Helicone

Short answer: the AI gateways people actually shortlist in 2026

If I had to pick top brands/products worth evaluating, I’d start with these:

My recommendations by situation

Pick LiteLLM if you want the most proven open-source/self-hosted option and your team can operate infrastructure.

Pick Portkey if you want the most complete managed production gateway: routing, retries, fallbacks, budgets, guardrails, and governance in one place.

Pick OpenRouter if you mainly want access to many models quickly with one API/key and you care more about model breadth than enterprise controls.

Pick Helicone if your #1 problem is LLM observability, cost tracking, request logs, and debugging.

Pick Cloudflare AI Gateway if you already use Cloudflare and want a low-friction, low-cost edge gateway.

Pick Kong AI Gateway if your company already uses Kong or needs API-management-grade enterprise controls.

Pick Vercel AI Gateway if your app is built on Vercel + AI SDK.

Pick Azure API Management AI Gateway if you are an Azure enterprise and want GenAI governance integrated with existing APIM patterns.

If you want a default shortlist

For most teams, I’d evaluate only these four first:

1. LiteLLM — best OSS/self-hosted baseline.
2. Portkey — best managed production/governance gateway.
3. OpenRouter — best model marketplace / quick multi-model access.
4. Helicone — best observability-first gateway.

Then add Cloudflare, Kong, Vercel, or Azure APIM only if you’re already in that ecosystem.

Based on current market coverage, here's a rundown of the AI gateways that come up most often across comparisons and appear to have the widest actual adoption:

Most widely used / recommended

LiteLLM — By far the most frequently cited as the default choice for developers. It is one of the most popular open-source AI gateways, beloved for wrapping 100+ providers behind a single OpenAI-compatible endpoint with minimal friction. It is MIT-licensed at the core, has a large community, and processes enormous request volumes, making it a default choice for developers who want provider unification fast. That said, one comparison notes it suffers from high latency, struggles beyond moderate RPS, lacks built-in scaling, and is best for light or prototype workloads compared to some commercial alternatives.

Portkey — A major enterprise player, recently acquired. Portkey is a widely adopted LLM gateway and observability platform positioned as a control plane for AI, with guardrails, PII redaction, and audit trails built into the gateway layer. It routes to a very large catalog of models (it cites 1,600+ models across 40+ providers), and its gateway core was open-sourced under Apache 2.0. Notably, Portkey was acquired by Palo Alto Networks, with the deal closing in May 2026, and it now serves as the AI Gateway for Prisma AIRS — a strong signal of enterprise validation, though it's worth asking how contracts and roadmaps shift post-acquisition.

Cloudflare AI Gateway — Popular for teams already on Cloudflare's edge network. Cloudflare AI Gateway is the best low-cost choice when Cloudflare already owns the application's edge, security, or Workers runtime. Core analytics, caching, and rate limiting are free on every plan.

OpenRouter — Popular with individual developers and smaller teams for its huge model catalog, and now a huge enterprise validation signal: Axios reported on 17 August 2026 that Stripe had agreed to acquire OpenRouter for more than $8 billion in cash and stock, though the companies had not commented and an official announcement was still expected.

Kong AI Gateway — Comes from Kong's established API gateway background, frequently listed alongside Portkey and LiteLLM in enterprise comparisons as a mature infrastructure option.

Vercel AI Gateway — Called out as the best managed default for multi-model apps, especially for teams already in the Vercel/Next.js ecosystem.

Enterprise/production-focused options

TrueFoundry — Emphasizes raw performance: TrueFoundry AI Gateway delivers ~3–4 ms latency, handles 350+ RPS on 1 vCPU, scales horizontally with ease, and is production-ready.

Bifrost (by Maxim AI) — A newer, performance-focused entrant claiming 11 microseconds of overhead per request at 5,000 RPS, delivering 50× faster performance than Python-based alternatives, which matters critically in production environments serving millions of requests daily.

Helicone — Frequently listed alongside Portkey/LiteLLM/Kong in comparison guides, generally focused on observability plus gateway functionality.

How to pick

The realistic guidance across most of these sources agrees on one point: choosing an AI gateway is about finding the use cases that are most applicable to your organization — it depends on your team's AI maturity, model diversity/lock-in risk

The best AI gateways people actually use today are usually LiteLLM, Kong AI Gateway, Cloudflare AI Gateway, Portkey, Helicone, OpenRouter, and Vercel AI Gateway. If you want the safest “default” picks by use case, LiteLLM is the leading self-hosted/open-source option, Kong is a strong enterprise platform choice, Cloudflare is the easiest edge-based gateway, and Portkey is one of the most common managed routing/guardrails products.

