Token Observe vs Kong AI Gateway
Kong governs the traffic. Token Observe governs the action. If you already run Kong, the first one is nearly free and the second one is the only reason to read further.
2 September 2026
- Kong AI Gateway product page
- Kong AI Gateway documentation
- Kong AI Gateway: get started
- Kong AI Gateway: AI providers
- Kong plugin: AI Prompt Guard
- Kong plugin: AI PII Sanitizer
- Kong plugin: AI Rate Limiting Advanced
- Kong Gateway audit logs
- Kong Gateway deployment topologies
- Kong MCP documentation
- Kong pricing
- Kong Gateway product page
- Kong plugin: AI Proxy Advanced, semantic load balancing
- Kong Inc. trust centre
- Kong announcement: Kong Agent Gateway
Their claims, not our testing. Verify anything that decides it for you.
On this page
If Kong is already your data plane, Kong AI Gateway is the better purchase for most readers
The strongest argument for Kong has nothing to do with AI. It is that the gateway is already there, already monitored, already in somebody’s on-call rota, and the AI controls arrive on it as plugins and routes rather than as a new process with a new failure domain. Kong publishes deployment topologies covering a database-backed traditional mode, a DB-less declarative mode whose Admin API is read only, and a hybrid mode in which a control plane — Konnect-managed or self-managed — configures data planes that can be deployed “in different data centers, geographies, or zones without needing a local clustered database for each DP group”, and it publishes a figure of “50K+ transactions per second per node” for the underlying engine. Token Observe is a single Node process with one SQLite writer on one host at its current target scale: no replica, no clustering, no vendor-operated uptime SLA, and it fails closed, so its availability becomes a governance property of your environment. On operating the request path this is not a close comparison and it would be dishonest to present it as one.
Breadth is the second advantage and it is wide. Kong’s provider list names OpenAI, Azure AI, Amazon Bedrock, Amazon SageMaker, Gemini, Vercel, Anthropic, Cohere, Hugging Face, Llama, Mistral, xAI, DashScope, Kimi, Cerebras, Ollama, Databricks, DeepSeek and vLLM, with the documented caveat that “some providers may not be available or require different configuration steps depending on your AI Gateway version, and some providers don’t support all route types”. Token Observe registers six. Kong also publishes an AI MCP Proxy that “converts API schemas into MCP-compatible tool definitions” and aggregates several APIs into one MCP server endpoint, which turns an existing REST estate into governed tools — Token Observe fronts MCP servers you already run and does not generate them. Semantic caching, an AI Prompt Compressor and semantic load balancing — which Kong documents on AI Proxy Advanced as routing “incoming requests to the most relevant OpenAI model based on the content of the request” — are published Kong features that Token Observe does not have at all, and on a chat-heavy estate the first two are the line items that actually move the invoice.
There is a third advantage worth naming because it is the sort of thing a security reviewer asks about and it favours Kong on a mechanism rather than on marketing. Kong’s audit logging supports optional cryptographic signing: with “audit_log_signing_key” configured, “a lexically sorted representation of each audit log entry is signed by the defined private key, and the signature is stored in an additional field within the record itself”, validated later against the public key. That is a per-entry signature. Token Observe hash-chains its audit entries and signs only the head, periodically, with Ed25519. For the narrow question “was this single row altered”, a per-entry signature is the stronger primitive, and this page will not pretend otherwise. Everything in these three paragraphs is Kong’s own published description, read on 2 September 2026 and not independently tested; where a row elsewhere on this page looks like an absence in Kong’s product, treat it as a question to put to Kong in writing rather than as a finding.
Token Observe and Kong AI Gateway, capability by capability
The Kong AI Gateway column paraphrases Kong Inc.’s own published material as it stood on 2 September 2026. None of it has been independently tested here, products in this category ship quickly, and a capability that is absent from a vendor’s documentation is not the same thing as a capability the product lacks. Check anything that decides it for you against their own current documentation.
