Choose an LLM API by modeling your real workload, then verifying account limits and cash-flow rules. A low token price can be a poor fit if your product sends large repeated prompts, needs high throughput, depends on batch or tools, or cannot tolerate a prepaid balance running out. Estimate requests, input tokens, output tokens, cached context, modalities and tools for both an ordinary and a peak month. Apply each provider’s current rates, then confirm the quotas, spending controls, payment method and regional requirements on the actual account you will use.
The figures below are dated illustrations from official documentation reviewed October 13, 2026. Anthropic’s list-price sheet is dated May 27, 2026. Model catalogs, rates, quotas and billing policies can change, so verify live documentation before signing a contract or publishing a forecast.
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What you are actually choosing
An LLM API decision has four separate parts: the model’s workload cost, the capacity available to your account, the way money moves through the account, and the deployment constraints around data and geography. Treating the decision as a contest between two input-token prices hides the risks that usually affect a startup launch.
- Workload cost: input and output tokens, cached input, cache creation, long-context pricing, image or audio processing, tool calls, batch jobs and priority service.
- Throughput: requests per minute (RPM), tokens per minute (TPM), daily token limits, burst behavior and separate quotas for batch or other features.
- Billing exposure: free access, prepaid credit, postpaid invoicing, minimum top-ups, spending caps, alerts and the error returned when a limit is reached.
- Deployment fit: required region, routing, data-residency policy, regulated endpoints and any price uplift for those choices.
- Operating model: latency and availability targets, retries, monitoring, key-level attribution and a fallback plan if one provider is unavailable.
A public price page is not a quote for your account. Tier qualification, billing history, account age, model availability and provider approval can change the limits you receive.
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Start with a workload model, not a headline rate
Build a simple usage sheet before comparing vendors. Use measured traffic if you have it; otherwise create low, expected and peak cases. Keep the input and output sides separate because they are normally priced differently.
Record the variables that drive cost
- Requests per day and per month, split by product feature.
- Input tokens per request, including system instructions and conversation history.
- Output tokens per request and the maximum output allowed by your application.
- The percentage of requests that repeat a long system prompt or retrieved context and could qualify for caching.
- Requests that use images, audio, structured outputs, web search, code execution or other paid tools.
- The share that can run asynchronously in a batch service instead of the interactive path.
- Retry rate, timeout rate and the extra tokens generated by retries.
- Geography, regional-processing or regulated-endpoint requirements.
Calculate a normal month and a peak month. A launch, marketing campaign or batch import can produce several times the average traffic. A provider that is cheapest in the normal case may be unsuitable if its quota blocks the peak case.
Use a transparent cost equation
For each model and service tier, calculate:
monthly cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate) + cache charges + tool or modality charges + service-tier charges
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Run the equation separately for uncached and cached context. Cache creation may have its own charge, while later cache reads may use a lower rate. Long-context requests can move into a different price band. Do not assume that a prompt is cheap merely because most of it is repeated; confirm how the provider bills cache writes and reads.
Dated price examples
The following figures are useful for building an initial worksheet, not for ranking providers. They are USD per million tokens where stated and reflect the named model, context and service conditions.
| Provider and model | Standard input | Standard output | Important qualifications |
|---|---|---|---|
| OpenAI gpt-6.1, short-context standard | $1.00 | $5.00 | Official pricing table accessed October 13, 2026. The table separately lists cached input, cache writes and higher long-context rates; those figures are not included here. |
| Google Gemini paid tiers | Not stated in the reviewed billing material | Not stated in the reviewed billing material | Google’s documentation emphasizes account-level RPM, TPM and spend-based limits, billing history and tier caps. Confirm the exact model price in the live model pricing table. |
| Anthropic Claude Sonnet 4.6, global standard | $3.00 | $15.00 | Anthropic list-price PDF dated May 27, 2026. Batch-processing row lists half these token prices: $1.50 input and $7.50 output. Verify that the model and endpoint remain available. |
For example, 20 million input tokens and 4 million output tokens in a month would be $40 plus $20, or $60, at the OpenAI rates above before cache, tools, long-context or regional charges. The same token volumes at the Anthropic standard rates would be $60 plus $60, or $120. A batch-eligible workload at Anthropic’s listed batch rates would be $30 plus $30, or $60. These are arithmetic illustrations, not forecasts of your actual bill.
