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Where the proxy sits in a real-time RAG request
A real-time RAG request has several hops. The user’s query reaches your application, the application retrieves passages from an index, it assembles a prompt, and then it calls a model. The proxy governs the last hop, from application to model provider. Kong describes its AI Gateway as handling format conversion, credential injection, load balancing, and cost and token tracking for LLM traffic, with authentication, observability and policies applied across traffic classes (Kong AI Gateway architecture). Apache APISIX describes a similar gateway scope and states that workflow state, tool selection and model-quality evaluation remain in the surrounding stack (Apache APISIX AI Gateway).
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| Concern | Proxy responsibility | Application responsibility |
|---|---|---|
| Provider access | Holds and injects provider credentials; Kong also converts request formats | Deciding which users, tenants or services may call which models |
| Target selection | Applies a routing policy across configured targets | Classifying requests into the classes that deserve different targets |
| Retries and failover | Retries within configured limits and moves to a fallback target | Making sure the business action behind a model call is safe to repeat |
| Retrieval | Optional. APISIX documents an ai-rag plugin for an Azure OpenAI and Azure AI Search flow | Chunking, indexing, reranking and context assembly in most stacks |
| Caching | APISIX documents Redis-backed exact matching and optional semantic matching | Cache-key design that reflects tenant, authorization and content versions |
| Quality | Not a gateway responsibility in the APISIX description | Answer quality, grounding checks and regression tests |
| Workflow and tools | Outside gateway scope | Agent orchestration, tool selection and state |
Choosing a routing policy
Kong defaults to round robin and also lists consistent hashing, least connections, lowest latency, lowest usage by token count or cost, semantic prompt routing, and priority weighted failover. Apache APISIX documents weighted round robin, consistent hashing, and semantic routing based on prompt similarity to per-instance examples. The table shows what each platform documents in the pages cited for this article.
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|---|---|---|---|---|
| Round robin | Default | Weighted round robin | Targets are interchangeable in capability and cost | Ignores differences in latency, load and price |
| Consistent hashing | Documented | Documented | A conversation or user should keep landing on one target | Hot spots when a few keys dominate; pinned traffic does not move off a degraded target by itself |
| Least connections | Documented | Not stated on the APISIX page | Long streaming generations hold connections open | Connection counts do not measure how quickly a target responds |
| Lowest latency | Documented | Not stated on the APISIX page | Interactive answers where responsiveness matters most | “Latency” must be defined first (see the streaming section) |
| Lowest usage (token count or cost) | Documented | Not stated on the APISIX page | Budget limits matter more than the fastest path | Cheaper targets may answer long-context queries worse; check quality per class |
| Semantic routing | Documented | Documented (prompt similarity to per-instance examples) | Request classes map cleanly onto model tiers | Similarity is not quality; define evaluation and fallback |
| Priority weighted failover | Documented | Fallback documented; priority ordering not stated on the APISIX page | You have a primary target and a backup with a clear preference order | The backup may quietly serve traffic at a different quality or price |
Session continuity
Affinity and consistent hashing keep a conversation’s turns on one target. LLM Gateway’s routing documentation lists several session-key inputs, including session headers and request fields (LLM Gateway routing documentation). That is one implementation’s design, not a standard shared by all gateways, so confirm which key your gateway uses before depending on it. Pinning trades flexibility for consistency: a pinned session cannot follow a healthier target without an explicit exception, so define when affinity yields to failover.
Latency
Decide what the route optimizes: time to first token, total response time, or availability. LLM Gateway’s documented latency mode uses time to first token for streaming requests and falls back to uptime for non-streaming requests. That split is a useful pattern, but it belongs to that product. A “lowest latency” policy elsewhere may measure something different.
Usage and cost
Kong can balance by token count or by cost. Usage-based routing protects budgets, but it sends load toward whichever target looks cheapest at the moment of the decision. Pair it with a quality check for each request class so budget routing does not move hard queries onto a weaker tier without anyone noticing.
Prompt fit
Semantic routing selects a target by how similar a prompt is to examples attached to that target. For example, short factual lookups and multi-passage synthesis could be mapped to different models. Similarity is a selection signal, not a quality guarantee. Document how the router behaves on prompts that match no example well, and what fallback applies.
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Streaming and latency measurement
Streaming is a first-class requirement, not a flag. Kong lists realtime streaming among its supported traffic types. The Inference Gateway documentation describes server-sent event streaming with token-level deltas, tool-call chunks and final usage metrics. That is the project’s own description, so check the behavior against the release you deploy (Inference Gateway documentation).
Record four measurements per request rather than one average:
- Time to first token: from the proxy receiving the request to the first token being forwarded. This is the pause users feel before anything appears.
- Gaps between tokens: stalls mid-stream that a total-time figure hides.
- Total completion time: from request receipt to the final token or end of stream.
- Outcome and attempts: whether the request succeeded, how many attempts it took, and which target answered.
A single average can hide a slow stream start behind a fast finish, or the reverse. Time to first token measured at the proxy is also not the same as time to first token in the browser or client; the difference is network and client-side time. Decide which end you measure and label it on every dashboard.
Retries, failover and circuit breaking
As of October 2026, Kong’s architecture page states that the data plane retries five times by default on an upstream error or timeout, then fails over to another target. Its passive circuit breaker is optional and off by default, and it has no active health probes (Kong AI Gateway architecture). Apache APISIX describes bounded retries and fallback strategies for selected upstream failures. These are product defaults, not recommendations. Set your own limits from your provider’s throttling behavior and your latency budget.
