A checklist for choosing an LLM API gateway

Calling model providers directly looks simpler until you juggle rate limits, outages, and a dozen API shapes. A practical checklist for choosing an LLM API gateway forces you to confront those operational realities before they bite in production, not after your pager goes off.

1. Inventory the models you actually call

Most teams start with one provider and quietly accrete others: a cheaper model for classification, a frontier model for reasoning, maybe a local model for PII. List every model ID your services touch today and the ones you expect to add next quarter. Be honest about experimental routes—if a researcher spins up a Mistral instance, it belongs on the list.

{
  "classifiers": ["openai/gpt-4o-mini", "anthropic/claude-3-haiku"],
  "reasoning": ["openai/gpt-4o", "anthropic/claude-3-5-sonnet"],
  "private": ["localhost/llama-3-8b"]
}

Coverage means more than model IDs

A gateway earns its keep only if it speaks all of those dialects behind one interface. Streaming delimiters differ. Tool-calling schemas differ—some models return arguments as a JSON string, others as an object. If you are calling three provider SDKs directly, you are already running a brittle gateway of your own making. The checklist for choosing an LLM API gateway must start with coverage: does it normalize auth, streaming, and function calling across your matrix without silently rewriting payloads?

2. Define failover and degradation policy

Providers throttle. Regions go dark. Your retry loop is not a strategy. Decide what happens when a model returns 429 or 503:

• Pin to same model on another provider?
• Drop to a weaker model with a latency win?
• Fail closed and return 503 to caller?

A gateway that auto-fallback hides this complexity. For example, n4n.ai exposes one OpenAI-compatible endpoint covering 240+ models and shifts traffic when a provider is rate-limited or degraded, so client code stays unchanged.

from openai import OpenAI

client = OpenAI(base_url="https://gateway.example.com/v1", api_key="sk-...")

try:
    resp = client.chat.completions.create(
        model="anthropic/claude-3-5-sonnet",
        messages=[{"role": "user", "content": "Summarize"}],
        timeout=10,
    )
except Exception:
    # gateway already tried fallback; surface error
    raise

Schema drift is the silent killer

If your gateway requires you to code the fallback graph, you have moved the problem, not solved it. Check whether fallback respects token budgets and output schemas—downgrading to a smaller model that truncates JSON will corrupt downstream parsers. Run a test where the primary is blocked and confirm the secondary returns a structurally compatible message.

3. Verify routing controls and cache hints

You will want to pin a provider for compliance, or force a specific region. A gateway should honor client routing directives via headers or body fields, not buried config. Equally important: it must forward provider cache-control hints. OpenAI prompt caching and Anthropic cache_control live in request bodies; a naive proxy that strips unknown fields silently kills your cache hit rate.

curl https://gateway.example.com/v1/chat/completions \
  -H "Authorization: Bearer $KEY" \
  -H "x-routing: provider=anthropic,region=us-east" \
  -d '{
    "model": "anthropic/claude-3-5-sonnet",
    "messages": [
      {"role": "system", "content": "You are a doc bot.", "cache_control": {"type": "ephemeral"}}
    ]
  }'

Negative tests matter

Test that the cache_control reaches the upstream. If the gateway rewrites the body, your 90% cache hit ratio becomes 0% and your bill triples. The checklist for choosing an LLM API gateway should include a negative test: send a field the provider rejects and confirm the gateway returns the provider’s error, not a generic 400. That proves the gateway is a pass-through where it counts.

4. Audit metering and attribution

Per-token billing is non-negotiable for cost control. You need usage broken down by model, route, and caller identity—not a single lump sum at month end. A gateway should emit structured usage matching provider granularity:

{
  "model": "openai/gpt-4o",
  "usage": {"prompt_tokens": 1200, "completion_tokens": 300, "cached_tokens": 1000},
  "route": "default",
  "caller": "svc-ingest"
}

Reconcile, don’t trust

If the gateway aggregates tokens across providers with different counting rules (e.g., Claude counts differently than GPT), you will misattribute spend. Per-token usage metering that reconciles to provider invoices is a requirement, not a nice-to-have. Pull a week of logs and diff against provider dashboards before committing. Missing cached token counts is a red flag—it hides the discount you thought you were getting.

5. Calculate the abstraction tax

A gateway adds a network hop and a new failure domain. Measure tail latency under load; a poorly tuned proxy can add 30–50 ms or silently buffer streams. You also trade direct provider feature launches for gateway support lag—if OpenAI ships a new parameter, how fast does the gateway pass it through?

Exit strategy

The tradeoff is operational simplicity versus control. Direct API calls give you raw access but multiply your surface area: N providers × M models × K retry policies. A gateway consolidates that, but you must trust its release cadence. The checklist for choosing an LLM API gateway should score this tax explicitly: latency budget, feature parity SLA, and exit strategy if the gateway dies. Standardize on an OpenAI-compatible interface so you can repoint base_url if needed.

6. Run a staged cutover

Do not flip a switch. Shadow first: send production traffic to the gateway in mirror mode, compare outputs and latencies. Then canary 5% of low-risk calls. Only then migrate critical paths.

stages:
  - name: shadow
    weight: 0
    mirror: true
  - name: canary
    weight: 5
    routes: ["svc-classify"]
  - name: full
    weight: 100

Use the real client

Common pitfall: teams test gateway with curl but ship a client that uses an older SDK that batches requests differently. Use your real client library in the shadow stage. Capture mismatches in tool-call parsing or stream termination that only appear under concurrency.

Common pitfalls to log

• Assuming all providers stream the same way. They don’t; gateway normalization can change delimiter behavior.
• Ignoring cache key scope. A gateway that keys cache by account not request shape breaks multi-tenant reuse.
• Forgetting tool-calling schemas. Model A returns arguments as string, Model B as object; gateway should not “helpfully” convert.
• Not testing degraded mode. Run a chaos test that blocks the primary provider and watch fallback actually engage.
• Trusting aggregated metrics. Summed tokens across providers mask which route blew the budget.

The checklist for choosing an LLM API gateway is not about features on a homepage. It is about whether your service stays up and affordable when the provider you least expect goes down. Write the answers to sections 1–5 into a one-page doc, then run section 6. That is the difference between a demo and a system.