (Managed)	LiteLLM (Self-Hosted)
Setup time	2 minutes	30-60 minutes
Models	627+ (included)	100+ (bring your keys)
Infrastructure	None needed	Docker/K8s required
API Keys	1 key for everything	1 key per provider
Billing	Unified, pay-as-you-go	Per-provider billing
Failover	Automatic, built-in	Manual configuration
Latency overhead	~50-150ms (edge nodes)	~5-20ms (local)
Cost	Per-token markup	Free (+ your infra)
Maintenance	Zero	Ongoing
Data privacy	Through Crazyrouter servers	Fully on your infra

When Self-Hosted (LiteLLM) Wins

1. Data sovereignty matters

If you're in healthcare, finance, or government where API traffic can't pass through third-party servers, LiteLLM is the only option. You control the entire data path.

2. You already have provider API keys

If your company has enterprise agreements with OpenAI and Anthropic (with custom pricing), using a managed gateway's markup doesn't make sense. Route through LiteLLM to keep your negotiated rates.

3. Ultra-low latency requirements

LiteLLM running locally adds only 5-20ms. Crazyrouter's edge nodes add 50-150ms. For real-time voice or gaming applications, every millisecond counts.

When Managed (Crazyrouter) Wins

1. You're a small team or indie dev

Managing a proxy server, monitoring uptime, handling provider outages, updating model configs — that's ops work. With Crazyrouter, you get an API key and forget about infrastructure.

2. You need models from many providers

Getting API access to OpenAI + Anthropic + Google + DeepSeek + ByteDance + xAI + Meta means 7+ separate accounts. Crazyrouter bundles them all under one key.

3. You want automatic failover

When OpenAI goes down (and it does), Crazyrouter automatically routes to backup providers. With LiteLLM, you configure this yourself and hope your failover config actually works at 3 AM.

4. Global users

Crazyrouter has edge nodes in 7 regions. If your users are in Tokyo, Seoul, or London, they hit the nearest node. With self-hosted LiteLLM, you'd need to deploy to multiple regions yourself.

Cost Comparison (Real Numbers)

Scenario: 1M tokens/day, GPT-5 + Claude Sonnet 4.6 mix

Cost Item	Crazyrouter	LiteLLM
API tokens	~$15/day	~$12/day (direct pricing)
Infrastructure	$0	~$3/day (server + monitoring)
DevOps time	$0	~$2/day (amortized)
Total	~$15/day	~$17/day

For small-to-medium usage, managed is actually cheaper when you factor in infrastructure and DevOps time. The crossover point where self-hosted becomes cheaper is usually around 5M+ tokens/day.

Setup Comparison

Crazyrouter (2 minutes):

from openai import OpenAI
client = OpenAI(
    api_key="your-crazyrouter-key",
    base_url="https://crazyrouter.com/v1"
)

LiteLLM (30+ minutes):

# litellm_config.yaml
model_list:
  - model_name: gpt-5
    litellm_params:
      model: openai/gpt-5
      api_key: sk-your-openai-key
  - model_name: claude-sonnet-4-6
    litellm_params:
      model: anthropic/claude-sonnet-4-6
      api_key: sk-ant-your-anthropic-key

docker run -p 4000:4000 \
  -v ./litellm_config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:main-latest \
  --config /app/config.yaml

Then configure monitoring, health checks, SSL, failover rules...

My Recommendation

Indie devs / small teams / startups: Use Crazyrouter. Focus on building your product, not managing infrastructure.
Enterprise with compliance requirements: Use LiteLLM. Control your data path.
Hybrid approach: Use Crazyrouter for development and testing (fast iteration), LiteLLM for production (data control).

Try Crazyrouter: crazyrouter.com ($0.20 free credit)
Try LiteLLM: github.com/BerriAI/litellm (open source)

Questions? Telegram community or docs.

Crazyrouter vs OpenRouter in 2026: Which AI API Gateway Should You Choose?

