Retrieval-augmented generation
AI answers grounded in approved internal information.
Explore Retrieval-augmented generationAI architecture
Inference & cost engineering
Balance AI responsiveness, task quality, and operating expense using measured workloads.
Discuss your projectBreak the response time into work you can measure
Context preparation
Fetch, filter, and assemble relevant information.
Model execution
Process the input and produce the output.
Tool work
Wait for external operations and validation.
Delivery
Stream or display a useful result.
A faster model may not fix a slow application if retrieval or external tools dominate the request. Measure complete workflows before selecting an optimization, and preserve the task-quality checks while changing performance behavior.
Choose the optimization that matches the constraint
| Approach | Useful when | Tradeoff to validate |
|---|---|---|
| Streaming | People benefit from progressive output | Partial output must remain understandable |
| Caching | Approved results are safely reusable | Freshness and access boundaries |
| Batching | Work does not require an immediate response | Completion time and retry handling |
| Model routing | Tasks differ in complexity | Routing accuracy and quality regressions |
When should dedicated inference be considered?
Compare sustained traffic, capacity utilization, deployment ownership, and quality requirements against managed options. Include idle capacity, engineering effort, and operational support in the cost model.
Your project, scoped around the outcome
Build with Inference & cost engineering
A workload benchmark, budget model, and tested performance recommendations.
Talk through the scopeRelated technologies
Tools, agents & orchestration
AI-assisted workflows that can request business-system actions.
Explore Tools, agents & orchestrationLLMOps & evaluation
AI applications that need controlled model and prompt updates.
Explore LLMOps & evaluation