Credits and pricing

Internalize uses one prepaid project balance for inference, learning, and memory hosting. The minimum purchase is $10; the suggested purchase is $25. There is no free trial, subscription, automatic top-up, or automatic overage invoice. Prices are in US dollars; checkout shows applicable taxes.

RatesCopy Anchor Link

These are the 2026-09-27 rates for GLM 5.3. Each request records the price version at admission. A later rate change cannot reprice an already admitted job. Requests admitted under the earlier price version retain that version for settlement.

What learning usesCopy Anchor Link

An internalization prepares a curriculum, generates teacher answers, scores them, trains an adapter, and validates the result. Sampling uses the input and output rates above. Training uses the training rate for every processed exposure, including repeated epochs. Cached input receives the lower rate only when the provider reports a cache hit. The source passage's token count alone is not the bill.

There is no completion surcharge. A candidate that fails validation still consumed compute; that completed compute is charged. A saved candidate with activation disabled or conflicted is billed for its compute and retained weights. Internal infrastructure failures are absorbed by us. An ambiguous provider outcome stays in reconciliation until usage can be established, rather than being charged from a guess.

Set a learning budgetCopy Anchor Link

Every internalization requires max_cost_microusd. This is the maximum total charge for the call, including the first 24 hours of a successfully saved memory. One dollar is 1,000,000 micro-USD. The supported per-call limit is $1–$100.

{
  "subject_id": "support-agent",
  "content": "Return labels are prepaid for all Northstar orders.",
  "max_cost_microusd": 5000000,
  "activate": true
}

This example authorizes at most $5. Admission reserves that amount against both the available balance and monthly project limit. It does not charge $5 immediately and does not guarantee the recipe will finish within $5. Work must stop before starting an operation whose conservative bound would exceed the budget. Known completed compute is charged if the limit is reached; unused credit is released after settlement.

The console shows the limit before submission. A limit is not a prediction of the final cost. Workload-specific cost estimates still need calibration against complete live runs before paid execution opens. Do not treat example budgets as a measured typical cost.

When integrating an agent, set this budget in trusted application code. Do not accept a spending limit, tenant, or memory identity from model-generated tool arguments. See agent learning.

Memory hostingCopy Anchor Link

Both serving weights and training state occupy storage. The initial reservation allows up to 32 GB for 24 hours, which is at most $0.32, within the call's limit. Settlement uses verified checkpoint sizes and retention periods. Storage from a rejected candidate is not charged to the customer.

A retained pair around 31.7 GB costs about $0.32 per day, or $9.51 per 30-day month. This is an illustrative size from a research run, not a guaranteed memory size. Source text can be short while its adapter is large.

Initial retention is 24 hours. Automatic paid renewal is not enabled. Check the version's expires_at; after expiration, inference fails explicitly rather than silently returning the base model. Continuous hosting requires an explicit funded renewal implementation before it can be offered.

Exact amountsCopy Anchor Link

All posted money uses integer micro-USD. For one request:

compute = uncached input × 12
        + verified cached input × 2.5
        + output × 30
        + training × 36

The total compute charge is rounded up once to a whole micro-USD. Cached tokens are a subset of input, not an additional charge. Storage uses decimal GB and a 30-day month, prorated by verified retention time; its aggregate is also rounded up once. Integer arithmetic is used for large byte-duration products.

For example, 1,000 uncached input tokens and 200 output tokens cost $0.018. A learning workload with 200,000 input, 20,000 output, and 60,000 training tokens costs $5.16 before hosting. These are arithmetic examples, not predictions of a typical call.

Reservations and settlementCopy Anchor Link

Inference reserves a conservative UTF-8 input bound, rendering overhead, and the maximum output allowance at the recorded rate. Learning reserves its explicit budget. Available balance is posted balance minus open reservations. Concurrent requests cannot spend the same credit.

A known terminal result settles once, including duplicate completion events. Read billing.settled, billing.pricing_version, billing.compute_microusd, and billing.storage_microusd on the job. Charges exceeding the reservation or incomplete usage remain in reconciliation and do not silently overdraw the project.

Purchases and refundsCopy Anchor Link

Only a verified Polar payment event adds net paid principal to the project. A checkout redirect never creates credits. Tax is not spendable credit. Duplicate payment events are idempotent; refunds remove credited principal and can freeze new spending when existing commitments exceed the remaining balance.

The hosted billing preview does not take a payment or add spendable credit. Paid model execution remains disabled while the native executor and payment setup are completed. See availability and spending limits.