> ## Documentation Index
> Fetch the complete documentation index at: https://docs.baseten.co/llms.txt
> Use this file to discover all available pages before exploring further.
# Pricing and limits
> Pricing, rate limits, budget enforcement, and usage tracking for Model APIs
Baseten enforces rate limits on Model APIs to ensure system stability:
* **Request rate limits**: Maximum API requests per minute.
* **Token rate limits**: Maximum tokens processed per minute (input + output combined). Cached input tokens count at full weight: a token served from the KV cache costs less but counts the same toward the token rate limit as an uncached one.
To monitor token and request consumption by API key, model, or service tier, see [Usage](#usage).
Default limits vary by account status.
| Account | RPM | TPM |
| :--------------------- | ------------------------------------------: | ------------------------------------------: |
| **Basic** (unverified) | 15 | 100,000 |
| **Basic** (verified) | 120 | 500,000 |
| **Pro** | 120 | 1,000,000 |
| **Enterprise** | [Custom](https://www.baseten.co/talk-to-us) | [Custom](https://www.baseten.co/talk-to-us) |
To raise your limits, [contact us](https://www.baseten.co/talk-to-us/increase-rate-limits/) to request email verification for higher Basic limits, or to move to Pro or Enterprise.
If you exceed these limits, the API returns a `429 Too Many Requests` error. See [Inference errors](/inference/errors#429-too-many-requests) for how to respond.
## Pricing
Model APIs bill per million tokens. For current per-model rates, see the [Model APIs pricing page](https://www.baseten.co/pricing). Fast tier variants have their own per-model pricing and rate limits; see [Supported models](/inference/model-apis/overview#supported-models) for details.
### Cached input tokens
Cached input tokens are prompt tokens served from the KV cache, billed at a discounted rate. Every request participates in caching automatically, with no flags or opt-in steps.
To raise cache hits across related requests, send the `x-session-affinity` header with a consistent value to pin them to the same region or replica. Scope one ID to a single conversation or full agentic task, including all subagents; reusing it across unrelated agents concentrates load on one replica and defeats the load-balancing benefit. A good ID is stable per task but uncorrelated across tasks. For example, use a SHA-256 hash of your internal conversation or session ID, truncated to 32 hex characters:
```python x-session.py theme={"system"}
import hashlib
conversation_id = "conv-7f3a2b1c" # your existing conversation or session identifier
affinity = hashlib.sha256(conversation_id.encode()).hexdigest()[:32]
```
Send the resulting value in the `x-session-affinity` header on every request in the conversation:
```bash Request theme={"system"}
curl -X POST https://inference.baseten.co/v1/chat/completions \
-H "Authorization: Bearer $BASETEN_API_KEY" \
-H "Content-Type: application/json" \
-H "x-session-affinity: 8f9ff769f61b13e167fb40ef2bae9f3b" \
-d '{
"model": "zai-org/GLM-5.2",
"messages": [
{"role": "system", "content": "You are a concise technical writer."},
{"role": "user", "content": "What is gradient descent?"},
{"role": "assistant", "content": "An optimization algorithm that iteratively adjusts model parameters by moving in the direction of steepest decrease in the loss function."},
{"role": "user", "content": "How does the learning rate affect it?"}
]
}'
```
```json Response theme={"system"}
{
"id": "chatcmpl-4a7e03f0b97e453fa5b8eeeac78b3d13",
"object": "chat.completion",
"model": "zai-org/GLM-5.2",
"choices": [
{
"index": 0,
"message": {
"content": "The learning rate determines the step size of each update. Too high, and the model overshoots the minimum or diverges; too low, and convergence becomes prohibitively slow or risks getting stuck in local minima.",
"role": "assistant"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 56,
"completion_tokens": 44,
"total_tokens": 100,
"prompt_tokens_details": {
"cached_tokens": 32
}
}
}
```
## Budgets
You can set a [monthly budget](/organization/billing#monthly-budget) to receive email notifications as your workspace's total spend approaches a configured amount. If you choose to enforce a budget, Baseten rejects Model API requests once your workspace reaches the budget. Budget enforcement doesn't affect dedicated inference or training.
## Usage
Track Model APIs usage with [`GET /v1/model_apis/usage`](/reference/management-api/model-apis/gets-model-apis-token-usage). The endpoint returns input, cached input, uncached input, and output token counts, plus request counts, in `1m`, `1h`, or `1d` buckets. Group results by API key, model, and service tier, or filter the response to specific values.
```bash Request theme={"system"}
curl --get "https://api.baseten.co/v1/model_apis/usage" \
--header "Authorization: Bearer $BASETEN_API_KEY" \
--data-urlencode "start_time=2026-08-06T12:00:00Z" \
--data-urlencode "end_time=2026-08-06T13:00:00Z" \
--data-urlencode "bucket_width=1h" \
--data-urlencode "group_by=api_key" \
--data-urlencode "group_by=model"
```
```json Response theme={"system"}
{
"items": [
{
"start_time": "2026-08-06T12:00:00Z",
"end_time": "2026-08-06T13:00:00Z",
"results": [
{
"api_key_prefix": "",
"model": "zai-org/GLM-5.2",
"service_tier": null,
"input_tokens": 14820,
"cached_input_tokens": 9200,
"uncached_input_tokens": 5620,
"output_tokens": 3200,
"request_count": 24
}
]
}
],
"pagination": {
"has_more": false,
"cursor": null
}
}
```
Usage data is available from August 5, 2026 at 20:45 UTC (`2026-08-05T20:45:00Z`). Baseten did not backfill earlier Model APIs usage.
## Next steps
Handle `429 Too Many Requests` and other status codes
Supported models and feature support