Jenny Met — Fri, 13 Mar 2026 01:39:33 +0000

Choosing between AI API gateways? Here's my hands-on comparison of Crazyrouter and OpenRouter after using both in production for 3 months.

Quick Comparison

Feature	Crazyrouter	OpenRouter
Models	627+ across 102 families	~300 models
API Formats	OpenAI + Anthropic + Gemini native	OpenAI format only
Pricing	Pay-as-you-go, no monthly fee	Pay-as-you-go + markup
Free Credit	$0.20 on signup	Varies
Global Nodes	7 regions	US-based
Image/Video Gen	✅ DALL-E, Flux, Midjourney, Sora, Veo3	Limited
Music Gen	✅ Suno	❌
TTS/STT	✅ Full support	Limited

Model Count: 627 vs 300

The biggest difference. Crazyrouter supports 627+ models across 102 model families, including providers like ByteDance (Doubao, Seedance), Alibaba (Qwen3), and xAI (Grok 4) that OpenRouter doesn't carry.

If you only use GPT and Claude, both work fine. But if you need access to Chinese models, video generation, or music generation, Crazyrouter covers significantly more ground.

API Format Support

This was the dealbreaker for me. OpenRouter only supports OpenAI-compatible format. Crazyrouter supports three formats natively:

OpenAI format:

from openai import OpenAI
client = OpenAI(
    api_key="your-key",
    base_url="https://crazyrouter.com/v1"
)

Anthropic format:

import anthropic
client = anthropic.Anthropic(
    api_key="your-key",
    base_url="https://crazyrouter.com"
)

Gemini format:

import google.generativeai as genai
# Also supported natively

If your codebase uses the Anthropic SDK directly (like many Claude Code users), Crazyrouter lets you keep your existing code structure. With OpenRouter, you'd need to rewrite everything to OpenAI format.

Pricing

Both are pay-as-you-go with no monthly fee. Pricing varies by model — some models are cheaper on Crazyrouter, others on OpenRouter. In my experience, Crazyrouter is generally 10-20% cheaper on popular models like GPT-5 and Claude Sonnet 4.6.

Both are cheaper than going direct to each provider individually, because you avoid the overhead of multiple billing systems.

Latency

Crazyrouter has edge nodes in 7 regions (US, Japan, Korea, UK, Hong Kong, Philippines, Russia). For Asia-Pacific users, this is a significant advantage — my latency from Tokyo was 50-80ms lower than OpenRouter.

If you're US-based only, the difference is negligible.

Beyond Text: Image, Video, Music

This is where Crazyrouter pulls ahead significantly:

Image generation: DALL-E 3, Flux Pro, Midjourney (full suite)
Video generation: Sora 2, Veo3, Kling, Seedance
Music generation: Suno
TTS/STT: Full support across providers

OpenRouter focuses primarily on text completion models. If you need multimodal capabilities through one API, Crazyrouter is the clear choice.

Tool Compatibility

Both work with popular tools:

Tool	Crazyrouter	OpenRouter
Cursor IDE	✅	✅
Claude Code	✅ (native Anthropic)	⚠️ (OpenAI format only)
LangChain	✅	✅
Dify	✅	✅
n8n	✅	✅

When to Choose Crazyrouter

You need 627+ models including Asian providers
You use the Anthropic SDK natively
You need image/video/music generation
Your users are in Asia-Pacific
You want the lowest per-token pricing

When to Choose OpenRouter

You only need popular Western models (GPT, Claude, Gemini)
Your team only uses OpenAI SDK format
You value community features and model rankings
You're already integrated and switching costs are high

Migration

Both support the same base pattern — change base_url and api_key:

# From OpenAI direct to Crazyrouter
client = OpenAI(
    api_key="your-crazyrouter-key",
    base_url="https://crazyrouter.com/v1"
)

Two lines of code. Everything else stays the same.

