Kong Konnect vs Self-Hosted Kong: Where AI Request Logs and Prompt Data Live

Start with data flow, not features

Kong Konnect uses a SaaS control plane with data planes that are either run by the customer or managed by Kong. Fully self-hosted Kong places the control and data planes in infrastructure the customer operates. For AI traffic, that difference matters less as a product distinction than as a boundary distinction: every component the customer does not operate is a place where data may be processed or stored under someone else's retention and access controls.

Two separations keep the comparison honest. The first is control plane versus data plane. The control plane holds configuration and management state. The data plane handles live requests and responses. The second is telemetry versus payload. Telemetry describes an interaction, for example timing, status, route or token counts. Payload is the interaction itself: the prompt text, the model response and any documents or tool output included in them. Metadata can be low risk while payloads carry personal, confidential or regulated content, so the two need separate handling decisions.

A third distinction applies to both models: default versus configured behaviour. What a gateway records out of the box and what an operator turns on through a plugin or setting are different facts, and each should be recorded in the design for the specific version in use.

Data categories by deployment model

Use the table as a worksheet. For each row, the useful output is a named location, a named owner and a named retention setting in each model you are considering.

• Configuration and policy state: Konnect: held in a vendor-operated control plane, so confirm what configuration is stored there and in which region. Self-hosted: held in a datastore or declarative files that you operate.
• Telemetry and analytics metadata: Konnect: determine whether request metadata and token usage metrics are sent to a hosted analytics service, and under what retention. Self-hosted: metadata stays where your own metrics and monitoring pipeline sends it.
• Prompt and response payloads: Both: establish whether payloads are captured at all, by which plugin or setting, and whether they ever leave the data plane. In a customer-run data plane, payloads transit your infrastructure. In a vendor-managed data plane, the vendor operates that infrastructure.
• Log destinations: Both: log-forwarding configuration decides which sink receives records. The sink's region, access model and retention belong to whoever operates it, which is usually the customer in either model.
• Retention and deletion: Konnect: separate retention for any hosted analytics data and for your own sinks. Self-hosted: a single owner, but every setting must be built and enforced by your team.

Default versus configured logging

Prompt and response capture should be treated as its own control, separate from request metadata logging. In most gateway designs, payload logging is a plugin or configuration decision, and the same request can produce a metadata-only record or a full-content record depending on that setting. Architects should document the state of that setting per route, per environment and per consumer, rather than assuming a global behaviour.

Log-forwarding is the second decision. Once records are forwarded to a log aggregator, storage service or security monitoring tool, the gateway is no longer the system of record for that content. Prompts that were never meant to persist in the gateway can end up persisting in several downstream systems, each with its own administrators, backups and retention schedule. In both deployment models, the practical exposure is often the sink rather than the gateway.

Redaction order also matters. If content is masked only after it has been written to a log, the unmasked content has already been stored. The design question is whether sensitive content is removed before it is logged or forwarded, and which component performs that step.

Retention, residency and who owns them

In a fully self-hosted model, residency and retention are entirely the customer's to configure and enforce. That gives full control over region and deletion schedules, and it also means no part of the outcome is inherited. Storage encryption, access roles, backup copies and deletion jobs all have to be built and evidenced by the operating team.

In a Konnect model, responsibility is split. Any data held by the hosted control plane or analytics service is subject to the region and retention options that service offers, while data in customer-run data planes and customer-configured sinks remains the customer's responsibility. A Kong-managed data plane shifts operation of the data plane itself to the vendor. For each hosted component, the architect needs to know the region it runs in, how long data is kept, and how that can be changed or verified.

Prompts frequently contain personal data, so retention and deletion duties under applicable privacy rules can attach to prompt and response stores. A general security outcome also applies: NIST CSF 2.0 includes the outcome that log records are generated and made available for continuous monitoring. That supports having logs, but it does not say where they should reside or whether they contain content.

Where gateway logs end and evidence begins

Either deployment model can answer where logs live. Neither answer establishes what policy applied to a given interaction, whether sensitive content was allowed, redacted or blocked, or whether a record can be presented to an auditor as a durable account. Logs describe traffic. Policy enforcement and evidence are separate requirements that sit alongside the logging design.

Trussed addresses that requirement. It enforces policy in the execution path across models, agents, tools and MCP servers: allow, redact, block, rate limit, or escalate for human approval, applied to individual interactions and tool calls. Enforcement is at runtime, not reviewed after the fact. Each decision generates audit-ready evidence, mapped to frameworks including HIPAA, NAIC, NYDFS, the EU AI Act and NIST. Trussed deploys self-managed in the customer cloud, with configurable retention and data residency, so evidence and the data it describes remain inside the boundary the regulated enterprise defines.