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Why averages can mislead
Suppose a support assistant has a 6,000-token conversation history but most requests add only a 200-token question. A forecast based on the question alone understates input spend. Conversely, a cache that serves the repeated history at a reduced rate can make the effective cost lower than an uncached calculation. Measure the distribution of prompt sizes, not just the mean: a small number of very long requests can dominate the bill and the TPM quota.
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Check limits before you promise a launch capacity
Throughput limits are operational constraints, not footnotes. For every model you plan to call, record the values shown in the provider console for your account.
| Limit to record | Why it matters | What to test |
|---|---|---|
| Requests per minute (RPM) | Caps concurrent user actions even when prompts are short. | Send a controlled burst and observe when 429 responses begin. |
| Tokens per minute (TPM) | Long prompts or outputs can exhaust capacity with relatively few requests. | Test your largest realistic prompt and output combination. |
| Daily token or request limit | Can stop a service late in the day despite unused RPM. | Confirm whether a daily ceiling exists for the model or feature. |
| Batch quota | Asynchronous work may have separate capacity and completion windows. | Submit a representative batch and verify acceptance and completion behavior. |
| Spend or usage cap | A payment limit can block calls even when technical quota remains. | Check the account console and the error returned near the cap. |
Google documents RPM, TPM and spend-based rate limits separately. Its paid-tier documentation also describes account-level caps of $250 at Tier 1, $2,000 at Tier 2 and $20,000–$100,000 or more at Tier 3. Those are documented tier caps, not a guarantee that a new startup receives the corresponding production throughput. Qualification depends on factors such as cumulative payments and account age.
Anthropic documents tier-based limits and spending-limit errors. Its API error reference says HTTP 429 can indicate either a rate limit or a usage-tier spending cap, while HTTP 402 indicates a billing or payment problem. Your retry logic must distinguish these cases: retrying a payment failure will not restore capacity.
Ask for increases early
Find the quota-increase procedure before traffic arrives. Providers may require billing history, an account review or a higher tier. Submit the request with measured RPM, TPM, peak concurrency, expected monthly spend and the date you need the capacity. Keep a lower-volume mode ready while approval is pending.
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Billing mechanics can be more important than a small difference in token price. Map the path from a successful request to the point where service stops.
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Free access
Google states: “New accounts begin on the Free Tier, which allows access to certain models in the Gemini API and AI Studio, up to the models’ free tier rate limits.” The statement is model-specific. Confirm that the model you need is eligible and that the free limits cover development traffic; do not design production economics around an allowance intended for testing.
Prepaid accounts
Google’s billing documentation says paid access involves linking a billing account and, where the prepaid model applies, making a minimum $5 prepayment. Determine the minimum balance, automatic top-up behavior, payment-failure handling and how quickly calls resume after a balance is replenished. A prepaid account can prevent an unexpected invoice but can also interrupt production when the balance reaches zero.
Postpaid or invoiced accounts
For postpaid arrangements, check the billing cycle, payment terms, credit review, hard caps and who can change them. Ask whether a card failure immediately blocks requests or starts a grace period. Record the escalation contact and the exact console alert that appears when spending is limited.
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Set a monthly budget below the maximum amount your company can safely lose to a bug or abusive traffic. Add alerts at several thresholds, such as 50%, 80% and 100%, and route them to someone who can disable a feature. Keep separate keys or projects for development, staging and production so one experiment cannot consume the production budget.
Account for geography and endpoint premiums
Some products require regional routing, data residency or a regulated endpoint. OpenAI documents a 10% uplift for certain regional-processing and FedRAMP endpoints. Anthropic’s price sheet distinguishes scopes and endpoint categories. Include the applicable uplift in the same workload calculation as token charges; otherwise a compliant deployment can appear artificially cheap.
Document where requests are processed, where logs are retained, which subprocessors apply and whether the selected model is offered in the required region. A model that is technically excellent but unavailable in your customers’ required geography is not a viable choice.
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Compare operational fit, not only price
Latency and availability
Measure time to first token, total completion time and timeout rates under your own prompt sizes. Record results by model, region and time of day. Published prices do not establish your latency or uptime; production measurements do.
Retries and idempotency
Implement bounded exponential backoff for transient rate-limit responses, but do not blindly retry billing errors. Include an idempotency strategy for actions that can create side effects, such as sending an email or updating a record. A retry can otherwise execute the business action twice even if the model call appears harmless.
Usage attribution
Log provider, model, API key or project, feature, request ID, input tokens, output tokens, cache status, tool usage, latency, response status and estimated cost. Redact customer content where required. This lets you find which team or feature created a cost spike instead of treating the whole product as one undifferentiated bill.