Where it sits in the request path
Clients call the Kong proxy URL on a path defined by the AI Model’s route configuration — the get-started guide sends POST to /v1/chat/completions on the proxy, with the base path set as config.route.paths.
Change OPENAI_BASE_URL or ANTHROPIC_BASE_URL and one key. For supported OpenAI-compatible, Anthropic and Gemini ingress that is normally the whole integration rather than an application refactor.
The gateway. Kong’s get-started guide stores the provider key on the AI Model Provider entity’s config.auth and states that “AI Gateway securely manages this credential and injects it into upstream requests automatically”.
The same shape. The agent presents a Token Observe bearer token that is SHA-256'd and looked up with a timing-safe comparison; provider keys stay server-side and never reach the agent.
One gateway for LLM, Model Context Protocol and agent-to-agent traffic — “Define a single endpoint for any traffic type: LLM, MCP, or A2A” — with A2A calls audited for “caller identity, capabilities invoked”. Kong announced Kong Agent Gateway on 14 April 2026 as the A2A capability inside AI Gateway.
Model dialects and tools: the OpenAI, Anthropic and Gemini ingress surfaces plus one Streamable HTTP MCP endpoint in front of every registered upstream MCP server.
A2A is a protocol Kong publishes support for and Token Observe’s published surfaces do not include. If agent-to-agent RPC is on your roadmap, that is a Kong row, not a tie.
Nineteen named providers, from OpenAI and Anthropic through Bedrock, SageMaker, Databricks, vLLM and Ollama, with the caveat that availability and route-type support vary by AI Gateway version.
Six first-class upstreams — OpenAI, Anthropic, Gemini, OpenRouter, Bedrock, Azure OpenAI — plus any OpenAI-compatible endpoint you register, with policy equivalence across them enforced by a table-driven test over every provider kind.
Different claims. Kong’s number is reach; the Token Observe number comes with a test that the same rule fires identically on each, because a policy that fires on OpenAI but not on Gemini is worse than no policy.
The Kong Gateway product page describes an “Ultra-lightweight, infinitely scalable NGINX engine with 50K+ transactions per second per node”. That figure is published for the underlying gateway engine; the AI Gateway product page does not carry a throughput figure of its own.
One Node process, one SQLite file in WAL mode. The only measured figure published is a laboratory baseline — 206.2 successful requests per second, 71.2 ms p50, 163.8 ms p95, over 30.143 seconds at concurrency 16 against a mock upstream on an Apple M1 Max — which is not a throughput commitment and is not offered as one.
What it enforces before the call
AI Prompt Guard scans chat messages whose role is user against allow and deny expression lists, deny taking precedence — “any request that matches an entry in the deny list will return a 400 response, even if it also matches an expression in the allow list” — with AI Semantic Prompt Guard, AI Semantic Response Guard, AI AWS Guardrails, AI Azure Content Safety and AI GCP Model Armor listed alongside it.
Nine weighted prompt-injection heuristics over Unicode-sanitised text, scored 1.25 times higher when the text arrived as a tool result, and eleven sensitive-data classes of which three are checksum-validated. Heuristics, not a classifier, with false negatives and published per-kind confidence scores so a policy can set its own threshold.
Kong integrates a choice of external content-safety services; Token Observe’s detection is deliberately small and is described in its own documentation as a compensating control rather than your only DLP.
The AI PII Sanitizer “helps protect sensitive information in client request bodies before they reach upstream services, or in LLM response bodies before they reach the client”, in placeholder or synthetic mode with optional restoration, running against the AI PII Anonymizer Service, which “can run in a Docker container”. Enterprise only.
Detection and redaction happen in-process with no side-car. Streamed responses pass a hold-back buffer with a 64-character floor and a separate channel per tool-call argument, and the cut is pulled back off any match it would split, because a card number spanning two SSE chunks otherwise escapes output redaction entirely.
