How much can I save with an LLM proxy?

Savings depend on your workload. Caching alone saves 30-50% for iterative development. Model routing to cheaper providers can reduce costs 5-20x for suitable tasks. Combined strategies typically achieve 50-80% reduction.

Does switching to a cheaper model reduce quality?

It depends on the task. Classification, summarization, and simple completions work well with cheaper models. Complex reasoning, coding, and creative tasks may need more capable models. The strategy is to route each type of request to the cheapest model that handles it well.

Can I set spending limits per team or project?

Yes. Stockyard supports per-team spend caps with daily and monthly limits. When a team hits their cap, requests are blocked until the next period.

Stockyard How to Reduce LLM API Costs — 5 Strategies That Actually Work

1. Cache identical requests

If your application sends the same prompt more than once, you are paying for the same answer multiple times. This happens more than you think: editors re-sending file context, classification tasks with repeated inputs, RAG pipelines with overlapping queries.

A prompt cache stores responses and returns them instantly for identical requests. Cache hits are free and return in milliseconds instead of seconds. For iterative development workflows, caching alone can cut costs 30-50%.

# Enable caching in Stockyard
curl -X PUT http://localhost:4200/api/proxy/modules/cache \
  -d '{"enabled": true}'
# Next identical request returns from cache: $0.00, ~2ms

2. Route to cheaper models

Not every request needs GPT-4o. Classification, summarization, and simple completions work well with cheaper models. DeepSeek-chat costs roughly 20x less than GPT-4o per token. Gemini Flash is 10x cheaper.

Use model aliasing to route different workloads to different models without changing application code:

# Route simple tasks to a cheap model
curl -X PUT http://localhost:4200/api/proxy/aliases \
  -d '{"alias": "fast", "model": "deepseek-chat"}'

# Keep complex tasks on GPT-4o
curl -X PUT http://localhost:4200/api/proxy/aliases \
  -d '{"alias": "smart", "model": "gpt-4o"}'

3. Set spend caps

Without a cap, a bug in your code or a spike in traffic can burn through your budget overnight. Stockyard enforces daily and monthly spend limits at the proxy level. When the cap is hit, requests return a clear error instead of silently running up the bill.

4. Track costs per request

You cannot reduce what you cannot measure. Most LLM providers show aggregate spend with a 24-48 hour delay. Stockyard's cost tracking shows per-request costs in real time: which model, how many tokens, exactly how much it cost.

Once you can see per-request costs, you find the expensive outliers. Often 5% of requests account for 50% of spend. Target those first.

5. Switch providers

Provider pricing changes frequently. A model that was cheapest last month might not be cheapest today. With a proxy, switching providers is a config change, not a code change. See our LLM API pricing comparison for 2026 for current rates across 40+ models.

Stockyard supports 40+ providers through a single OpenAI-compatible endpoint. You can route to DeepSeek for cost-sensitive tasks and OpenAI for quality-critical ones, all through the same API.