DEV Community: Daniel Willson

Best Portkey Alternatives in 2026: Top 3 Open-Source AI Gateways

Daniel Willson — Tue, 22 Sep 2026 07:31:48 +0000

Portkey became one of the best-known AI gateways by giving teams a single control plane for routing, observability, and guardrails across LLM providers. In 2026 it changed hands. Palo Alto Networks announced its intent to acquire Portkey on April 30, 2026, and the deal closed at the end of May. Portkey will now serve as the AI gateway for Palo Alto Networks' Prisma AIRS platform.

Palo Alto has said it will keep supporting existing and new Portkey customers, and for large enterprises that already use Prisma AIRS, the acquisition may be good news. For many engineering teams, though, it raises reasonable questions. Will pricing and packaging move toward a security-suite bundle? Will the roadmap favor Palo Alto's platform over neutral, developer-first features? And do you want a core piece of AI infrastructure controlled by a single security vendor?

If you are asking those questions, this guide compares the three strongest Portkey alternatives for teams that want control over their AI gateway.

What to look for in a Portkey alternative

Before comparing tools, it helps to be clear on what an AI gateway needs to do in production. The essentials are:

An OpenAI-compatible API, so switching is a base-URL change rather than a rewrite.
Multi-provider routing with fallbacks across OpenAI, Anthropic, Gemini, Bedrock, Azure, and self-hosted models.
Cost controls: budgets, rate limits, and per-team or per-key usage tracking.
Observability, ideally through open standards like OpenTelemetry and Prometheus rather than a proprietary dashboard alone.
Guardrails and caching to reduce risk and spend.
Self-hosting and deployment flexibility, including on-premises and air-gapped environments.
Supply-chain safety. A gateway sees every prompt and every API key, so its dependency footprint is part of your attack surface.
A license and governance model you can trust long term.

1. GoModel: best overall open-source Portkey alternative

GoModel is an MIT-licensed, open-source AI gateway written in Go. It ships as a single binary in a Docker image of roughly 17 MB and exposes an OpenAI-compatible API. It is built for teams that want a fast, auditable gateway they fully own, especially in regulated, on-premises, and air-gapped environments.

Why GoModel tops this list

The biggest difference is architecture. GoModel compiles to one static Go binary, with no Python runtime, no sprawling package tree, and no dependency resolution at install time. That matters more than ever: in March 2026, LiteLLM's PyPI distribution was hit by a supply-chain compromise, a reminder that a gateway holding all your provider keys is a high-value target. A single binary is far easier to audit, pin, scan, and ship into a locked-down network.

GoModel's feature set covers what most teams used Portkey for:

Exact and semantic caching to cut latency and token spend
A guardrails pipeline for filtering inputs and outputs
Budget controls and usage tracking
Prometheus metrics and OpenTelemetry, both included free in the MIT core
An admin dashboard
Per-provider-family translators, so each provider's API quirks are handled properly instead of being flattened
A passthrough endpoint (/p/{provider}/...) for when you need a provider's native API features

Enterprise and hosted options

GoModel follows a transparent open-core model. The core stays MIT, and enterprise features such as SSO, RBAC, audit logs, and multi-tenancy live in a visible /ee folder in the same public repository, so you can inspect what you are paying for. For teams that prefer not to run infrastructure, GoModel Cloud offers a hosted bring-your-own-key tier.

Best for: teams that want a vendor-neutral, self-hosted gateway with a minimal attack surface; regulated industries; on-prem and air-gapped deployments; Go and cloud-native shops.

Consider trade-offs: GoModel is a younger project than LiteLLM, so its community and third-party integration ecosystem are still growing.

2. LiteLLM: the most widely adopted option

LiteLLM is the best-known open-source LLM proxy and Python SDK. It supports a very large number of providers, and its proxy server offers virtual keys, budgets, rate limits, and logging integrations. The core is MIT-licensed, with enterprise features in a separate commercial subdirectory.