Fallback engineering
A second provider can protect availability, but it adds prompt adaptation, output differences, monitoring and two billing systems. Define the conditions for failover: sustained 429 responses, a provider outage, a regional restriction or a budget threshold. Test the fallback with the same acceptance suite used for the primary model; otherwise a nominal fallback may produce unusable output.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical selection procedure
- Define the product promise. Write the required quality, maximum response time, supported regions, privacy constraints and peak concurrency.
- Inventory request types. Separate chat, extraction, classification, batch generation, tool use and multimodal calls. Assign each its own token and latency distribution.
- Measure or estimate traffic. Produce low, expected and peak monthly requests, input tokens and output tokens. Include retries and unusually long conversations.
- List service features. Mark which requests can use caching, batch processing, priority modes or tools. Record any separate charges.
- Calculate cost by provider and model. Use the exact context band, endpoint, region and service tier. Show normal and peak totals rather than one average.
- Inspect the live account console. Capture RPM, TPM, daily, batch and spend limits for the account and model you will actually deploy.
- Verify billing mechanics. Record free eligibility, prepayment minimum, top-up behavior, billing cadence, hard caps, alerts and payment-failure response.
- Run a controlled load test. Test realistic prompt sizes, concurrency, retries and peak bursts. Capture 429, 402 and timeout behavior.
- Review deployment constraints. Confirm region, data handling, regulated endpoints and any documented price uplift.
- Choose with a written decision rule. For example: select the lowest-cost option that meets quality, peak TPM, regional and recovery requirements with a defined budget reserve.
Common failure modes and fixes
The estimate uses only input tokens
Symptom: the invoice is much higher than the forecast. Fix: separate input and output tokens, include system prompts and conversation history, and log actual distributions.
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Symptom: development works, then launch traffic receives quota errors. Fix: verify model eligibility and account-specific RPM, TPM and spend limits; obtain a paid tier or quota increase before launch.
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A rate-limit error is retried forever
Symptom: retries increase traffic and worsen the outage. Fix: honor retry-after guidance when provided, cap attempts, add jitter and switch to a queue or fallback path. Treat 402 billing errors as a payment workflow, not a transient capacity event.
Long context silently changes the price band
Symptom: a small number of long conversations dominate spend. Fix: track prompt length percentiles, set a context budget, summarize old turns and calculate long-context rates separately.
A prepaid balance expires at the worst time
Symptom: production calls fail after the balance reaches zero. Fix: configure automatic top-up where appropriate, maintain a reserve, alert before depletion and document who can add funds.
The fallback model is never tested
Symptom: failover returns malformed or lower-quality responses. Fix: run both models through a golden test set, validate schemas and rehearse the switch under load.
Decision matrix for a startup
| If your priority is… | Emphasize | Evidence to require |
|---|---|---|
| Lowest predictable unit cost | Input/output rates, caching and batch eligibility | Workload calculation using your measured token distribution |
| Fast consumer-facing responses | Latency, burst RPM and priority capacity | Load-test results at peak concurrency |
| Large asynchronous jobs | Batch price and batch quota | Completion behavior and half-rate eligibility where documented |
| Strict spending control | Prepay, hard caps, alerts and payment-error behavior | Console settings and a tested low-balance scenario |
| Regulated or regional deployment | Endpoint availability, residency and uplifts | Written confirmation for the required region and endpoint |
| High availability | Quota headroom, incident history, retries and fallback | Failover test with validated output quality |
What to put in the procurement record
Save a dated snapshot of the model name, context class, input and output rates, cache rules, batch or priority terms, endpoint, region, account tier, RPM, TPM, daily and spend limits, billing method, minimum top-up, alert settings and support path. Record the access date next to every number. This prevents a six-month-old spreadsheet from being mistaken for a current quote.
Recheck the official pricing and API documentation immediately before launch, after a model migration and whenever monthly spend changes materially. The Anthropic Sonnet 4.6 figures cited here come from a May 27, 2026 list-price PDF; the OpenAI and Google figures cited here were displayed or documented on October 13, 2026. Rates, model names, quotas and payment mechanics are volatile.
Bottom line
Pick the API that fits your complete operating envelope, not the one with the lowest isolated token price. A defensible choice has a workload-based cost model, verified account quotas for the peak case, a billing path your cash flow can tolerate, documented regional compliance and tested recovery behavior. Keep the calculation and account limits under review because both pricing and capacity can change.
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