A low-cost AI backend does more than choose a cheaper model: it predicts request size, records the provider’s actual usage, makes reusable context cache-friendly, and manages throughput and spending with separate controls. Count tokens before a call when you need to validate context fit or estimate cost; use the response’s usage data for accounting. Neither a character estimate nor an assumed cache hit is a reliable bill.
What a cost-aware AI backend needs to do
For each model request, your backend should be able to answer four questions: what did the application send, what did the provider count and charge, what was reused from cache, and which policy allowed the request to proceed? That calls for two different measurements:
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- Preflight count: a model-specific count of the proposed request, used to check size, estimate cost, or select a route.
- Post-call usage: the provider-reported input, output, and cache usage for the completed request, used for metering and cost analysis.
A count is not a price guarantee. Actual cost depends on the selected model, the provider’s current rates, the request and response usage, cache reads or writes where applicable, and potentially the platform serving the model. Treat each of these as explicit inputs to your accounting rather than relying on one static estimate.
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How to count tokens before calling an LLM API
Count the request you will actually send
Word counts and character-based rules of thumb cannot reliably predict billable tokens. Tokenization varies by model, encoding, and language. The request can also include framing and structured or multimodal content that a simple text tokenizer will miss. OpenAI’s token-counting guide describes counting requests that include elements such as images, files, tools, and conversations. Anthropic provides a token-counting guide and advises using the intended model ID for the count.
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At your API boundary, normalize the provider, exact model ID, request shape, tenant or project, and task class. When the decision depends on size, call the matching provider’s counting facility for the request being prepared. A local tokenizer can help with plain text, but it may not account for provider-specific request structure, tool definitions, schemas, files, or image payloads.
Use the count for decisions, not final billing
A preflight count is useful for rejecting or rerouting a request that is too large, checking whether it plausibly fits a context window, and estimating a cost range. Leave room for generated output and any model-specific or non-visible tokens. Do not present an estimate as the final charge: record the usage returned after the call.
Counts are tied to the tokenizer and model context used to produce them. Store the provider, model ID, count method, and estimated input size with the request record. If you migrate models, count against the target model rather than reusing the old model’s estimate. That keeps later comparisons interpretable and avoids treating a tokenizer change as a sudden shift in customer behavior.
How to make reusable prompt context cache-friendly
Keep shared content stable and put variable content after it
Look for repeated material: system instructions, tool definitions, shared reference documents, and prior conversation context. Where the provider’s cache rules allow it, keep this common prefix stable and place request-specific content after it. This can make repeated context eligible for reuse without changing the task-specific part of the request.
Eligibility is provider- and model-specific. Check the model’s minimum eligible prefix, cache breakpoints, retention period, and write/read pricing before designing around caching. A stable prompt is an optimization opportunity, not proof that a request will be served from cache.
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Measure cache outcomes instead of assuming them
OpenAI documents that cache keys can influence routing but do not pin a request to a machine or guarantee a hit; routing behavior also differs across model generations. See the OpenAI prompt-caching guide for the current behavior of supported models. Track total input tokens, cached tokens, cache-write tokens when exposed, latency, and realized cost. Calculate hit rates for useful groups such as tenant, workspace, model, or day, so a strong aggregate rate does not hide misses in a particular workload.
Anthropic’s published pricing documentation describes automatic caching and explicit breakpoints, with different write and read charges. The examples in that documentation include a five-minute cache write at 1.25 times the base input price, a one-hour cache write at 2 times the base input price, and a cache read generally at 0.1 times the base input price, with model-specific exceptions. These are provider-published multipliers, not universal prices; verify the current model and deployment rates. A platform partner may also set separate regional pricing.
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How to meter LLM usage per customer
Persist one usage record per provider request. A practical record includes:
- Request ID, timestamp, tenant or project, task class, and application route.
- Provider, exact model ID, request status, and end-to-end latency.
- Preflight input count and the method or model context used to obtain it.
- Provider-reported input and output tokens, cached tokens, and cache-write tokens where exposed.
- The applicable price version or billing period used to calculate cost.
