Choosing an AI Gateway: BUZZ vs OpenRouter vs Helicone vs LiteLLM

Once a team has more than one LLM-backed feature in production, an AI gateway stops being optional. You need a single place to manage keys, monitor spend, route between models, and enforce privacy. The interesting question is not whether to put a gateway in front of your model calls. The interesting question is which gateway, and why.

The market has settled into a few distinct shapes. BUZZ is a transparent, privacy-first managed gateway that consolidates the four frontier providers behind one key. OpenRouter is a wide-catalog model marketplace with cross-provider routing. Helicone is an observability platform delivered through a proxy. LiteLLM is an open-source self-hosted proxy. Each one optimizes for a different thing, and the trade-offs become obvious as soon as you put them next to each other.

This piece is not a ranking. It is a decision framework. We will look at the dimensions that actually decide the choice, score each gateway on each dimension honestly, and then give scenario-specific recommendations.

The decision dimensions

Most "AI gateway comparison" content is shaped like a feature matrix. That obscures the only thing that matters: the underlying trade-offs the four products have made. A gateway that is wide on models is usually shallow on privacy. A gateway that is deep on observability is usually heavy on retention. A self-hosted proxy is cheap on per-token margin and expensive on engineering time. You cannot maximize all five of the dimensions below. You have to pick which two or three matter for your situation.

1. Model breadth

How many providers and models can you reach through one key. This dimension matters most when your workload is exploratory: you are running evaluations across many models, you care about access to long-tail or open-source models, or your team wants to switch models without contractual friction. It matters much less when your production traffic is concentrated in two or three frontier models that are stable for months at a time.

2. Pricing relative to direct calls

A gateway can be priced above, at, or below first-party rates. Above is uncommon and usually justified by other features (observability, routing). At-rate is the marketplace pattern: the gateway takes a small margin and gives you a single bill. Below first-party is rarer and depends on the gateway's commercial structure and volume buying power. The honest way to compare is to take your last month of real traffic and price it against each candidate, not to read headline rate cards.

3. Privacy and retention

Where do request and response bodies live after the request finishes. The default in this category is to retain at least some traffic for abuse monitoring, debugging, or analytics, often with an opt-out. The minority position is to forward bytes and never persist them. If your prompts contain customer data, system prompts you treat as IP, or retrieval context that should not leave your perimeter, this is the dimension that decides things. If you are building consumer features with no PII or proprietary content, it matters less.

4. Observability and tooling

How much do you see about your own traffic after it leaves your application. At one end, a transparent forwarder gives you billing metadata only and assumes you log inside your own perimeter. At the other end, an observability-first proxy gives you per-request dashboards, prompt versioning, evaluations, and replay. The trade-off is direct: visibility requires retention. You cannot have both deep prompt-level analytics and zero retention from the same vendor.

5. Self-hosted vs managed

Who runs the gateway. A managed service hands you a base URL and a billing relationship. A self-hosted proxy hands you a Helm chart and an on-call rotation. Self-hosted is the right choice when compliance requires the gateway inside your VPC, when you want to extend the routing logic in code, or when you have an existing ops team who would rather operate one more service than depend on another vendor. It is the wrong choice when you are a small team and want to ship features instead of running infrastructure.

The four contenders, head to head

Below is a compact view of how the four options sit on each dimension. The text underneath the table is where the nuance lives.

BUZZ: transparent forwarder, privacy-first

BUZZ is built around a deliberately narrow scope. One API key reaches Claude, GPT, Gemini, and Grok. The gateway forwards request and response bytes verbatim, supports prompt caching, tool use, streaming, and extended thinking because those are properties of the upstream protocol that a transparent forwarder preserves by definition. Bodies are never written to logs, never persisted to a database, never cached for replay across users, and never forwarded to a third-party analytics or moderation pipeline. Only billing metadata survives a request: model name, token counts, timestamp, user ID, status, latency.

BUZZ publishes its full pricing endpoint as a public machine-readable resource and the model catalog with capabilities at a separate endpoint, so you can wire both into your own provisioning logic. Rates are set below first-party list pricing on the providers BUZZ supports.

The honest trade-off: BUZZ does not try to be a marketplace. If you want to evaluate ten obscure open-source models or route to a niche provider, BUZZ is not the gateway for that. It is built for teams that have settled on the major frontier providers, want a single key in front of them, and care more about transparent forwarding and a tighter privacy posture than about catalog size.

# BUZZ with the Anthropic SDK
from anthropic import Anthropic
client = Anthropic(
    base_url="https://buzzai.cc",
    api_key="sk-buzz-..."
)

# Same key, OpenAI SDK
from openai import OpenAI
client = OpenAI(
    base_url="https://buzzai.cc/v1",
    api_key="sk-buzz-..."
)

OpenRouter: model marketplace, broadest catalog

OpenRouter's identity is breadth. The catalog spans more than a hundred models across many providers, including frontier closed models, open-weight models hosted by various inference vendors, and long-tail experimental releases. A single API key reaches all of them, with cross-provider fallbacks and routing rules that let you express preferences like "prefer this provider, fall back to that one if it is rate limited."

Pricing typically tracks first-party rates closely with a small margin, plus credits-based billing that lets you allocate spend per model without juggling separate provider accounts. For exploration-heavy workloads, that is a real win. The cost of running evaluations across twenty models drops from twenty contractual relationships to one.

