AI Gateway Strategy: Offline, Online, and Agentic LLMs

An offline summarization job, a customer-facing chat experience, and an AI agent may call the same model API, but they should not share the same traffic policy. Batch work prioritizes throughput and unit cost. Interactive requests prioritize response time and fairness. Agent workflows create bursts, chains of dependent calls, and larger failure domains.

An AI gateway gives platform teams a common enforcement point for model access. The useful strategy is not to apply one policy to every request. It is to classify the workload first, then assign routing, quotas, fallbacks, and telemetry that match its operating objective.

This guide turns the offline, online, and semi-online framework described in Modal's LLM Engineer's Almanac into practical AI gateway decisions. In this article, agentic refers to the bursty, system-to-system portion of the semi-online category.

Three LLM Workload Types, Three Operating Objectives

The workload label is a planning tool, not a protocol. A single product may contain all three types, and a request can move from one class to another as it passes through a workflow.

Workload	Typical examples	Primary objective	Signals to watch
Offline	Bulk summarization, evaluation runs, enrichment pipelines	Complete large jobs at an acceptable cost	Tokens per job, throughput, queue age, provider errors
Online	Chat, copilots, search answers, voice interactions	Respond consistently within a user-facing latency budget	Time to first token, p95/p99 latency, stream failures, rejection rate
Agentic or semi-online	Tool-using agents, document workflows, event-driven analysis	Finish a multi-step task within an end-to-end deadline	Burst concurrency, tool and model errors, retry count, task completion time

The distinction matters because a generic route can create avoidable contention. A batch job can consume a shared provider quota while interactive users are waiting. A retry policy that is reasonable for a background task can multiply calls inside an agent loop. A cache that helps repeated support questions may be unsafe for user-specific prompts.

AI Gateway Policy Matrix

Start with separate routes, consumers, or policy groups for each workload. They may still use the same gateway cluster and model providers, but they should have independent budgets and observable service objectives.

Policy area	Offline batch	Online interactive	Agentic or semi-online
Routing goal	Cost and available capacity	Predictable latency and quality	Capacity headroom and task continuity
Traffic isolation	Job or team quota	User, tenant, or application quota	Agent, workflow, and tenant quota
Fallback	Accept a slower alternative if output remains compatible	Use a bounded fallback that fits the response deadline	Limit attempts across the whole workflow, not only each model call
Caching	Useful for repeated deterministic inputs	Use only when freshness and user isolation allow it	Usually selective; tool state can make responses context-dependent
Telemetry	Tokens, cost, throughput, queue age	Time to first token, latency percentiles, stream completion	End-to-end duration, model calls per task, tool errors, retry budget
Backpressure	Pause or slow job dispatch	Reject or shed load predictably	Cap concurrency before fan-out exhausts downstream systems

This matrix is a starting point. Set actual thresholds from measured provider behavior and product service-level objectives, not from generic values copied from another deployment.

Strategy 1: Isolate Offline and Batch Workloads

Offline work can usually wait, but it can also consume a large number of tokens. The gateway should keep that traffic from exhausting the quota needed by interactive applications.

Route for cost without hiding quality requirements

Create a dedicated batch route and authenticate the service or job owner. Multi-provider routing can prefer an approved lower-cost model, while a fallback can preserve availability when that provider is rate-limited or unhealthy. The output contract must remain compatible: switching providers is unsafe when a downstream pipeline depends on a provider-specific schema, tokenizer, safety behavior, or model capability.

Apache APISIX's ai-proxy-multi plugin supports multiple LLM instances, weighted load balancing, retries, fallbacks, health checks, and LLM access-log fields. Use those mechanisms only after testing the same prompts and structured outputs against every eligible model.

Separate dispatch from gateway traffic control

An AI gateway can authenticate, route, limit, and observe an LLM request. It is not a batch scheduler. Queue ownership, job state, delayed execution, and replay belong in a worker or orchestration layer outside the gateway.

That separation prevents a misleading design in which a request accepted by the gateway is assumed to be safely queued. The application should record job state before dispatch and decide how to resume or compensate after a timeout.

Measure the whole job

Track model usage at the gateway, but join it with application-level job data. Useful measures include:

input and output tokens per completed job;
cost per successful output, not only cost per request;
queue age and execution time;
provider rejection and fallback rates;
validation failures after a provider or model switch.

Strategy 2: Protect Online Inference Latency

Interactive workloads need a latency budget based on the user experience. There is no universal time-to-first-token target: a voice assistant, code completion tool, and research interface have different tolerances.

Keep the route and failure budget narrow

Give interactive traffic its own route and quota. Prefer a model-provider path that meets the measured latency and quality target, then define a small number of compatible fallbacks. Each additional retry consumes time, quota, and possibly money, so cap retries within the remaining request deadline.

Streaming can improve perceived responsiveness, but it changes failure handling. Once the gateway has started sending a stream to the client, switching to another provider may not produce a coherent response. Test disconnects, partial responses, and client cancellation instead of treating streaming as a simple Boolean optimization.

Apply fair-use controls at the right identity

Request-level limits do not reflect the different token cost of short and long prompts. Token-aware limits can protect shared capacity more directly. Apache APISIX's ai-rate-limiting plugin supports prompt-token, completion-token, total-token, and expression-based strategies. Policies can be scoped with variables and, in a distributed gateway deployment, backed by Redis-based counters.