Keep estimated and actual usage in separate fields. Use the estimate for pre-call policy and diagnostics; use returned provider usage for the request’s accounting. OpenAI’s production best practices recommend tracking usage, monitoring realized cost, and setting threshold alerts. Its prompt-caching guidance also identifies cache and input-token usage, latency, and cost as useful things to monitor.
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Do not calculate output usage by counting only the visible answer. Provider-reported output can include generated tokens that are not shown in the final text. Anthropic explains this in its pricing documentation. For accounting, use the provider’s reported usage rather than a recount of displayed text.
Aggregate the records by tenant, user, model, route, and time period. Those views help distinguish a costly customer workload from a model-wide change, identify cache misses, and show whether an optimization reduced actual spend rather than only the preflight estimate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Separate throughput limits from spending limits
Rate limits protect throughput; spend policies protect budgets. They are related operational concerns, but one is not a substitute for the other. A service can reach a request-per-minute ceiling before a token-per-minute ceiling, or the reverse. Your backend should therefore apply independent controls for concurrency and queues, request/token throughput, and tenant or project budget.
OpenAI documents rate-limit dimensions that include requests per minute (RPM), requests per day (RPD), tokens per minute (TPM), tokens per day (TPD), images per minute (IPM), and audio minutes per minute for some models. Limits vary by model, organization or project, and usage tier; consult the current rate-limit documentation and account dashboard rather than hard-coding a generic limit. OpenAI describes organization monthly usage limits separately from configurable spend controls.
Anthropic’s rate-limit documentation says that, for most models, uncached input and cache creation count toward input-token-per-minute limits while cache reads are excluded; it also documents model-specific exceptions. Check the actual model and account limits before setting application quotas.
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Use queues and concurrency limits to smooth bursts, and implement provider-appropriate retry and backoff behavior when requests are throttled. Set application-level quotas for your tenants as well as provider-level safeguards. A provider rate limit tells you what the account can send; it does not define the spending policy your product should enforce for each customer.
Optimize using workload evidence
Start with measured cost and quality by task. Then test changes against representative requests, tracking quality, latency, throughput, and actual cost together. OpenAI’s production guidance identifies shorter prompts, smaller models, and caching as possible cost levers; each should be validated for the workload before becoming a routing rule.
- Reduce token volume: remove redundant prompt material and constrain unnecessary output while preserving required context and answer quality.
- Reuse repeated context: organize stable prefixes to fit the provider’s cache rules, then compare cache reads and writes with the uncached baseline.
- Route suitable tasks to lower-cost models: evaluate representative inputs before changing the default model. A lower per-token price does not establish equivalent quality or latency.
- Batch compatible work where appropriate: compare the effect on throughput and end-to-end latency with the value of combining requests.
When comparing candidate providers or models, use observed workload data rather than a price list alone. Compare quality on representative tasks, effective cost including cache reads and writes, token-count support for the request shape, latency, throughput headroom, cache eligibility and retention, and deployment constraints such as geography and privacy requirements. Quality, latency, privacy, and procurement outcomes depend on your application and environment; they cannot be inferred from token prices or provider documentation alone.
A practical request lifecycle
- Normalize: identify provider, target model ID, task, request shape, tenant, and applicable pricing and policy versions.
- Count when needed: use the provider’s matching token-count facility to check size, context fit, routing, or approximate cost. Keep a safety margin for output.
- Apply pre-call policy: enforce tenant budget and request-size rules, then check concurrency and throughput capacity. Queue, route, or reject according to explicit policy.
- Send a cache-conscious request: retain stable shared context where practical, place variable content after it when compatible, and follow the model’s current cache rules.
- Record actual usage: persist provider-reported token and cache fields, status, latency, and the pricing context for the call.
- Reconcile and alert: aggregate realized costs by customer and route, compare them with budget thresholds, and investigate changes in usage, cache behavior, latency, or throttling.
- Evaluate changes: test prompt, cache, batching, or model-routing changes on representative tasks before applying them broadly.
This design makes the estimate useful before inference and the usage record authoritative for accounting, while keeping cache performance, throughput, and customer budgets independently observable.
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