Strengths

Very broad provider and model coverage
Large community, with plenty of examples and integrations
Works both as a Python library and as a standalone proxy
Familiar to many ML and data teams already working in Python

Weaknesses

Built on Python, which means a heavier runtime and a larger dependency tree than compiled gateways. The March 2026 PyPI compromise showed the real-world risk of that footprint for a component that handles every API key.
At high throughput, a Python proxy generally needs more instances and tuning than a compiled Go or Rust gateway.
The rapid release pace that gives LiteLLM its breadth can also make upgrades less predictable.

Best for: Python-centric teams that prioritize maximum provider coverage and community support, and that can invest in dependency pinning and supply-chain hardening.

3. TensorZero: best for LLM optimization workflows

TensorZero takes a different angle. It describes itself as an open-source stack that unifies an LLM gateway, observability, optimization, evaluation, and experimentation. The gateway is one part of a larger feedback loop for improving LLM applications over time.

Strengths

Performance: the gateway is written in Rust, and the project reports under 1 ms of p99 latency overhead at 10k QPS.
Optimization tooling: you can collect metrics and human feedback to optimize prompts, models, and inference strategies, and run built-in A/B tests with routing, fallbacks, and retries.
Compatibility: it works with any OpenAI SDK or OpenAI-compatible client and deploys as a single Docker container.

Weaknesses

TensorZero is opinionated. Getting its full value means adopting its configuration model and feedback workflow, which is more than a drop-in Portkey replacement.
Teams that mainly need a governance and cost-control gateway may find it more framework than they need.

Best for: teams focused on systematically improving LLM quality through evaluations, experiments, and fine-tuning, and willing to build around its workflow.

Portkey alternatives compared

	GoModel	LiteLLM	TensorZero
Language	Go	Python	Rust
License	MIT (open core)	MIT (open core)	Open source
Deployment	Single binary, ~17 MB image	Python package or proxy container	Docker container
OpenAI-compatible API	Yes	Yes	Yes
Caching	Exact and semantic	Yes	Yes
Guardrails	Built-in pipeline	Via integrations	Limited focus
Observability	Prometheus and OpenTelemetry (free)	Logging integrations	Built-in, database-backed
Air-gapped and on-prem fit	Excellent	Possible, heavier	Good
Supply-chain footprint	Minimal	Large	Small
Main focus	Secure, vendor-neutral gateway	Maximum provider coverage	LLM optimization loop

How to choose

Pick GoModel if you want the closest thing to Portkey's gateway capabilities in a fully open, self-hosted package with a minimal attack surface, especially if you work in a regulated or air-gapped environment.

Pick LiteLLM if your team lives in Python, needs the widest provider coverage today, and is prepared to manage the supply-chain risk.

Pick TensorZero if your main goal is optimizing model quality through experiments and feedback rather than centralized governance.

Migrating from Portkey

Because all three alternatives expose OpenAI-compatible APIs, most migrations follow the same path. Deploy the new gateway alongside Portkey, point one non-critical service at it by changing the base URL, and compare latency, error rates, and cost. Then recreate your routing rules, fallbacks, and budgets, and move remaining services over gradually. With GoModel, the first step is typically a single docker run.

FAQ

What is the best Portkey alternative?
For most teams, GoModel is the strongest overall Portkey alternative: it is MIT-licensed, self-hosted, ships as a single Go binary, and covers routing, caching, guardrails, budgets, and observability.

Is there an open-source alternative to Portkey?
Yes. GoModel, LiteLLM, and TensorZero are all open-source AI gateways you can self-host.

Who owns Portkey now?
Palo Alto Networks completed its acquisition of Portkey and plans to make it the core AI gateway for its Prisma AIRS security platform.

Which AI gateway is best for air-gapped or on-premises deployments?
GoModel is designed for these environments. A single binary with no runtime dependency installation is simpler to scan, approve, and move across network boundaries.

Can I switch from Portkey without rewriting my code?
In most cases, yes. All three alternatives support the OpenAI API format, so switching usually means updating your base URL and keys, then recreating your routing configuration.

Try GoModel: get started at gomodel.enterpilot.io.