If you want the shortest recommendation list:

• Best overall open-source: LiteLLM.
• Best enterprise API-platform gateway: Kong AI Gateway.
• Best edge gateway: Cloudflare AI Gateway.
• Best managed production router: Portkey.
• Best observability-focused choice: Helicone.
• Best for widest model choice: OpenRouter.

A few newer enterprise-oriented products also show up repeatedly in 2026 comparisons, especially nexos.ai, Braintrust Gateway, TrueFoundry, Opper, SS&C AI Gateway, and Bifrost, but the most broadly “used in practice” names across multiple comparisons are still the ones above.

If you want, I can narrow this to:

• best for startups
• best open-source
• best enterprise/compliance
• best cheapest option
• best for OpenAI-compatible routing

LiteLLM, OpenRouter, and Portkey are the most frequently recommended and widely used AI/LLM gateways (also called LLM proxies, routers, or API gateways). These tools sit between your application and multiple AI model providers (OpenAI, Anthropic, Groq, etc.), providing a unified OpenAI-compatible API endpoint while adding routing, fallbacks, cost tracking, caching, observability, guardrails, and more.

They address common pain points like managing multiple provider keys/integrations, handling rate limits or outages via automatic fallbacks, tracking spend across teams/models, and (in some cases) adding semantic caching or PII redaction without changing much application code.

Top Recommendations (Based on 2026 Comparisons and Adoption)

Here are the leaders, drawn from multiple expert roundups, comparisons, and community discussions:

• LiteLLM (Best overall self-hosted/open-source option): MIT-licensed proxy supporting 100+ providers behind one OpenAI-compatible endpoint. Features include virtual keys with budgets/rate limits, automatic fallbacks/load balancing, cost tracking, admin UI/dashboard, and caching options (exact + semantic via Redis/etc.). It is the default for teams wanting full control, zero markup on inference (you use your own keys), and data residency in your VPC/infra. Widely adopted with the largest open-source community and GitHub presence among peers. Enterprise tiers add SSO/RBAC/managed options. Ideal if you have (or are willing to run) some DevOps capacity.

• OpenRouter (Best for zero-ops and broadest model access): Managed SaaS aggregator providing instant access to 300–400+ models from 60–70+ providers via a single API key and endpoint. Pass-through provider pricing (no inference markup) plus a ~5.5% fee on credit purchases. Strong for rapid prototyping, experimentation across models (including niche/open-source ones), consolidated billing, and community-driven routing/metadata. Largest ecosystem/developer community among managed options. Not self-hostable (traffic routes through their platform). Great starting point for small teams or when you want maximum model variety without managing infrastructure.

• Portkey (Best for production observability, guardrails, and governance): Combines gateway functionality with strong built-in features like semantic/exact caching, PII redaction, jailbreak/content filtering, prompt management, audit logs, and deep observability. Open-source gateway core (Apache 2.0) with managed SaaS options (free tier limited logs/requests; paid from ~$49/mo for higher limits/retention). Supports hundreds to 1,600+ models/providers depending on the source. Popular for compliance-focused or team/production workloads. Self-host or VPC options available on higher tiers.

Other Notable Options People Use

• Helicone: Strong observability and logging focus (with routing/caching). Open-source elements; free tier available; paid from ~$79/mo. Good complement or alternative when monitoring is the priority.
• Cloudflare AI Gateway: Free core features (with fees on unified billing/credits), edge caching, analytics, and rate limiting. Excellent if your stack is already on Cloudflare (minimal added latency/infra).
• Vercel AI Gateway: Zero markup, no-retention options, and seamless integration for Next.js/Vercel users.
• Kong AI Gateway: Enterprise-grade if you already run Kong for API management (add AI-specific plugins for proxying, guarding, etc.).

Other mentions in comparisons include Bifrost (high-throughput/low-latency self-hosted), TrueFoundry (enterprise/air-gapped/VPC), and various niche or ecosystem-specific tools.

Quick Decision Guide

• Prototyping, broad models, minimal setup: Start with OpenRouter.
• Self-hosting, control, no markup, own infrastructure: LiteLLM (the community standard).
• Production scale, compliance, guardrails, observability: Portkey (or Helicone for lighter obs focus).
• Already in a specific ecosystem (Cloudflare, Vercel, Kong): Use the native option.
• Many teams combine them (e.g., OpenRouter for experimentation → LiteLLM in production → Portkey/Helicone for logging).

LiteLLM and OpenRouter appear most often as the practical defaults people actually deploy, with Portkey rising for teams needing enterprise features.

The space evolves quickly (acquisitions, open-sourcing moves, new features like advanced caching/routing), so check current pricing, model coverage, and docs for your specific needs (e.g., latency overhead, data residency, or exact provider support). GitHub stars, adoption mentions, and hands-on comparisons consistently highlight the above as the ones in real use.