The cost of an AI-agent run is the sum of every model request, token, tool invocation, retry, subagent, runtime minute, storage unit and third-party service used from start to finish. A single user message can trigger a chain of calls, so multiplying one model-call price by one is usually wrong. The reliable method is to instrument each request, classify every charge, then reduce the largest avoidable components without harming task completion.
What counts as an agent run?
An agent run starts when your application accepts a task and ends when it returns a final result, reaches a configured stop condition or fails. Between those points, the agent may plan, call a model, invoke tools, inspect results, ask a subagent for help, retry a failed request and make another model call. Billing follows that activity, not just the text the user typed.
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For cost analysis, treat the run as a trace containing individual requests. Each request should record its model, input tokens, output tokens, cached-input tokens (if reported), tool calls, latency, retries and outcome. Sum the trace for a run-level number, then retain the request-level detail so you can explain why the total changed.
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| Cost component | What can be billed | Why teams miss it |
|---|---|---|
| Input tokens | System and developer instructions, conversation history, tool definitions, user text, files, images and previous tool results sent with each request. | A short new message can carry a very large repeated context. |
| Cached input | Repeated prompt prefixes that qualify for a provider’s cache rate. | A session or identical-looking prompt does not guarantee a cache hit; eligibility, lifetime and minimum-prefix rules vary. |
| Output tokens | Visible text, structured output, tool-call arguments and, on some providers, reasoning tokens. | Long hidden reasoning or verbose JSON can cost as much as visible prose. |
| Tool usage | Model calls to search, retrieval, code execution, browsing, databases or other tools; some server-side tools have their own usage fee. | Teams count the model request but not the tool’s per-call or execution charge. |
| Retries and loops | Every failed or repeated request, including backoff attempts and agent loops that revisit the same step. | A successful final answer can conceal several paid failures. |
| Subagents | Separate model and tool activity delegated to specialist agents. | The root agent’s dashboard may not make handoffs obvious unless trace IDs are propagated. |
| Runtime and sandbox | Container or code-execution time, compute size, networked services and minimum billable durations. | These charges are outside token totals. |
| Storage and third parties | File-search or vector storage, object storage, observability, databases and external APIs. | The provider invoice and a vendor invoice may cover the same run. |
OpenAI’s agent observability guidance treats reasoning tokens as output tokens and instructs teams to include root-agent work, subagents, retries, applicable tools, sandbox compute and third-party charges. It also notes that cache-write charges can apply to models that price them, while a usage record may not expose a separate cache-write count. Anthropic’s platform documentation counts tokens in tool definitions and tool-use/tool-result blocks, adds a system prompt for tool use, and describes separate usage-based charges for server-side tools and execution-time billing for code execution. Google Cloud’s Generative AI pricing for Agent Platform distinguishes model-related prices from other Agent Platform product charges. These details make a token-only estimate incomplete.
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Measure one run before changing architecture
Cost controls work when they are tied to a measured baseline. Capture a representative sample of successful, slow, failed and unusually expensive runs rather than relying on an average from a single day.
- Create a run identifier. Generate one trace ID at task intake and pass it through the root agent, every model request, tool invocation, retry and subagent handoff.
- Log request-level usage. Store model name, input tokens, output tokens, cached-input tokens when available, request status, latency and timestamp. Record the exact provider and adapter, because reporting fields differ.
- Record tool events. For each call, capture tool name, start and end time, payload size, returned size, success or error, retry count and any provider-reported fee.
- Separate root and delegated work. Give every subagent its parent run ID and its own span. This prevents delegated calls from disappearing into a separate dashboard.
- Attach runtime and storage usage. Log container size and minutes, code-execution duration, files indexed or stored and external-service requests.
- Join usage to an invoice estimate. Apply the rate card that was valid for the request date and region. Keep the rate-card version with the record so a later price change does not rewrite history.
- Inspect outliers. Sort by total cost, number of requests, input-token volume, output-token volume and tool calls. The largest category is your first optimization target.
The OpenAI Agents SDK documents aggregated usage for a run and per-request usage entries. Use those fields as a starting point, then validate that your specific model adapter and backend expose the fields you need. A middleware layer that records raw provider responses is useful when an abstraction hides cache, reasoning or tool details.
Five ways to cut avoidable cost
1. Measure every request, not just the final response
Instrumentation is itself a cost-control measure: it reveals whether a prompt change actually reduced spend or merely moved it to tools and retries. Set budgets and alerts at both request and run level. Useful limits include maximum model calls, maximum tool calls, maximum wall-clock time and maximum output tokens. When a limit is reached, return a controlled partial result or ask for human intervention instead of allowing an unbounded loop.
Track distributions, not only means. The 95th-percentile run can be several times the median when a tool fails or a task fans out to subagents. Break dashboards down by workflow, model, tenant and outcome so a cheap successful path is not averaged together with an expensive failure path.
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2. Remove unnecessary context
Audit the exact payload sent on every model call. Mark each item as required for the current decision, useful only occasionally or obsolete. Common sources of waste are full chat histories, duplicate tool schemas, repeated policy text, entire documents when a relevant excerpt would do, and raw tool output that the agent never reads.
- Summarize or compact old turns after a task milestone, preserving decisions and unresolved items rather than every utterance.