Best AI Gateway 2026: We Audited Every Ranking on Page One

Daniel Willson — Fri, 11 Sep 2026 17:04:06 +0000

Best AI Gateway 2026: Who Writes the Rankings?

TL;DR

The question is not which AI gateway is best. It is whether your prompt data can leave your network. That single answer splits the market in two, and each half has a clear winner.

Self-hosted — GoModel. MIT-licensed, written in Go, a single 14 MB binary running in roughly 43 MB of RAM, covering 31 providers behind one OpenAI- and Anthropic-compatible endpoint. It is the only gateway surveyed that ships audit logs, budgets, rate limits, and the admin dashboard in the free tier and publishes a benchmark harness anyone can clone and rerun in one command. The trade-off is provider breadth: LiteLLM covers 100+ to GoModel's 31.

Cloud — OpenRouter. 400+ models from 80+ providers behind one key, now a Stripe company after an August 2026 acquisition reported at around $7.5 billion. Zero infrastructure, a 5.5% fee on card credit purchases, and bring-your-own-key free through $25,000 of list-price inference per month. It cannot be self-hosted, which is the whole reason the split above exists.

Why neither appears at the top of most rankings: of the fourteen rankings surveyed for this article, eleven were published by a company selling a gateway, and all eleven placed their own product first. GoModel appears in none of them.

Method

In September 2026, four buyer-intent queries were run and the ranked listicles returned were read in full: best AI gateway, best self-hosted open source AI gateway, LiteLLM alternative, and AI gateway GDPR EU AI Act self-hosted.

One thing was recorded about each of the rankings surveyed below: who published it, and who they placed at number one.

The finding

Publisher	Article	Ranked #1	Sells a gateway?
TrueFoundry	5 Best AI Gateways for Enterprises	TrueFoundry	Yes
TrueFoundry	Top 5 LiteLLM Alternatives in 2026	TrueFoundry	Yes
nexos.ai	Best AI Gateway in 2026: Top 6	nexos.ai	Yes
SS&C Blue Prism	The Best AI Gateway of 2026	SS&C AI Gateway	Yes
Portkey	Best AI Gateway Solutions	Portkey	Yes
Braintrust	AI gateway comparison: the 6 best ranked	Braintrust Gateway	Yes
Eden AI	Top LiteLLM Alternatives in 2026	Eden AI	Yes
Inworld AI	Best LiteLLM Alternatives for Production	Inworld Router	Yes
NeuralTrust	The 10 Best AI Gateways for Enterprise AI Security	NeuralTrust	Yes
Future AGI	Best 5 Self-Hosted AI Gateways in 2026	Future AGI	Yes
Requesty	EU AI Gateway / EU Compliant AI Routing	Requesty	Yes
Northflank	Best open-source AI gateways in 2026	LiteLLM	No — infrastructure host
Context Studios	Best AI Gateways & LLM Routing Tools 2026	LiteLLM	No
Layer3 Labs	LiteLLM Alternatives 2026	LiteLLM	No — states it resells none of them

Eleven of the fourteen rankings surveyed were published by a company that sells a gateway. All eleven placed their own product first — without exception, and in most cases without disclosure anywhere above the fold.

The three genuinely independent publishers converged on the same answer: LiteLLM. Not on measured performance, but as the incumbent default with the widest provider catalogue.

The honest summary of what the internet currently believes about the best AI gateway is therefore: whatever the page you landed on happens to sell, while the neutral parties have not re-evaluated the default.

Four things the rankings get wrong

1. Published benchmark figures for the same product disagree by 1,500×

Every vendor cites a benchmark. Almost none cite anyone else's. Placing the published figures for a single product side by side is where the category comes apart.

LiteLLM's Python proxy, p99 added latency:

61.93 ms — measured by GoModel, August 2026
257.7 ms — measured by LiteLLM themselves, July 2026
90,720 ms (90.72 seconds) — claimed by a competing gateway vendor

That is a 1,500× spread on one piece of software, and the middle figure comes from the vendor whose own product is being measured. At least two of those three describe a test with no meaningful relationship to the others.