Kong publishes access control on the traffic: “enforce auth for MCP server access control”, “enforce centralized AuthN/Z for all A2A traffic” and access controls for MCP tool usage, with consumers and consumer groups as the identity that policies scope to.
Deny-by-default permissions on the action itself: an agent may hold an allow on tool:orderdb/get_details and model:gpt-5o-mini while tool:payments/issue_refund is simply absent and therefore denied. An explicit deny beats every allow wherever it is written, and a delegation chain intersects rather than unions.
AI Rate Limiting Advanced limits on prompt_tokens, completion_tokens or total_tokens and on a cost computed as (prompt_tokens × input_cost + completion_tokens × output_cost) / 1,000,000, over local, cluster or Redis counters, scoped by consumer, consumer group, IP, header, path, model or provider. Over the limit returns HTTP 429. Enterprise only.
Hard USD ceilings per request, per rolling hour, per UTC day and per UTC month, reserved before egress inside one per-agent transaction after the route is resolved and every reachable fallback is priced at its most expensive rate. A budgeted route with an unpriced reachable target is refused with a 409 rather than priced at zero.
Not described in Kong’s published documentation as of 2 September 2026 on the product, AI Gateway, plugin, MCP and Agent Gateway pages read for this comparison. The nearest published mechanism is identity enforcement on the caller — “only authorized agents can initiate or participate in A2A communication” — which decides who may call rather than parking one call on a person.
A policy action of require_approval returns 403 carrying an approval id, mints a record bound to the SHA-256 of the canonicalised action plus its execution context, is single-use through a compare-and-set so two concurrent retries cannot both execute, and expires at 60 minutes by default.
The honest reading of that first cell is that these pages do not describe it, not that Kong cannot do it. Ask Kong in writing what an approval would be bound to and whether a retry with one argument changed still satisfies it.
Kong publishes “semantic caching, routing, and load balancing” for LLM traffic, and AI Proxy Advanced documents load balancing across several targets. How a provider’s content-policy refusal is classified when a chain moves on is not stated on the pages read for this comparison as of 2 September 2026.
Seven typed failure classes. A 429, a timeout and a 5xx move to the next provider; a content-policy refusal, an authentication failure, an over-long context and a malformed request stop where they are. Every fallback candidate is filtered through the agent’s retention, training and region policy before it can be used.
Ask Kong the question rather than reading the blank as an answer: a chain that treats a refusal as retryable returns the next provider’s completion as a success, and the record then contains no refusal. Kong may well classify it correctly; the pages read simply do not say.
What it records, and what the record is for
Admin API requests and “entries for all insertions, updates, and deletions to the cluster database”, carrying RBAC user identity, workspace association and request id. Documented from Kong Gateway 3.4, for on-premises deployments, disabled by default.
Every governance-plane change — an agent created, a policy widened, the kill switch engaged, an approval decided — appended to a hash chain whose entry digest covers the previous entry’s hash plus the canonical JSON of that entry’s own content, so a break names a sequence number rather than a region.
Optional per-entry signing: with an audit_log_signing_key configured, “a lexically sorted representation of each audit log entry is signed by the defined private key, and the signature is stored in an additional field within the record itself”, validated later with the public key.
SHA-256 entry digests by default; HMAC-SHA256 under a key held outside the database when an audit MAC key is set, with the head sealed by a checkpoint MAC at every boot and Ed25519 anchors published off-box on a schedule. Tamper-evident, not tamper-proof, and Token Observe reports which of the two you are holding in every verification result.
This row goes to Kong on the narrow question. A signature on every entry answers “was this row edited” directly; a chain plus a periodic anchor answers “was anything edited since the last anchor you kept somewhere the database administrator cannot reach”.
AI metrics, token usage, latency and cost calculations, Konnect Observability dashboards and OpenTelemetry integration, with A2A telemetry “on every A2A call, including payloads, latency, token usage, and errors”.