My Verdict

I use Crazyrouter as my primary gateway because of the triple API format support and broader model coverage. For a team that only uses GPT and Claude via OpenAI SDK, OpenRouter is a solid choice too.

Try both — Crazyrouter gives $0.20 free credit on signup, enough to test your main workflows.

Crazyrouter: crazyrouter.com
OpenRouter: openrouter.ai

Have questions? Join the Crazyrouter Telegram community.

How to Use 627+ AI Models in Cursor IDE with Crazyrouter (2026 Guide)

Jenny Met — Fri, 13 Mar 2026 01:39:03 +0000

Cursor IDE is powerful, but it only supports a few built-in models. Here's how to unlock 627+ models — including GPT-5, Claude Opus 4.6, DeepSeek R1, Gemini 3, and more — through Crazyrouter.

Why Use Crazyrouter with Cursor?

Access models Cursor doesn't natively support (DeepSeek, Qwen3, Llama 4, Grok 4)
Save money — Crazyrouter pricing is often lower than direct API pricing
Switch between models without changing IDE settings
One API key for everything

Setup (3 Minutes)

Step 1: Get a Crazyrouter API Key

Go to crazyrouter.com
Sign up — you get $0.20 free credit immediately
Go to crazyrouter.com/token to generate your API key

Step 2: Configure Cursor

Open Cursor → Settings (⌘/Ctrl + ,)
Go to Models section
Click Add Model or OpenAI API Key
Set:
- API Key: Your Crazyrouter key
- Base URL: https://crazyrouter.com/v1
Add model names you want to use:
- gpt-5
- claude-sonnet-4-6
- deepseek-v3.2
- gemini-2.5-pro
- llama-4-maverick

Step 3: Use Any Model

In the Cursor chat or inline edit, select your model from the dropdown. All 627+ models are available.

Best Models for Coding in Cursor

Task	Recommended Model	Why
General coding	`gpt-5`	Best all-around
Complex refactoring	`claude-opus-4-6`	Deep reasoning
Quick fixes	`gpt-5-mini`	Fast and cheap
Code review	`claude-sonnet-4-6`	Thorough analysis
Budget coding	`deepseek-v3.2`	90% quality, 30% cost
Large context	`gemini-2.5-pro`	1M token context

Cost Comparison (Per 1M Tokens)

Model	Direct Price	Via Crazyrouter
GPT-5	$1.25 / $10.00	Lower
Claude Sonnet 4.6	$3.00 / $15.00	Lower
DeepSeek V3.2	$0.27 / $1.10	Lower
Gemini 2.5 Pro	$1.25 / $10.00	Lower

Check exact prices at crazyrouter.com/pricing.

Pro Tips

1. Use environment variables (zero-config)

export OPENAI_API_KEY=your-crazyrouter-key
export OPENAI_BASE_URL=https://crazyrouter.com/v1

Many tools (including Cursor) auto-detect these.

2. Mix models per task
Use gpt-5-mini for autocomplete (cheap, fast) and claude-opus-4-6 for complex refactoring (thorough, smart).

3. Try DeepSeek for budget coding
DeepSeek V3.2 is ~80% cheaper than GPT-5 and handles most coding tasks well. Great for prototyping.

Also Works With

This same setup works with:

VS Code + Continue extension
Windsurf IDE
Claude Code CLI (use Anthropic base URL: https://crazyrouter.com)
Codex CLI (set OPENAI_BASE_URL)
Aider (set OPENAI_API_BASE)

Troubleshooting

"Model not found" error: Check the exact model name at crazyrouter.com/pricing. Names are case-sensitive.

Slow responses: Try a different model. Some smaller models (gpt-5-mini, deepseek-v3.2) respond faster.

Rate limits: Crazyrouter handles rate limiting across providers. If you hit limits, it auto-routes to backup providers.

Get started: crazyrouter.com — $0.20 free credit on signup.