Choose the right model for the job
Use premium models where quality, reasoning, or reliability matters. For simpler tasks like classification, routing, tagging, and short extraction, test cheaper models before making the most capable model the default.
A provider or model swap can change monthly cost without changing your product workflow. Compare options in the LLM API pricing table before scaling traffic.
Reduce unnecessary prompt length
- Remove duplicated instructions from every request.
- Send only the retrieved context needed for the current answer.
- Summarize long conversation history when exact wording is not needed.
- Keep structured examples short and focused on the desired behavior.
Control generated output
Output tokens can be the expensive side of a request. Set maximum output limits by workflow, ask for concise responses when appropriate, and avoid generating verbose explanations for tasks that only need a label, score, or short answer.
Use caching and batching where appropriate
If a provider discounts cached input tokens, keep stable instructions or repeated context consistent enough to benefit from caching. For non-interactive jobs, batching can also reduce operational overhead or qualify for provider-specific pricing options.
Caching and batching are not universal fixes. Use them when they match the product experience, latency requirements, and provider support.
Monitor usage before growth compounds cost
Track request count, average input tokens, average output tokens, retry rate, and model mix before production usage grows. Small prompt or model changes are easier to make before customers depend on a workflow.
CostRivo's pricing page outlines upcoming planning tools for builders and teams who need deeper AI cost visibility.