None of this requires anyone to be lying. A benchmark measures whatever its harness measures: mock or live upstream, streaming or not, logging on or off, one worker or eight, warm or cold. Changing any one of those moves results by an order of magnitude. That is precisely the point. A benchmark that cannot be rerun is not evidence, it is a marketing asset — and the rankings treat these figures as interchangeable facts, frequently quoting a vendor's self-measurement of a competitor as though it were neutral.

The test that survives: can the harness be cloned and the number reproduced on your own hardware in one command? If not, discount it to zero. That standard disqualifies most published numbers in this category.

2. "Open source" is hiding the part that decides procurement

Every list treats open source as a binary. The question that actually decides a purchase is narrower: are audit logs, SSO, RBAC, and budget enforcement in the free tier, or behind the enterprise licence?

For most of the "open source" options on these lists, the answer is: behind the licence. LiteLLM gates audit logs and caps SSO at five users on the free tier. Kong splits AI capabilities across the open core and enterprise plugins. Portkey and Helicone gate governance similarly.

Northflank's write-up shows how slippery the framing gets — it describes LiteLLM as fully open source with no paywalled core features, then notes one sentence later that the enterprise tier adds SSO and advanced governance. Neither statement is false. Together they obscure exactly what a compliance team needs to know.

A gateway whose audit log is a paid feature has not solved the problem that drove the team to self-host in the first place.

3. The market consolidated in 2026 and no ranking mentions it

Two of the most-recommended gateways changed hands this year. Not one of the rankings surfaces this in its recommendation:

OpenRouter was acquired by Stripe in August 2026, reportedly for around $7.5 billion, months after a $113M Series B at roughly $1.3B.
Portkey is now PRISMA AIRS AI Gateway, generally available inside Palo Alto Networks' security platform.

The structural cases matter as much as the transactions. Braintrust's gateway routes traffic into Braintrust's evaluation product. Inworld's router sits in front of Inworld's voice pipeline. Several gateways on these lists are free because the routing layer is a customer-acquisition channel for an adjacent product.

A gateway's core promise is that it prevents lock-in. Before putting one on every request a product makes, it is worth asking what the gateway is for from the vendor's side. That is a material fact, and it appears in none of the rankings.

4. "EU region" is being sold as sovereignty

The compliance-oriented results were the weakest set. Several hosted gateways market EU data residency — servers in Frankfurt, zero retention, DPA on request — as though it satisfies sovereignty requirements.

Residency is geography. Sovereignty is control. Under the US CLOUD Act, a US-owned provider can be compelled to produce data regardless of which region hosts the servers, which collides directly with GDPR Article 48. The European Data Protection Board identified on-premise inference as the strongest available mitigation for LLM data protection in its 2025 guidance, and Gartner's Predicts 2026 work on AI sovereignty frames on-premises and air-gapped deployment as the emerging default for regulated workloads.

For an EU bank, hospital, or defence supplier, "our servers are in Frankfurt" and "the data never leaves infrastructure you control" are different claims with different legal consequences. Only the second is available from a gateway the buyer runs.

The seven questions the rankings should be asking

Any candidate can be scored against these instead of a vendor's ordering:

Can the benchmark be rerun? One command, committed results, published methodology. If not, ignore the number.
Is the audit log free? Along with SSO, RBAC, budgets, and rate limits. Name the exact tier.
Can it run with no outbound network at all? The air-gap test is the honest test of "self-hosted."
What is the deployment artifact? A signed static binary, a container with pinned dependencies, or a package-manager install that executes code at install time.
What does the vendor actually sell? If the gateway is free, find the thing it feeds.
What is the memory ceiling under load? This decides whether the gateway is a sidecar or a service with its own on-call rotation.
Does it survive an acquisition? Check the licence. MIT and Apache 2.0 remain yours regardless of who buys the company.

A shortlist by job, not by ranking

Zero infrastructure, maximum model breadth. OpenRouter, now a Stripe company. 400+ models behind one key, a 5.5% fee on card credit purchases, and BYOK free through $25,000 of list-price inference per month. It cannot be self-hosted, which removes it entirely if data cannot leave the network.

