> For the complete documentation index, see [llms.txt](https://k-ai.gitbook.io/knowledge-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://k-ai.gitbook.io/knowledge-ai/operate/scaling.md).

# Scaling & quotas

K-AI is designed to scale horizontally without customer-side tuning. This page summarises what scales automatically, what is physically isolated, and which quotas you can configure.

## How K-AI scales

* **Stateless services scale horizontally.** The management plane, the audit and retrieval services, the authentication service, the web crawler, and the billing surface are stateless and run as multi-replica deployments. No sticky sessions; identity travels in every request.
* **LLM serving is tier-managed.** K-AI operates the LLM service with automatic failover between an on-cluster primary, an on-cluster fallback, and a managed endpoint of last resort. Customers do not configure tiers — the service routes requests transparently.
* **Indexation is ephemeral per document.** Each document is parsed in its own short-lived workload, then released. A malformed file cannot poison a shared worker, and concurrency scales with available capacity rather than a fixed pool.
* **Per-instance physical isolation prevents noisy-neighbour effects.** Each K-AI Instance has its own compute, its own vector index, and its own object storage. A bulk re-ingest on one instance cannot block queries on another.
* **Bulk-ingestion throughput is governed centrally.** For large initial backfills (multi-million-document estates), K-AI raises ingestion capacity for the duration of the backfill and returns to baseline afterwards. Coordinate timing with your account team.

## Quotas you can configure

K-AI enforces quotas at three levels. SaaS deployments come pre-configured for the typical mid-to-large customer envelope; on-premise and Snowflake Native App deployments inherit the same defaults and can be tuned per environment.

| Level                        | What it controls                                                                                                   | How to change it                                                     |
| ---------------------------- | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------- |
| **Per-organisation KCU cap** | Total monthly consumption across all of an org's instances and cost types.                                         | Configurable on request via your account team.                       |
| **LLM rate limits**          | Per API key (Instance API), per user (Retrieval / Audit), per IP (auth surfaces). Protects against runaway agents. | Defaults are sized for production workloads; adjustments on request. |
| **Vision rate limits**       | Per-key call rate and image-size cap for multimodal requests.                                                      | Defaults are sized for production workloads; adjustments on request. |

Quota events surface in the K-AI Consumption Monitoring System as standard cost events — your dashboards reflect them without extra configuration.

## What this means in practice

For customers, scaling is transparent in the steady state. The cases that benefit from coordination with K-AI are:

* A planned backfill of more than a few hundred thousand documents.
* An expected step-change in MCP traffic (new agent rollout, new copilot deployment).
* A change to your monthly KCU envelope.

For all of these, contact your account team in advance so the platform team can size accordingly.