Same Key, Different Deadlines: Routing Interactive Requests and Overnight Batch Jobs to Different Models

Why One Key, Many Models?

A single API key that can call multiple models lets you choose the right tool for each job. Interactive requests—like chat messages or real-time suggestions—require low latency. Overnight batch jobs—like data processing or report generation—can tolerate higher latency but often benefit from lower cost or higher throughput. By routing these workloads to different models, you optimize both user experience and budget.

Latency vs. Cost: The Core Trade-off

Models differ in speed, price, and capability. Typically, smaller or more efficient models respond faster and cost less, while larger models may be slower but more accurate. However, this is not universal. The key is to measure latency and cost for your specific use case rather than relying on general assumptions.

• Interactive use: Users expect responses in under a second to a few seconds. Choose models that consistently meet your latency threshold.
• Batch use: Jobs run in the background; latency of minutes or hours is acceptable. Prioritize cost-effectiveness and throughput.

Which Models for Which Job?

Since model performance varies by task and provider, we recommend measuring latency and cost for the models you are considering. As a starting point, you can categorize models into three tiers:

• Fast & lightweight: Often suitable for simple tasks like classification, short summarization, or intent detection. These are good candidates for interactive requests when they meet your accuracy needs.
• Balanced: General-purpose models that offer a mix of speed and capability. Use for interactive requests when lightweight models are insufficient, or for batch jobs that need better quality.
• Powerful & slower: Typically larger models with higher latency and cost. Reserve for batch jobs where quality is critical, or for interactive requests only when latency is acceptable for your users.

Remember: these are guidelines, not rules. Always test with your own data.

Measure Your Own Latency

Before routing, collect data. For each model you plan to use, run a representative set of requests and record:

• Response time: Time from request to full response (for streaming, time to first token and total time).
• Cost per request: Based on input and output tokens.
• Quality: Whether the output meets your requirements.

Use this data to set thresholds. For example, if your interactive users tolerate up to 2 seconds, only route to models that consistently respond within 1.5 seconds under peak load.

Build a Decision Table

Create a simple table to guide routing. Here's a template:

| Model | Avg Latency (p95) | Cost per 1K tokens | Suitable for Interactive? | Suitable for Batch? |
|-------|-------------------|---------------------|---------------------------|---------------------|
| Model A | 800 ms | $0.002 | Yes | Yes |
| Model B | 2.5 s | $0.001 | No | Yes |
| Model C | 1.2 s | $0.005 | Yes | No |

Fill in your own measurements. Update regularly as models evolve.

Implementing Routing

With a unified API key, you can route requests in your application code. Common patterns:

• Endpoint-based routing: Have separate endpoints for interactive and batch jobs. Each endpoint calls the appropriate model.
• Queue-based routing: Push batch jobs to a queue (e.g., Redis, RabbitMQ). Workers pull from the queue and call the chosen model.
• Dynamic routing: Based on request parameters (e.g., user tier, task type), select the model at runtime.

Since all models are accessible via the same API key, you avoid managing multiple credentials and can switch models with a simple parameter change.

Cost Considerations

Our platform charges users the official model price multiplied by 1.3. If you contribute a key, you are credited official price × 1.1 (or × 1.2 for premium models) in USDC. This means your effective cost depends on your usage and contributions. Routing batch jobs to lower-cost models can reduce your overall spend, while interactive jobs may justify the higher cost of faster models.

Monitor and Adjust

Routing is not set-and-forget. Monitor latency and costs regularly. If a model's latency increases or a cheaper alternative becomes available, update your routing rules. Use logging and analytics to track performance per model and per job type.

Example Workflow

1. User sends a chat message → route to Model A (fast, interactive).
2. User schedules a nightly report → push to batch queue → worker routes to Model B (cost-effective, batch).
3. Monitor: Model A p95 latency is 900 ms, Model B cost is 0.001 per 1K tokens.
4. Adjust: If Model B latency is acceptable for some interactive tasks, consider using it for non-critical interactive requests.

Conclusion

By measuring latency and cost, building a decision table, and implementing routing with a single API key, you can serve interactive users quickly while processing batch jobs efficiently. Start small, measure, and iterate.