On retention, OpenRouter's documented default is to retain a sample of traffic for abuse and content monitoring purposes, with an explicit opt-out available in account settings. Teams that want OpenRouter's catalog but a tighter privacy posture should toggle the relevant settings explicitly and verify the change in their account configuration. This is a sensible operating posture for a wide-catalog marketplace; the breadth comes with provider obligations that have to be enforceable.

OpenRouter is the right call when your priority is "give me access to as many models as possible behind one key" and a marketplace's default retention behavior, configurable as it is, is acceptable for your data.

Helicone: observability proxy

Helicone is a different product class. It is best understood as an LLM observability platform that happens to be delivered through a proxy. The dashboard is the product: per-request logs, prompt versioning and management, evaluations, custom properties, user-level analytics, latency and cost breakdowns. If you have ever wished your LLM traffic looked more like a Datadog dashboard, Helicone is the closest thing on the market.

That value depends on retention by design. Helicone records the requests and responses that flow through it so it can show them to you later. Teams that adopt Helicone are usually doing so because they want that visibility. The tooling around prompt management, scored evaluations, and traffic introspection is genuinely useful for teams whose primary engineering challenge is "we cannot tell what our prompts are doing in production."

The trade-off is exactly the inverse of BUZZ. Helicone is the right choice when observability is the dominant problem, and a vendor holding your traffic in order to show it to you is a feature rather than a risk. It is the wrong choice when minimizing where prompts can land is a hard constraint.

LiteLLM: open-source self-hosted proxy

LiteLLM is the open-source option. You deploy it yourself, configure provider credentials in your environment, and expose a unified OpenAI-compatible interface to your applications. The codebase is active, the provider list is wide, and you can extend the routing layer in Python when the built-in policies do not match your needs.

The wins are real. Per-token cost is whatever your upstream charges with no proxy margin. Compliance teams who need the gateway inside their own VPC get exactly that. You can write custom hooks for caching, redaction, model selection, or routing without filing a feature request. There is no vendor lock-in beyond the configuration format.

The cost is operational. Someone has to deploy it, scale it, secure it, upgrade it, monitor it, and be on call when it breaks. For a team with existing platform engineering capacity, that is acceptable overhead. For a small team trying to ship product features, every hour spent debugging a self-hosted proxy is an hour not spent on the application. LiteLLM is the right answer when the constraints (compliance, customization, cost at very high volume) genuinely require self-hosting, and the wrong answer when the team is reaching for it because "we should run our own infrastructure" feels right.

When to pick which

The dimensions and contender notes above generalize. The scenarios below are what people actually ask about.

Migration considerations

Switching gateways is much easier than switching databases, but it is not free. The friction lives in three places.

Wire compatibility

If your current gateway presents a verbatim upstream API (Anthropic's /v1/messages, OpenAI's /v1/chat/completions), changing base URLs is a one-line edit. If your current gateway introduces its own request schema, response wrappers, or proprietary feature flags, every call site that uses those non-standard fields becomes a migration item. When evaluating any gateway, lean toward ones that keep the wire identical to the upstream provider for exactly this reason.

# Before: direct provider
client = Anthropic(api_key="sk-ant-...")

# After: BUZZ, only the base URL and key change
client = Anthropic(
    base_url="https://buzzai.cc",
    api_key="sk-buzz-..."
)

Cost reconciliation

The previous gateway's invoice format will not match the new one. Plan a one-month overlap where you can run a small percentage of traffic through the new gateway, compare the line items, and confirm the model-by-model totals reconcile. This catches edge cases like cache token accounting differences, tool-call billing variations, and rate-limit-driven provider fallbacks that change the effective unit price.

Compliance and contracts

If your data processing addendum names the previous vendor, switching means a new DPA and possibly a new sub-processor disclosure to your customers. For a transparent zero-retention forwarder, that disclosure can be much shorter than the previous one because the data flow is simpler. Either way, plan for the legal cycle, not just the technical cycle.

What this means for the gateway you should choose

The four contenders are good products, optimized for different priorities. The shortest version of the framework:

• Privacy and lower cost on frontier models, with a managed gateway: BUZZ.
• Maximum model breadth with one key: OpenRouter.
• Deep prompt-level observability: Helicone.
• Self-hosted control: LiteLLM.

Anyone telling you a single gateway is "the best" is selling you something. The right gateway is the one whose architectural trade-offs match the constraints you actually have. If your constraints are privacy, transparent forwarding, support for prompt caching and tool use across the frontier providers, and rates that beat first-party list, BUZZ is built for that. If they are not, one of the other three will fit better, and that is fine.

Frequently asked questions

Conclusion

An AI gateway is a small piece of architecture that absorbs a lot of decisions. Model strategy, vendor management, privacy posture, observability, deployment ownership: all of it ends up implicitly encoded in the choice you make. The cleanest way to think about it is to write down which two or three of those dimensions you actually care about most, and then pick the gateway that was designed around those same dimensions. Forcing a gateway to be something it was not built to be is more expensive than just picking the right one.

If your priorities are transparent forwarding, zero retention of bodies, support for the major frontier providers behind one key, and rates below first-party list, BUZZ is built for that workload. If they are not, one of the other three will fit better. Either way, decide on the basis of trade-offs you understand, not on a marketing page that promises to be all things at once.