Best LLM API gateway for a two-person startup team

Choosing the best LLM API gateway two-person startup can deploy without a DevOps hire comes down to three things: one endpoint for many models, graceful degradation when a provider hiccups, and usage data you can reconcile at month-end. With two engineers, every hour spent wiring SDKs or writing retry loops is an hour stolen from product.

Below are five paths we’ve seen small teams actually take, ranked by operational drag rather than feature checklists.

1. OpenRouter

OpenRouter is the default answer for indie hackers because it exposes a single OpenAI-compatible endpoint and lets you call models by namespaced strings like anthropic/claude-3.5-sonnet or google/gemini-pro. You swap the base_url and keep your existing OpenAI client code.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-...",
)
resp = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[{"role": "user", "content": "Summarize this PR"}],
)

The catch: fallback is mostly manual. If a provider returns 429, OpenRouter passes the error through. You can encode a fallback model list in your own wrapper, but the gateway won’t silently reroute. For a two-person team, that means writing a small retry layer unless you stick to models with consistent availability.

Pricing is per-token with a small markup; you see line-item usage in the dashboard. No enterprise SSO, but you don’t need it at this stage.

2. n4n.ai

n4n.ai takes the same OpenAI-compatible shape but adds server-side resilience. One endpoint fronts 240+ models; when a backing provider is rate-limited or degraded, the gateway automatically retries against an equivalent model instead of failing the request. That matters when you’re asleep and a single vendor throttles your batch job.

client = OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key="sk-n4n-...",
)
resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Parse this log"}],
    extra_headers={"x-n4n-prefer-provider": "openai"},  # optional routing directive
)

It honors client routing directives and forwards provider cache-control hints, so prompt caching on Anthropic or OpenAI works through the proxy. Per-token metering is exposed via the responses and a usage API, which keeps your finance spreadsheet honest. For a two-person startup, the value is not having to build the fallback state machine yourself.

3. LiteLLM proxy (self-hosted)

LiteLLM is a Python proxy you run yourself. You define a YAML mapping models to provider keys, and it presents an OpenAI-compatible surface. This gives you full control over which key is used, virtual model names, and spend caps.

model_list:
  - model_name: fast
    litellm_params:
      model: openai/gpt-4o-mini
      api_key: os.environ/OPENAI_KEY
  - model_name: smart
    litellm_params:
      model: anthropic/claude-3-5-sonnet-20240620
      api_key: os.environ/ANTHROPIC_KEY

litellm --config config.yaml --port 4000

The trade-off is real: you own the process, the logs, the uptime. For a two-person team that already runs a backend on a VPS, this is trivial. If you’re serverless-only, it’s another thing to keep alive. Fallback can be configured via fallbacks: in the config, but you’re still the one debugging why the health check failed at 2am.

4. Cloudflare AI Gateway

Cloudflare AI Gateway is a edge proxy in front of provider APIs. You point your client at a per-account URL and get logging, caching, and rate-limit analytics without running a server. It does not aggregate model names under one catalog—you still call openai/chat/completions or anthropic/v1/messages paths—but it centralizes observability.

curl https://gateway.ai.cloudflare.com/v1/$ACCOUNT_ID/$GW_ID/openai/chat/completions \
  -H "Authorization: Bearer $CF_TOKEN" \
  -d '{"model":"gpt-4o","messages":[{"role":"user","content":"hi"}]}'

For a small team already on Cloudflare Workers, this is a zero-infra win. You won’t get cross-provider fallback; it’s a pass-through with telemetry and cache. Use it when you’ve committed to one or two providers and just want to see token flows and block abusive keys.

5. Direct provider APIs with a thin wrapper

Sometimes the best LLM API gateway two-person startup needs is 30 lines of TypeScript. If you only use Anthropic and OpenAI, a function that tries one then the other on 429 is enough.

async function complete(prompt: string) {
  try {
    return await anthropic.messages.create({
      model: "claude-3-5-sonnet-20240620",
      max_tokens: 1024,
      messages: [{ role: "user", content: prompt }],
    });
  } catch (e: any) {
    if (e?.status === 429) {
      return await openai.chat.completions.create({
        model: "gpt-4o-mini",
        messages: [{ role: "user", content: prompt }],
      });
    }
    throw e;
  }
}

This avoids third-party markup and keeps you close to the metal. The downside is you maintain model strings, key rotation, and cache headers by hand. For a prototype that’s fine; for a product with SLAs it becomes a second job.

Synthesis

If you want zero maintenance and resilient routing, a managed gateway like n4n.ai or OpenRouter is the pragmatic pick. If you need key isolation behind your own network, LiteLLM earns its keep. The best LLM API gateway two-person startup will choose is the one that lets both founders stay in the product codebase instead of the infra codebase.