Choose a key that represents the entity you intend to protect, such as a tenant, consumer, or application. A global limit alone can allow one customer to degrade every other customer's experience.

Do not assume gateway stickiness preserves model KV cache

Prefix-aware routing can improve cache reuse inside an inference platform when requests reach the same compatible model replica. A gateway route alone cannot guarantee that outcome across external providers or opaque provider backends. Treat KV-cache affinity as an end-to-end inference-platform feature and verify it with provider or self-hosted runtime telemetry.

Strategy 3: Bound Agentic and Semi-Online Bursts

Agent workflows can fan out into multiple model and tool calls. A single user action may generate a burst even when request volume at the product edge looks small.

Budget the workflow, not just an individual call

Set limits for concurrent workflows, model calls per workflow, tokens per tenant, and the maximum end-to-end duration. A per-call retry policy is not enough: three retries at five steps can turn one task into many downstream calls.

Pass a workflow or trace identifier through the gateway so logs can be correlated with the agent orchestrator. The orchestrator should own task state, tool selection, compensation, and the overall retry budget. The gateway should enforce traffic policy at each model boundary.

Reserve capacity and make overload explicit

Separate agent traffic from interactive chat so a tool-use spike cannot consume the entire online quota. Multi-provider fallback may add headroom, but only when the alternate model satisfies the workflow's tool-calling and output requirements.

When capacity is exhausted, return an explicit rejection that the orchestrator understands. Avoid wording such as "retrying automatically" unless the component returning the response is actually responsible for that retry.

Minimize sensitive telemetry

Agent prompts and tool results can contain credentials, customer data, or internal system context. Log model, token, timing, route, and outcome fields by default; record prompt or response payloads only under a reviewed data policy. For a broader control checklist, see AI Gateway security and AI Gateway observability.

A Workload-Aware Architecture

The gateway is one layer in the system. Schedulers and agent orchestrators stay outside it, while traffic policy is centralized at the model boundary.

flowchart LR
    B[Batch scheduler and workers] -->|batch route| G[AI gateway]
    U[Interactive application] -->|online route| G
    A[Agent orchestrator] -->|agent route| G

    G --> P1[Approved provider A]
    G --> P2[Approved provider B]
    G --> S[Self-hosted model service]
    G --> T[Metrics and logs]

    B --> J[Job state store]
    A --> W[Workflow and tool state]

The three routes can share authentication infrastructure and telemetry pipelines while retaining different routing pools, quotas, and failure budgets. If you need a concrete multi-model pattern, see routing local Qwen and cloud models through an AI gateway.

What an AI Gateway Can and Cannot Do

Clear component boundaries prevent a sound traffic policy from becoming an unreliable application architecture.

The gateway can	The gateway cannot do by itself
Authenticate callers and apply route or consumer policy	Schedule GPU capacity or guarantee provider latency
Route across configured, compatible model instances	Prove that two models return equivalent results
Enforce request or token quotas	Own durable batch-job or agent-workflow state
Apply bounded fallback and health-based routing	Preserve KV cache across unrelated providers
Emit model, token, timing, and outcome telemetry	Make every retry safe after partial or side-effecting work

This boundary also changes how teams respond to incidents. A provider 429 may trigger an approved fallback at the gateway. A tool call that created a ticket before timing out requires workflow-level idempotency or compensation; a model retry cannot solve it.

Rollout Checklist

Use an incremental rollout rather than moving every LLM call behind one new policy at once.

Inventory traffic. Identify callers, models, providers, prompt sensitivity, streaming use, and output contracts.
Classify workloads. Label each route as offline, online, agentic, or a documented hybrid.
Define objectives. Select a small set of workload-specific service and cost signals.
Separate identities and quotas. Prevent batch and agent bursts from consuming interactive capacity.
Test model compatibility. Evaluate quality, schemas, safety behavior, and tool use before enabling fallback.
Bound retries. Include the gateway, SDK, orchestrator, and provider retries in one failure budget.
Protect telemetry. Confirm which fields may be logged and how long they are retained.
Canary the policy. Start with a limited tenant or traffic percentage and compare against a control group.

How to Verify the Strategy

Do not promise a fixed performance multiplier. Establish a baseline and compare equivalent traffic before and after each policy change.

Workload	Baseline and validation measures
Offline	Completed jobs, tokens per completed job, cost per output, queue age, fallback rate
Online	Time to first token, p95/p99 latency, stream completion, rejection rate, user-visible errors
Agentic	Task completion, calls and tokens per task, tool failures, retry count, end-to-end duration

Segment the results by model, provider, route, tenant, and response status. A lower average cost is not an improvement if fallback quality drops or retries increase the cost per successful task.

Conclusion

Workload-aware AI gateway strategy starts with isolation. Offline pipelines need cost and throughput controls, interactive products need predictable latency and fairness, and agentic systems need bounded concurrency and workflow-level failure budgets. Those policies can share one gateway platform without becoming one undifferentiated configuration.

AISIX AI Gateway provides a central place to manage model traffic and apply workload-specific access, routing, and observability policies. Keep scheduling and workflow state in their appropriate application layers, validate every fallback, and tune the gateway from measured production behavior.