Embeddings enable semantic retrieval by capturing the meaning of text, while keyword search returns results based on exact or lexical matches. Many Azure workloads—FAQ search, routing, deterministic classification, or structured lookups—achieve the same or better accuracy using simple keyword or metadata filtering. When embeddings are used for these uncomplicated tasks, organizations pay for token-based embedding generation, vector storage, and compute-heavy similarity search without receiving meaningful quality improvements. This inefficiency often occurs when RAG is used automatically rather than intentionally.
A large share of production AI workloads include repetitive or static requests—such as classification labels, routing decisions, FAQ responses, metadata extraction, or deterministic prompt templates. Without a caching layer, every repeated request is sent to the model, incurring full token charges and increasing latency. Azure OpenAI does not provide native caching, so teams must implement caching at the application or API gateway layer. When caching is absent, workloads repeatedly spend tokens for identical outputs, creating avoidable cost. This inefficiency often arises when teams optimize only for correctness—not cost—and default to calling the model for every invocation regardless of whether the response is predictable.
Many Azure OpenAI workloads—such as reporting pipelines, marketing workflows, batch inference jobs, or time-bound customer interactions—only run during specific periods. When PTUs remain fully provisioned 24/7, organizations incur continuous fixed cost even during extended idle time. Although Azure does not offer native PTU scheduling, teams can use automation to provision and deprovision PTUs based on predictable cycles. This allows them to retain performance during peak windows while reducing cost during low-activity periods.
Development, testing, QA, and sandbox environments rarely have the steady, predictable traffic patterns needed to justify PTU deployments. These workloads often run intermittently, with lower throughput and shorter usage windows. When PTUs are assigned to such environments, the fixed hourly billing generates continuous cost with little utilization. Switching non-production workloads to PAYG aligns cost with actual usage and eliminates the overhead of managing PTU quota in low-stakes environments.
When organizations size PTU capacity based on peak expectations or early traffic projections, they often end up with more throughput than regularly required. If real-world usage plateaus below provisioned levels, a portion of the PTU capacity remains idle but still generates full spend each hour. This is especially common shortly after production launch or during adoption of newer GPT-4 class models, where early conservative sizing leads to long-term over-allocation. Rightsizing PTUs based on observed usage patterns ensures that capacity matches actual demand.
Many production Azure OpenAI workloads—such as chatbots, inference services, and retrieval-augmented generation (RAG) pipelines—use PTUs consistently throughout the day. When usage stabilizes after initial experimentation, continuing to rely on on-demand PTUs results in ongoing unnecessary spend. These workloads are strong candidates for reserved PTUs, which provide identical performance guarantees at a substantially reduced hourly rate. Migrating to reservations usually requires no architectural changes and delivers immediate cost savings.
Azure releases newer OpenAI models that provide better performance and cost characteristics compared to older generations. When workloads remain on outdated model versions, they may consume more tokens to produce equivalent output, run slower, or miss out on quality improvements. Because customers pay per token, using an older model can lead to unnecessary spending and reduced value. Aligning deployments to the most current, efficient model types helps reduce spend and improve application performance.
Some workloads — such as text classification, keyword extraction, intent detection, routing, or lightweight summarization — do not require the capabilities of the most advanced model families. When high-cost models are used for these simple tasks, organizations pay elevated token rates for work that could be handled effectively by more efficient, lower-cost models. This mismatch typically arises from defaulting to a single model for all tasks or not periodically reviewing model usage patterns across applications.
PTU deployments guarantee dedicated throughput and low latency, but they also require paying for reserved capacity at all times. In non-production environments—such as dev, test, QA, or experimentation—usage patterns are typically sporadic and unpredictable. Deploying PTUs in these environments leads to consistent baseline spend without corresponding value. On-demand deployments scale usage cost with actual consumption, making them more cost-efficient for variable workloads.