One trace per governed request holding the post-redaction prompt excerpt, tool calls and arguments, policy decisions, approvals, tokens and cost, in a timeline written for a compliance officer rather than a log line. OTLP over HTTP is ingested as an input, not offered as a competing tracing backend.
Konnect analytics and dashboards over AI traffic, with request storage and retention set by plan.
A question in English translated into a validated filter object over fourteen allow-listed fields, never into SQL, shown back as editable chips, degrading to a deterministic keyword parser when no model answers. It cannot group, count or correlate across traces, so “which agents used the same card number twice” is not a question you can ask.
Audit records are purged on a configurable TTL. The pricing page states analytics retained for 30 days on the free trial, and 10M requests per month stored on Plus.
Trace retention is unset by default, and unset means keep forever. That is a decision you should make deliberately on day one rather than discover in year two.
How it deploys and who operates it
Traditional mode with a shared database and every node acting as both planes; DB-less declarative mode where “the Admin API is read only”; and hybrid mode splitting control plane from data planes, with the control plane either Konnect-managed or self-managed.
One shape. Self-hosted in your network, one Node process, one SQLite file, no control plane anywhere else.
Konnect is a cloud-managed control plane with data planes running in your environment; the pricing page lists “fully self-hosted API Gateways available” under Enterprise.
Nowhere in the runtime. The vendor receives no product telemetry, no phone-home data, no prompts, no keys and no trace database, and the data flow is documented so you can verify that rather than accept it.
Hybrid mode lets you deploy groups of data planes across data centres, geographies or zones without a local clustered database for each group, and Kong publishes 50K+ transactions per second per node for the engine.
No replica, no clustering, no vendor-operated uptime SLA. Token Observe fails closed: if it stops, governed agents cannot call models, and the support documentation asks you to name an owner for that decision before you need one.
Kong operates a published trust centre at trust.konghq.com, which konghq.com/compliance redirects to, and that is where Kong publishes its certifications and attestations. The trust centre renders its contents in the browser and no list of standards could be read from it for this comparison, so no particular certification is claimed here in either direction. Ask Kong for the current reports, their scope and their dates, because scope and date are what an assurance answer turns on.
None held. No SOC 2, no ISO 27001, no ISO 42001, no independent penetration test. Stated first rather than under questioning, alongside a published residual-risk register and a published defect list.
What it costs and how it is licensed
Kong describes Kong Gateway as “the world’s most adopted open source API gateway” and offers it for Open Source, for Kong Enterprise and for Kong Konnect.
Source-available, under a licence that is a published template pending review by counsel in England and Wales rather than an executed grant of rights, carrying a 30-day evaluation drafted so a prospective customer’s security team may read, run and attack the software before a purchase order is raised, with no gag clause.
A free trial at “$0 for 30 days—no credit card required” with enterprise functionality and analytics retained for 30 days.
Runs fully offline against a built-in mock provider with no API keys, and seeds demo agents, policies and two weeks of traces. No price is published on this site.
On Plus, AI Gateway is listed at “$100/month per model” with a maximum of five unique LLM models; Enterprise carries “no limits” on models with custom, volume-discounted pricing.
Providers and models are configuration rather than line items: six first-class upstreams plus any OpenAI-compatible endpoint you register, with no per-model meter in the product.
Plus is charged per gateway per month, with 1 million API requests included and “$200/month per additional 1 million API requests/month” beyond that, and a maximum of “10 million API requests/month”.
No request meter in the product. What a governed request costs you is what the model provider invoices, priced per request into a ledger against whichever provider actually served it.
The AI PII Sanitizer and AI Rate Limiting Advanced pages each state that the plugin “is only available as part of our AI Gateway Enterprise offering”, and the pricing page lists AI AWS Guardrails, AI Azure Content Safety and AI Prompt Compressor under Enterprise, available on Plus as an add-on.
One product with no tiers. Permissions, the policy engine, approvals, budgets, the kill switch, the audit chain and the flight recorder are the thing, not the upgrade.