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Bounded retries
Every retry adds load to a target that is already failing or throttling. A retry policy that is reasonable for a timeout can be harmful for a rate-limit response, so configure limits per error type wherever the platform allows it. Check whether the platform waits between attempts. The Kong architecture page cited here does not describe a delay schedule.
Failover order
Set the sequence explicitly:
- Retry the same target only for errors that are likely transient, and only within the bound you set.
- Fail over to a backup target that accepts the same request format and has been evaluated for the same request class.
- When every target is exhausted, return a bounded error the application can handle, rather than an open-ended wait.
- Record which target served each answer, so a fallback does not stay invisible in your metrics.
Partial streams
A retry or failover after the first token has reached the client can produce duplicated or discontinuous output. Decide in advance whether a stream may be retried only before its first token is forwarded. After that point, ending the stream with an error the client can display is usually the safer behavior. Also confirm what happens upstream when the client disconnects. The sources cited here do not establish whether generation is cancelled in that case.
Repeating a model call is usually harmless; repeating the work around it may not be. If the application acts on a model response, such as writing a record or calling a tool, a second response can trigger a second action unless the application deduplicates by request ID. Keep side effects keyed to that ID.
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Circuit breaking
Kong’s passive breaker reacts to failures observed in live traffic. It sends no synthetic probes, so the first requests after an outage are what trip it. Because it is off by default, enable it deliberately and test its thresholds against real provider errors. The Apache APISIX page cited here does not describe a circuit breaker, so verify that behavior separately if you use that platform.
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Apache APISIX documents an ai-rag plugin with an Azure OpenAI and Azure AI Search flow (Apache APISIX AI Gateway). It is a supported integration, not the required shape of a RAG system. If your retrieval uses a different index, or your reranker sits in the application, the gateway’s plugin does not replace that work.
APISIX also documents response caching with Redis-backed exact matching and optional semantic matching. The capability is established; a safe caching policy is not something the page provides. At minimum, a cache key should include:
- tenant and user or authorization scope, so one user’s answer is never served to someone without access to the underlying documents;
- the model identifier and version;
- the prompt template version;
- a retrieval identity, such as the document versions or index snapshot the answer was grounded on, so a changed source does not keep serving stale answers.
Set a time-to-live and an invalidation path for document updates. Semantic matching raises a further question: two similar-looking questions can have different correct answers. Enable it only for workloads where you have measured that it does not return wrong answers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Observability
When AI proxy logging is enabled, APISIX documents summaries of model, latency, token usage and time to first token. Kong documents cost and token tracking and structured telemetry across gateway traffic. Whichever platform you use, log at least:
- route, provider and model name;
- outcome, including error class, number of attempts, and whether failover occurred;
- time to first token for streams, and total latency;
- input and output token counts where the provider returns them;
- the request class from your classifier or routing rule, so latency and cost can be compared by workload.
Prompts and retrieved passages are often the most sensitive data in a RAG system, and they are also the data you need to debug answer quality. Define a logging policy before enabling body logging: what is stored, for how long, who can read it, and whether passages are stored in full or referenced by document ID. Neither the Kong nor the APISIX documentation sets a universal retention policy, so that decision rests with you.
Choosing models by workload
OpenAI’s deployment checklist advises: “Choose the model that performs well on representative tasks rather than routing every request to the most capable model.” That is provider guidance, and the method generalizes to any provider: measure candidate models on your own traffic, then route by the results (OpenAI API deployment checklist).
- Sample real queries from each request class, including the hard cases: long contexts, ambiguous questions, and queries where retrieval returns little.
- Run each candidate model on the same retrieved context and score answers for correctness and grounding against reference answers.
- Record time to first token, total time and cost per answer for each candidate.
- Choose the least expensive model that meets the quality bar for each class, and make it that class’s default target with a defined fallback.
- Repeat the comparison when a provider changes a model or when your retrieval changes, since either can shift the results.
Vendor examples and setup
Kong AI Gateway’s getting-started guide requires AI Gateway and authenticates its tutorial with a Konnect personal access token. It separates a provider entity, which holds the connection and credentials, from a model entity, which carries the routing configuration. The guide lists 2.0 as the minimum AI Gateway version it documents (Kong AI Gateway getting started). The configuration pattern is:
- Declare a provider and its credentials.
- Define a model and a route for it.
- Map the model to an upstream target.
This sequence is Kong’s. Self-hosted and other gateways use different objects and commands, so treat it as a pattern to compare against rather than a portable procedure.
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Apache APISIX is licensed under Apache 2.0 and built around an extensible plugin model. Its documented plugins cover proxying, routing, token controls, RAG, prompt controls and logging. For RAG workloads, the ai-rag, caching and logging plugins are the relevant ones.
LLM Gateway and Inference Gateway also document routing and streaming behavior of their own (LLM Gateway routing documentation, Inference Gateway documentation). This article does not rank any of these products. The documentation establishes what each one offers; it does not establish which performs better under your traffic.
Quick Recap
Deployment checklist
- Match the gateway version you run to the version whose documentation you read. Retry counts and breaker state are version- and product-specific.
- Define request classes, a target for each, and a named fallback.
- Configure retry bounds per error type, and confirm how the platform spaces attempts.
- Decide whether a stream may be retried after its first token reaches the client.
- Enable the circuit breaker deliberately and test its thresholds against real provider errors.
- Include tenant, authorization, model version, prompt version and document version in every cache key before enabling caching.
- Set a logging and retention policy for prompts and passages before enabling body logging.
- Check each provider’s rate limits and cap concurrency below them.
- Rerun workload evaluation before changing any default route.
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