Model Routing Strategies for Production

In production, sending every request to a single model is rarely optimal. Some requests are trivial, some are hard, and some are high-stakes. Model routing is the practice of sending each request to the model best suited for it, balancing quality, cost, and latency automatically. With Model Database's single OpenAI-compatible endpoint, routing is easy to implement because every model is reachable by changing one field.

This guide walks through practical routing strategies you can put into production.

Why route at all

A fixed single-model setup forces a compromise: pick a cheap model and quality suffers on hard requests, or pick an expensive model and overpay on easy ones. Routing breaks that compromise by matching each request to the right tier. Done well, it gives you near-frontier quality at close to small-model cost, because the expensive model only runs when it is actually needed.

Strategy 1: Rule-based routing

The simplest approach uses heuristics on the request itself. Route by task type, input length, customer tier, or feature. It is transparent, predictable, and has no extra latency:

def pick_model(task_type, input_len):
    if task_type == "classify":
        return "openai/gpt-4o-mini"
    if task_type == "summarize" and input_len < 4000:
        return "google/gemini-2.0-flash"
    if task_type == "code" or task_type == "reasoning":
        return "anthropic/claude-opus-4-8"
    return "anthropic/claude-sonnet-4-6"

Start here. Most teams get the majority of routing's benefit from a handful of clear rules.

Strategy 2: Cascade with escalation

A cascade tries a cheap model first and escalates only when the result fails a check. The check can be schema validation, a failing test, or a confidence score the model returns. Because every billable response includes X-MDB-Charged-USD, you can measure your real escalation rate and blended cost:

def cascade(task):
    cheap = client.chat.completions.create(
        model="openai/gpt-4o-mini",
        messages=[{"role": "user", "content": task}],
    )
    if quality_ok(cheap):
        return cheap
    return client.chat.completions.create(
        model="anthropic/claude-opus-4-8",
        messages=[{"role": "user", "content": task}],
    )

Cascades work best when a cheap, reliable quality check exists and when most requests pass it, keeping escalations rare.

Strategy 3: Classifier-based routing

For nuanced decisions, use a fast, cheap model to classify difficulty first, then route accordingly. You pay for a small extra call but gain smarter routing than static rules allow:

def classify_difficulty(task):
    r = client.chat.completions.create(
        model="openai/gpt-4o-mini",
        messages=[{"role": "user", "content": f"Reply only 'easy' or 'hard': {task}"}],
    )
    return r.choices[0].message.content.strip().lower()

def smart_route(task):
    hard = classify_difficulty(task) == "hard"
    model = "anthropic/claude-opus-4-8" if hard else "anthropic/claude-sonnet-4-6"
    return client.chat.completions.create(
        model=model, messages=[{"role": "user", "content": task}],
    )

Build in fallbacks for reliability

Routing isn't only about cost, it is also about resilience. If a request to one model fails or times out, retry against an alternative. Since all models share one endpoint and key, a fallback is just another model value:

for model in ["openai/gpt-4o", "anthropic/claude-sonnet-4-6", "meta-llama/llama-3.3-70b-instruct"]:
    try:
        return client.chat.completions.create(model=model, messages=msgs)
    except Exception:
        continue

This pattern keeps your feature available even if one model is degraded.

Measure and tune continuously

Routing is not set-and-forget. Log the model used, the outcome, and the X-MDB-Charged-USD and X-MDB-Balance-USD headers for every request. With that data you can answer the questions that matter: what is my blended cost per request, how often do I escalate, and are any rules sending easy work to expensive models? Use GET /v1/models to keep your routing table current as new models become available, and re-evaluate periodically, a model that was the best choice last quarter may be superseded.

Ready to route smarter? Create a key and add credit at your dashboard, and see the docs for the endpoints, streaming, and headers you'll build your routing layer on.