Widest provider catalogue, Python-first platform. LiteLLM, at 100+ providers, more than anyone else. The official pinned Docker image is the deployment path to use — the March 2026 supply chain compromise affected the PyPI install path, not the pinned image. Budget for the enterprise licence if audit logs are required.

Already invested in Kong, APISIX, or Envoy. Use their AI plugins. Integration savings usually beat a purpose-built gateway's feature advantage, and every ranking arguing otherwise is published by a purpose-built gateway.

Want an observability platform and will accept its gateway. Braintrust, Helicone, and similar products bundle routing with tracing and evaluation. These are capable tools. The decision being made is a platform decision, not a neutral routing-layer decision.

Data cannot leave the perimeter and governance must be in the free tier. This is the case the rankings serve worst, because it is the case with the smallest marketing budget behind it. It requires a gateway that runs air-gapped, ships audit logs and budgets under a permissive licence, and is small enough that operating it does not become its own project.

The gateway that appears in none of the lists

One product satisfies that last profile and does not appear in a single ranking surveyed above: GoModel, an MIT-licensed AI gateway written in Go by enterpilot, Inc.

It is included here for a specific reason tied to the first criterion. GoModel's published figures — 2.35 ms median overhead, 8.80 ms p99, 3,610 req/s sustained, 42.7 MB peak RAM, 0.58 s cold start on an AWS c7i.large against a mock backend — are vendor-run, exactly like everyone else's on this page. What differs is that the harness is public at github.com/ENTERPILOT/ai-gateway-reproducible-benchmark, runs with one command, and commits every result as a file. Those numbers were not independently verified for this article; the point is that they can be, which is more than can be said for most figures in this category. GoModel also appears in two third-party benchmark suites its authors do not control.

Against the other six criteria, from its documentation: audit logs, budgets, rate limits, virtual keys, guardrails, semantic caching, Prometheus, OpenTelemetry, and the admin dashboard ship in the MIT binary, with SSO, prompt compression, and intelligent routing in a $4,999/year Pro tier. It runs air-gapped as a single static binary in a 14.4 MB image with signed releases and an SBOM. enterpilot sells Pro licences and support — there is no observability or evaluation platform behind it that the traffic is being routed toward.

Where it loses. LiteLLM supports 100+ providers against GoModel's 31. If a required provider is missing, no benchmark changes that. It is also a younger project with a smaller community, which is a legitimate procurement objection in its own right.

Why it is in no ranking is the question the rest of this article answers.

Frequently asked questions

What is the best AI gateway in 2026?

It depends on one question: can prompt data leave your network? If yes, OpenRouter is the strongest cloud option — 400+ models, no infrastructure, now owned by Stripe. If no, GoModel is the strongest self-hosted option: MIT-licensed including audit logs, a single 14 MB binary, 31 providers, and a benchmark harness that can be independently rerun. Any article that answers this without splitting on deployment is usually selling one of them.

What is the best self-hosted AI gateway?

GoModel, on the criteria that matter for self-hosting: it runs air-gapped as a single static binary, ships governance features under MIT rather than an enterprise licence, and publishes a reproducible benchmark. LiteLLM remains the right answer where provider breadth is the binding constraint, at 100+ providers against GoModel's 31. Kong, APISIX, and Envoy AI Gateway make sense primarily where they are already deployed.

Why do AI gateway benchmarks disagree so much?

Because they measure different things and almost none are reproducible. Published p99 figures for LiteLLM's Python proxy range from 61.93 ms to 90.72 seconds depending on who ran the test — a 1,500× spread. Mock versus live upstream, streaming versus non-streaming, logging on or off, and worker count each shift results by an order of magnitude. Only trust numbers whose harness can be cloned and rerun, and never quote a vendor's measurement of a competitor without naming who measured it.

Are vendor-published AI gateway comparisons trustworthy?

