I Tested 6 AI API Gateways in 2026 — Here's My Real-World Comparison

Full disclosure: I'm a developer advocate at TokenBay. I've also been an OpenRouter user since late '24 and have no affiliation with the other gateways listed below. I'm sharing raw numbers and honest opinions — the good, the bad, and the 'meh.'

If you're building anything serious with LLMs in 2026, you've probably already realized: no single provider covers everything.

GPT-5.5 is the best at creative writing. Claude Opus 4.8 wins on coding. Gemini 3.1 Pro gives you the biggest context window. DeepSeek V4 Flash is the cheapest by a mile.

But managing 4 API keys, 4 billing dashboards, and 4 different SDKs gets old fast. That's where AI API gateways come in — a single endpoint that routes to every model.

I've spent the past month kicking the tires on 6 gateways: TokenBay, OpenRouter, AI/ML API, CometAPI, Requesty, and Portkey. Here's what I found.

Why I started using gateways

A few months back, I was running a side project with three agents — a coding agent, a research agent, and a customer support bot. Each used a different model for different tasks. My API key drawer looked like a mess of credentials. My billing was split across 4 providers. My code had if-else chains just to switch between model endpoints.

I needed one API key and one bill. A gateway was the obvious answer.

What to look for in a gateway

Through this process, I settled on 5 dimensions that matter most:

1. Pricing — Are you paying more than direct API rates, or less?
2. Model coverage — Does it have the models you actually need?
3. Latency — Does the extra hop matter for your use case?
4. Developer experience — Docs, SDKs, how fast can you go from signup to first API call
5. Extra features — Caching, routing, analytics, fallbacks

The contenders

1. OpenRouter

The biggest gateway by model count — 315+ models. It's been around the longest and has the most community traction.

• Pricing: Passes through official rates + a ~5.5% credit purchase fee. No discounts on the models themselves.
• Model coverage: Excellent. You'll find almost everything here, including niche open-source models.
• Latency: Very good. They're well-provisioned with multiple upstream providers for popular models.
• DX: Solid docs, OpenAI-compatible, plenty of community examples.
• Pros: Largest selection, reliable, good community
• Cons: Credit fee adds up at scale, no discounts, no built-in caching

Best for: When you need access to everything and don't want to set up individual provider accounts.

2. TokenBay

The newest player, but where I'm currently landing for most of my traffic.

• Pricing: Up to 40% below official rates across most popular models. No platform fee on top.
• Model coverage: Good — GPT-5 series, Claude (Opus 4.7/4.8, Sonnet 4.6), Gemini 2.5/3 series, DeepSeek, Qwen, and more. Not as broad as OpenRouter, but covers all the models a production app actually needs.
• Latency: Comparable to direct API calls in my experience. The extra hop is barely noticeable.
• DX: OpenAI-compatible. Drop-in replacement — change your base URL and you're done.
• Free credits: 500 free credits to start, no credit card needed to try.
• Pros: Best pricing, simple, no platform fee
• Cons: Newer — smaller community, fewer integrations (yet)

Best for: Cost-sensitive production workloads where every cent counts.

3. AI/ML API (aimlapi.com)

A well-funded gateway with 400+ models covering chat, image, video, voice, music, 3D, and OCR.

• Pricing: Competitively priced, with discounts on many models compared to official rates.
• Model coverage: The broadest in this comparison — also covers multimodal and generative media models.
• Latency: Good overall, though some less popular models can have cold-start delays.
• DX: Clean docs, SDKs for Python/JS, OpenAI-compatible.
• Free tier: Starts from $20 prepaid.
• Pros: Massive model selection, covers non-text models (image, video, audio)
• Cons: Dashboard could be more polished, pricing isn't always the most transparent

Best for: Teams that need multimodal models (image, video, audio) alongside text LLMs under one API.

4. CometAPI

A straightforward gateway focused on competitive pricing.

• Pricing: ~20% below official on many models. Clear, upfront.
• Model coverage: Decent — covers major providers but fewer niche models than OpenRouter.
• Latency: Fine for most use cases.
• DX: Simple, clean. OpenAI-compatible. Gets out of your way.
• Pros: Clear pricing, no hidden fees
• Cons: Fewer extra features, smaller community

Best for: Teams that want a simple discount gateway without bells and whistles.

5. Requesty

A managed gateway that positions as a "LiteLLM alternative" — more features but hosted.

• Pricing: 5% markup on base model costs.
• Model coverage: 400+ models across 30+ providers.
• Latency: Very good — they focus on infrastructure quality.
• DX: Excellent. One API key for everything, detailed analytics dashboard, routing and caching built in.
• Extra features: Smart routing, caching, EU data residency, SSO on enterprise.
• Free tier: 200 requests/day on free models, no credit card.
• Pros: Feature-rich, good for teams needing governance and observability
• Cons: Premium pricing — you pay for the extras

Best for: Teams that need managed infrastructure with routing, caching, and governance — not just a proxy.

6. Portkey

More of an AI gateway + observability platform than a pure proxy. It's open-source with a managed tier.

• Pricing: Managed plan starts at a monthly fee + per-request usage. Self-hosted open source is free (but you run it).
• Model coverage: Supports all major providers through its gateway mode.
• Latency: Good for managed, depends on your infra for self-hosted.
• DX: Strong — especially if you need observability (logs, traces, analytics) alongside routing.
• Extra features: Comprehensive observability, A/B testing, guardrails.
• Pros: Open-source core, excellent observability, fallback policies
• Cons: Self-hosting adds ops overhead; managed costs more than pure proxies

Best for: Teams that need deep observability and are willing to pay for it.

Latency: Does the extra hop matter?

Short answer: not really, for most use cases.

In practice, the gateway overhead is barely noticeable for chat applications and most API workflows. The caveat: if you're doing real-time streaming at massive scale, every ms counts — but you already know who you are.

What I ended up using

For my own projects, I'm currently running a dual-gateway setup:

• TokenBay for my production workloads where I care most about cost
• Requesty for projects where I need caching, analytics, and routing policies

The nice thing about OpenAI-compatible gateways: switching between them takes one environment variable change. No code changes.

Key takeaways

1. Don't pay the platform fee. Some gateways add 5% on top of model costs. That's money you don't need to spend — use a gateway that discounts instead.
2. Gateway latency is mostly a solved problem. Unless you're building HFT-level real-time systems, you won't notice the extra hop.
3. Model coverage matters less than you think. You don't need 400 models. You need the 10-15 good ones. Focus on quality of service for the models you'll actually use.
4. Free credits are your friend. Most gateways offer credits to start — test before you commit.

Try them yourself

If you're currently managing multiple API keys and wondering if a gateway is worth it — it probably is. Start with whichever gives you the best pricing for the models you actually use, and swap if you need different features later.

TokenBay is offering 500 free credits to try — no credit card needed — at tokenbay.com.

Happy to answer questions about any of these — I've run enough curl commands against each one to have opinions.