- Send only the tool definitions available in the current state; do not expose an entire tool catalog to a narrow step.
- Limit retrieval results by relevance and token budget, and strip markup, logs and duplicate records before insertion.
- Return structured fields from tools instead of large human-formatted transcripts.
- Keep stable instructions and tool definitions in a reusable prefix where practical, then place new task details later in the request.
Measure before and after by comparing input tokens per request and task-completion rate. A smaller prompt that causes an extra clarification call may cost more overall.
3. Use prompt caching when the request qualifies
Prompt caching can reduce the price of repeatedly processed prefixes, but it is conditional. Providers differ on minimum prefix length, exact-match rules, cache lifetime, model support, geographic scope and whether cache writes are charged. An open session does not by itself prove that a cache entry was used.
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- Put stable system instructions, tool schemas and other invariant material first.
- Append changing user details, retrieved passages and current tool results after that stable prefix.
- Keep serialization deterministic: changing whitespace, key order or schema text can break a prefix match.
- Inspect cache-read and cache-write fields where the provider exposes them.
- Calculate the break-even point using the model’s uncached rate, cached-input rate and any write charge.
Do not force caching when the prefix changes every request or when a shorter prompt would be cheaper without it. Treat cache savings as a measured property of a workload, not a guarantee attached to a product name.
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4. Control tool calls, retries and agent loops
Every tool call can add model input for the schema and arguments, model output for the call, tool execution time and a follow-up request containing the result. Design tools to finish a useful unit of work in one call. Combine dependent lookups server-side, add pagination and field selection, and return only data needed for the next decision.
Use bounded retries with error-aware policies. A validation error should be fixed or surfaced, not retried identically. Apply exponential backoff for transient failures, cap attempts, and record the reason for each retry. For loops, define a progress signal such as a changed state, completed checklist item or reduced error count; stop when progress stalls.
Subagents are worthwhile when specialization improves completion or reduces expensive root-agent reasoning. Otherwise, a handoff adds another context construction, model call and tool sequence. Set a per-subagent budget and pass a concise task contract rather than the entire parent transcript.
Server-side tools and code execution may have separate usage prices. Include those charges and minimum execution windows in the same run ledger as token costs.
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5. Compare providers and architectures on the same task
There is no universal cheapest provider. A lower input rate can be offset by more output tokens, weaker tool reliability, extra retries, higher runtime charges or lower completion quality. Build a fixed evaluation set that represents your production mix, then run the same acceptance tests with equivalent context, tools, timeout rules and output requirements.
| Comparison dimension | Questions to answer |
|---|---|
| Input | How many uncached input tokens does each implementation send, and what is the regional rate? |
| Cache | Is the prefix eligible, what is the cached-input rate, how long does it live and are writes charged? |
| Output and reasoning | What output volume is produced, and are reasoning tokens billed as output? |
| Tools | Are tool calls, retrieval, browsing or code execution priced separately? |
| Runtime and storage | What are the rates and minimums for containers, sandboxes, files, vector indexes and network services? |
| Quality and operations | Does the system complete the task with the same accuracy, latency, safety and retry rate? |
Record prices and terms on the date tested. Provider pages change, and a model’s rate card may vary by region, modality, batch mode or service tier. Recheck live pricing immediately before publishing a cost comparison or changing production routing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to calculate a run without fooling yourself
Use a ledger rather than a single token multiplier:
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run cost = model input + cached input + model output/reasoning + tool fees + runtime/sandbox + storage + third-party services + retries and subagents
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For each component, multiply measured usage by the applicable dated rate. Keep uncached and cached input separate. Keep root-agent and subagent requests separate. Add one-time or minimum-duration charges only when the provider’s terms say they apply.
As concrete rate-card examples, the OpenAI API pricing page showed, when accessed October 13, 2026, $2.50 per 1,000 tool calls. The same page listed container sessions at $0.03 for 1 GB, $0.12 for 4 GB, $0.48 for 16 GB and $1.92 for 64 GB per 20-minute session, with eligible sessions billed by the minute and a five-minute minimum. It also listed file-search storage at $0.10 per GB per day with 1 GB free. These are provider prices, not a typical agent-run cost; verify the live page, applicable tool and region before using them in a budget.
Diagnosing an unexpectedly expensive run
- Input tokens jumped: compare the current payload with the last inexpensive trace; look for duplicated history, tool schemas or untrimmed retrieval results.
- Output tokens jumped: inspect reasoning and tool-call arguments, then enforce an output cap or a structured response schema.
- Tool count jumped: identify loops, pagination mistakes and tools returning data that triggers another unnecessary call.
- Retries dominate: group failures by error code and fix validation, timeout or authentication causes before tuning prices.
- Cache reads disappeared: compare serialized prefixes, model or region changes and cache lifetime; do not assume the session remained eligible.
- Invoice exceeds token estimate: reconcile server-side tools, containers, storage, code execution, subagents and third-party invoices against the run trace.
A practical operating policy
Set a per-run budget, enforce hard limits in the orchestrator, and alert on both cost and quality regressions. Review the top-cost traces weekly, remove context and calls that do not improve completion, and rerun a fixed task set whenever you change models, tools, caching layout or retry logic. This measurement-first loop keeps savings tied to real workloads instead of unsupported claims about an average run or a permanently cheapest platform.
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