A chain of plugins and a single verdict are not the same control
Kong’s published governance model is composable by design, and that is most of why it is good: a plugin binds to a route, a service, a consumer or a consumer group, and you assemble the behaviour you want from AI Prompt Guard, the AI PII Sanitizer, AI Rate Limiting Advanced and whichever content-safety service you already buy from AWS, Azure or Google. Each plugin decides about the request in front of it. That model scales across an API estate the way a plugin ecosystem is supposed to, and if what you need is prompt filtering on three routes it will be configured before lunch.
Token Observe takes one decision instead, and the ordering around it is load-bearing rather than incidental. Eleven steps run per governed request: authenticate, resolve the agent record, open a trace so that even a blocked request is recorded, sanitise Unicode so smuggled invisible characters are stripped before anything reads the payload, scan for sensitive data and injection, take one governance verdict, enact it, route honouring the agent’s data policy, call upstream, govern any tool call the model proposes on the way back, then meter and record. The verdict at step 6 resolves five possible actions to one outcome, with a block beating an approval and an approval beating a redaction, so two rules that disagree produce a decision an auditor can read rather than whichever middleware ran last.
The practical difference shows up in the answer to a specific question: what did the system decide, and why. A chain of independent filters produces a set of outcomes that has to be reassembled after the fact into a story. A single verdict produces the story directly — the policy that fired, its trigger, the action taken, the redaction plan applied, and the trace id returned to the caller on every response. Neither approach is better in general. The one you want depends on whether the reader of the record is the engineer who wrote the route or the compliance officer who has been asked what happened.
- Shadow mode before enforcement
- Every Token Observe policy can run recording what it would have done without stopping anything, so you learn its false-positive rate before it blocks real work. Where the deployment turns the gate on, no rule may begin enforcing until a backtest of that exact rule has been replayed against recorded traffic and acknowledged by a named person.
- The response side is the hard half
- A streamed response cannot be re-decided once bytes are on the wire, so response-side policy is resolved before the first byte from the policies that could apply rather than from the classes that turn out to be present. A blocking class ends the stream with an in-band ACP_POLICY_BLOCKED frame the instant it is seen.
- Tool calls on the way back
- If the model proposes a tool call, it is evaluated against tool_call policies before it is returned, so a rule about refunds over a threshold binds even when the agent executes the tool itself. Defence in depth rather than a guarantee: Token Observe can only refuse a proposal it is shown.
Two different bets on tamper evidence, and what each one buys
Kong’s audit log and Token Observe’s audit chain solve the same problem with different primitives, and a security reviewer comparing them should be clear about which question each answers. Kong signs entries: with a signing key configured, a lexically sorted representation of each audit log entry is signed with the private key and the signature is stored in an additional field on the record, so any entry can later be validated against the public key. That is a direct answer to “was this row altered”, entry by entry, and it is documented as available from Kong Gateway 3.4 for on-premises deployments, disabled by default.
Token Observe chains entries instead. Each entry’s digest covers the previous entry’s hash plus the canonical JSON of its own content, so an edit or a deletion breaks verification at a named sequence number rather than merely somewhere; appends happen inside a transaction that also takes the chain tip, so concurrent writers cannot fork it. Configure an audit MAC key and those digests become HMAC-SHA256 under a key held outside the database; leave it unset and the chain is plain SHA-256, which an operator with write access can rewrite and recompute, and Token Observe reports which of the two you are holding in every verification result and every export. Above that sits Ed25519 anchoring, off until you configure a signing key, which periodically signs a statement of the chain head and publishes it off-box.
The claim the anchor buys is narrow and it is the only one made for it: any copy of an anchor you kept off-box beats any rewrite made after you took it. That is worth something only if the sink is somewhere the database administrator cannot reach, and it does not make anything tamper-proof. The complementary property the chain has and a per-entry signature does not is that deletion is detectable — removing a signed row leaves no gap in a set of independently signed records, but removing a chained row breaks the chain at a sequence number you can name. Neither product’s mechanism dominates the other, and the design worth buying may be both: Kong’s signature answering the per-row question on the gateway’s own configuration changes, Token Observe’s chain and anchor answering the completeness question about the governance decisions.