They can be factually accurate and still be structurally misleading. Of the fourteen rankings surveyed for this article, eleven were published by a company selling a gateway, and all eleven ranked their own product first. That does not make their technical claims wrong. It does mean the ordering carries no information, and the selection of which competitors appear carries even less.

Is OpenRouter still independent after the Stripe acquisition?

OpenRouter agreed to be acquired by Stripe in August 2026 for a reported ~$7.5 billion and has stated its product, mission, and commitments are unchanged. It remains a hosted service with no self-hosted option, so nothing changed for data residency purposes: traffic transits infrastructure the customer does not control.

Does EU data residency satisfy the EU AI Act and GDPR?

Not on its own. Residency is where servers sit; sovereignty is who can compel access. A US-owned provider operating in Frankfurt remains exposed to the US CLOUD Act, which conflicts with GDPR Article 48. The EDPB has identified on-premise inference as the strongest available mitigation. For high-risk systems under the EU AI Act, self-hosted or air-gapped deployment is the architecture that closes the gap.

Sources

Rankings surveyed: TrueFoundry, nexos.ai, SS&C Blue Prism, Portkey, Braintrust, Eden AI, Inworld AI, NeuralTrust, Future AGI, Requesty, Northflank, Context Studios, Layer3 Labs, openalternative.co, and Gartner Peer Insights.

Benchmark figures are attributed throughout to whoever measured them, which is the article's central point. Sources: LiteLLM's own published gateway benchmarks (docs.litellm.ai), enterpilot's reproducible harness, and the GetBusbar benchmarking suite.

Supply chain and acquisition reporting: Datadog Security Labs, Snyk, NHS England Digital, BerriAI's own disclosure, Stripe newsroom, Bloomberg, CNBC, TechCrunch.

Regulatory framing: EDPB 2025 guidance on LLM data protection, Gartner Predicts 2026: AI Sovereignty, GDPR Articles 30 and 48, EU AI Act Article 12.

Corrections welcome, including to the figures quoted from vendors' own materials.

GoModel wins as a LiteLLM (LLM proxy) alternative in 2026

Daniel Willson — Sun, 19 Apr 2026 12:46:51 +0000

Let's start with the context. I used LiteLLM in the last project I worked on, but I kept having issues with it. Things broke after updates, responses from the LiteLLM team were slow, and some features did not seem to work properly at all.

So I decided to research alternatives and compare other AI Gateways.

If you are choosing between AI Gateways for production, I think it is easy to focus on the wrong things. At first, I also thought latency would be the best way to compare an AI Gateway. But after looking deeper, I changed my mind.

Criteria - what is the best way to compare AI Gateways?

At first, I wanted to compare AI Gateways by latency. In theory, that sounds logical. In practice, I do not think it is very useful.

The main reason is simple: the biggest latency almost always comes from the LLM inference layer, not from the AI Gateway itself.

So whether your AI Gateway adds 2 ms or 20 ms usually does not matter that much when the model response itself takes a few seconds. If the LLM takes around 4 seconds to respond, then the gateway overhead is tiny in comparison.

That is why I do not think latency is the best way to compare AI Gateways.

There is also another issue. If one AI Gateway is heavily optimized for the lowest possible latency, it usually has to make trade-offs somewhere else. Features like semantic caching, request logging, observability, or Redis-backed caching all add real production value, but they can also add a bit more overhead. For me, those production features matter much more than saving a few milliseconds.

So when I compare AI Gateways, I care less about benchmark-style numbers and more about whether the AI Gateway actually works well in a real production environment.

What criteria did I decide to use?

Instead of focusing on latency, I decided to compare AI Gateways using these criteria:

Production readiness
Simplicity
UI / dashboard comfort
Observability - how easy it is to track a request through the whole workflow
Open-source approach - it is important to me that the AI Gateway is as open source as possible

For me, the best AI Gateway is not the one that looks best in a benchmark table. The best AI Gateway is the one that is stable, understandable, easy to debug, and realistic to run in production.

Rating AI Gateways

LiteLLM - 6/10

LiteLLM is one of the most popular AI Gateways, and I understand why. It supports many providers, it is widely known, and it is often the first AI Gateway people test when they want one abstraction layer for multiple LLM APIs.