- What the export actually is
- A compliance export carries the traces and events for the period, approvals with approver identity and rationale, the audit entries, a chain verification result naming the sequence number of any break, and a SHA-256 digest of the bundle. Digest-sealed, not signed — durable origin evidence comes from the keyed chain plus the off-box anchor.
- An offline verifier
- One Node script, no install, no database, no network: exit 0 trusted, exit 1 not. It verifies an anchor holding only the pure core package and node:crypto, which is possible because the private half of the signing key never crosses the port boundary.
- Failing closed on corruption
- The boot sequence walks the whole chain before the process listens. A verdict of intrinsic corruption latches readiness and audit writes unavailable, governed requests receive ACP_AUDIT_UNAVAILABLE, and there is deliberately no online clear — recovery means restoring a database whose chain and independently retained head verify.
Where each one is the right answer
Choose Kong AI Gateway when
- Kong is already your API data plane. Adding AI Gateway plugins to a gateway your team already operates costs you a route and a configuration change, and standing up a second fail-closed process costs you a new failure domain.
- You need high availability, multiple zones or a managed control plane today — hybrid data planes across geographies, Dedicated Cloud or serverless gateways — none of which Token Observe offers at its current single-writer, single-host scale.
- Reach is the requirement: nineteen named AI providers, MCP tools generated from existing REST API schemas, agent-to-agent traffic, semantic caching and prompt compression are published Kong features and are not Token Observe features.
- Your governance requirement is content and consumption — filter these prompts, cap these tokens, chargeback by consumer group — and nobody has yet asked you to prove which human authorised a specific action.
Choose Token Observe when
- The agents take actions somebody answers for — a refund, a deployment, a ticket transition, a database write — and a tool returning 200 is not acceptable proof that the effect happened once.
- You need a human decision bound to one exact payload rather than to a route or an action type, single-use, expiring, with the approver recorded against the trace.
- Deny-by-default authority over each action is the control you are shopping for, so that a successful prompt injection buys an attacker only what that agent could already do.
- The reader of the record is an auditor or a compliance officer, reads of the record themselves need to be attributable, and the answer to “who says the head you are showing me is the head” has to be something other than a log file.
When you would run both
Running both is the normal answer, and Token Observe’s own roadmap says so in the instruction it gives itself: integrate above or beside Kong rather than compete on connectivity. Kong keeps doing what it is good at — the data plane, the provider fan-out, the consumer-scoped token and cost limits, the caching, the dashboards — and Token Observe governs only the agents that take consequential actions, either in front of Kong or behind it. In front, Token Observe holds the agent credential and the policy verdict and routes to Kong as an OpenAI-compatible upstream, so one place still owns provider keys and regional routing. Behind, Kong terminates and forwards the governed agents to Token Observe. Both arrangements add a hop and a second failure domain, so the decision should be deliberate rather than default: a chat assistant does not need both, and a refund agent might. Where you need another enforcement point to be shown to agree, policy scope and trigger matching can be compiled to digest-locked OPA Rego with reproducible positive and negative witnesses — an artifact that deliberately excludes permissions, budgets, approval consumption, kill switches, action precedence and side effects, because Token Observe stays authoritative for those.
The category argument sits above this one: Token Observe and llm gateways covers what the whole category does and does not do, which is the better page to read if you have not yet shortlisted a product.
The others in the same slot
LiteLLM
If you already run LiteLLM, keep it. The question that decides whether you need anything more is about the actions your agents take, not about the proxy.
Portkey
Both hold the payload before it reaches a provider. One is built to carry it to more than 250 models; the other is built to refuse it and prove afterwards who said it could go.