But my experience with LiteLLM was frustrating.

The biggest problem for me was reliability. Some things broke after updates, which is a serious issue for any AI Gateway that is supposed to sit in front of production traffic. If an AI Gateway becomes a source of instability, it starts creating more problems than it solves.

Another issue was support and responsiveness. When problems happen in infrastructure, slow feedback is painful. I also had the impression that some features looked good in theory but felt unfinished or unreliable in practice.

I also care a lot about the open-source side of AI Gateways, and here LiteLLM did not fully convince me either. Some features that matter are not as open as I would like. That may be fine for some teams, but for me it lowers the score.

LiteLLM is still an important AI Gateway in the ecosystem, but based on my experience, I would be careful about treating it as the obvious default.

Why 6/10?
Because LiteLLM is powerful and well known, but I had too many issues with reliability and confidence in production.

Bifrost — 7.5/10

Bifrost is another interesting AI Gateway, especially if you are exploring alternatives to LiteLLM.

What I did not like is that too many useful features are behind a paywall. The dashboard keeps pushing the paid version, and that changes the experience. Instead of feeling like a truly open AI Gateway with optional commercial features, it feels more like a commercial AI Gateway with a limited open layer.

That matters because an AI Gateway is infrastructure. If I depend on an AI Gateway in production, I do not want to keep discovering that the more serious features are locked away behind licensing prompts.

At the same time, Bifrost still looks more structured than some other AI Gateways. If your team is comfortable with the pricing model and the locked features are acceptable, it can still be a reasonable choice.

Why 7.5/10?
Because Bifrost looks solid, but the paywall-heavy product experience makes it less attractive to me.

GoModel — 9/10

GoModel is my favorite AI Gateway right now.

Out of the AI Gateways I looked at, GoModel feels the most aligned with what I actually want from production infrastructure. I do not need an AI Gateway to do everything. I need an AI Gateway that is simple, clean, understandable, reliable, and easy to operate.

That is exactly why GoModel stands out to me.

GoModel doesn't have hundreds of integrations, but for my use case that is actually a positive. A smaller and more focused feature set makes GoModel feel simpler. And in infrastructure, simplicity is not a weakness - simplicity is often the reason a tool survives in production.

What I like about GoModel as an AI Gateway is that it feels more intentional. Instead of trying to be everything for everyone, GoModel feels like an AI Gateway that focuses on the core things that matter. That makes GoModel easier to understand, easier to debug, and easier to integrate into a real workflow.

I will go with GoModel as a simple and focused AI Gateway.

Another reason I rate GoModel highly is that the reduced complexity makes the whole system easier to reason about. When you use AI Gateways in production, the operational side matters a lot. You want to understand what happens to a request, where failures happen, and how much hidden complexity the gateway introduces. GoModel gives me more confidence here than other AI Gateways I reviewed.

So even though GoModel may still be missing some features compared to larger AI Gateways, I think GoModel makes better trade-offs for teams that care about simplicity, maintainability, and clarity.

For my use case, GoModel is currently the best AI Gateway choice.

Why 9/10?
Because GoModel keeps the AI Gateway experience simpler, cleaner, and more production-friendly. It is not the biggest AI Gateway, but for me it makes the best trade-offs.

Final thoughts on AI Gateways

After working with LiteLLM and researching alternatives, my conclusion is simple: I do not think latency and amount of integrations are the best ways to compare AI Gateways.

The real differences between AI Gateways are elsewhere:

how stable the AI Gateway is,
how easy the AI Gateway is to operate,
how easy it is to debug requests,
how good the observability is,
and how open the project really is.

For me, GoModel currently looks like the best AI Gateway option.

Not because GoModel has the longest feature list.
Not because GoModel wins on tiny latency differences.
But because GoModel feels like an AI Gateway that makes the right trade-offs.

And for production infrastructure, that matters more than marketing.

Sometimes the best AI Gateway is not the one that does the most.
It is the one that causes the fewest problems. That's how GoModel works for me.