Cloudflare AI Gateway
Cloudflare’s gateway decides what the payload contains. Token Observe decides whether the agent that sent it was allowed to.
Envoy AI Gateway
Both hold the request. One charges the token budget once the response completes; the other reserves the money before the request leaves your network.
MuleSoft AI Gateway
Both refuse the call inline. One refuses on behalf of an endpoint, the other on behalf of an agent that has an owner.
We already run Kong. Do we have to replace it?
No, and replacing it would be work with no governance outcome attached. Kong is the data plane; Token Observe is a decision point that can sit in front of it or behind it, and its own roadmap instructs it to integrate above or beside Kong rather than compete on connectivity. The starting shape that costs least is narrow: route the handful of agents that take consequential actions through Token Observe, leave everything else pointing where it points now, and revisit when the estate tells you to.
Kong already limits tokens and cost. What does Token Observe’s budget do differently?
Kong’s AI Rate Limiting Advanced counts prompt, completion or total tokens and a cost derived from per-model input and output prices, over local, cluster or Redis counters, and returns 429 over the limit. Token Observe reserves before egress rather than counting after: the money verdict is taken last, after permissions, rate limits and policy, and after the route is resolved, so every provider and fallback the route could execute is priced and the most expensive of those rates is reserved against the agent’s per-request, hourly, daily and monthly windows inside one per-agent transaction. A budgeted route with an unpriced reachable target is refused with a 409 before egress rather than priced at zero, because an empty price table is exactly how a ceiling gets silently disarmed. The cost of a hard ceiling is stated too: one billable egress, no retry and no failover.
Does Kong AI Gateway support human approvals?
The Kong pages read for this comparison on 2 September 2026 — the AI Gateway product page, the AI Gateway documentation, the get-started guide, the AI Prompt Guard, AI PII Sanitizer and AI Rate Limiting Advanced plugin pages, the MCP documentation, the audit log documentation and the Kong Agent Gateway announcement — do not describe a mechanism that pauses a request on a named human and resumes it. The closest published control is identity enforcement on the caller, so that only authorised agents can initiate or participate in A2A communication, which answers who may call rather than whether this particular call is approved. That is a statement about what those pages say rather than about what the product does, and Kong ships quickly — Agent Gateway itself was announced in April 2026. The question worth asking Kong in writing is a specific one: if an approval exists, is it bound to an action type or to one exact payload, and does a retry with a single argument changed still satisfy it. Token Observe’s answer is the SHA-256 of the canonicalised action plus its execution context, single-use and expiring, and it is the row this comparison turns on.
Whose audit trail is stronger?
It depends on the question. Kong documents optional per-entry signing, where a lexically sorted representation of each audit log entry is signed with a configured private key and the signature stored on the record — a direct answer to whether one row was altered. Token Observe hash-chains entries so that an edit or a deletion breaks verification at a named sequence number, optionally under an HMAC key held outside the database, and anchors the head with Ed25519 to a sink off the box. Chaining detects deletion, which independently signed rows do not; a per-entry signature answers the single-row question more directly than a periodic anchor. Neither is tamper-proof, and the Token Observe documentation uses the phrase tamper-evident deliberately. If both matter to you, run both — they are recording different things anyway.
Have you benchmarked Token Observe against Kong AI Gateway?
No. Every statement about Kong on this page is taken from Kong’s own published product, documentation and pricing pages, read on 2 September 2026, and none of it has been independently tested. There has been no witnessed bake-off, and one is named in Token Observe’s own launch gates as evidence that does not yet exist. The Kong pages themselves carry version caveats — provider availability and route-type support vary by AI Gateway version — so check the current documentation rather than this page for anything you are about to depend on. What is offered instead of a benchmark is a 30-day evaluation written so a prospective customer’s security team can read, run and attack the software before a purchase order is raised, under a licence that remains a template pending review by counsel in England and Wales rather than an executed grant of rights.
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