AI Gateway for Batch Inference and Fine-Tuning

Most AI gateway examples show a synchronous request: choose a model, send a prompt, and receive a response. Batch inference and fine-tuning do not fit that shape. An application uploads a file, creates a job, polls an identifier, may cancel it, and later downloads output. The work can outlive a client connection, a gateway process, or an application deployment.

That lifecycle creates a routing question. A follow-up request such as GET /v1/batches/{id} has no model field. The gateway still needs to know which provider, credential, and model context owns the resource. It also needs to authenticate the caller, enforce access, preserve useful telemetry, and avoid recording the same completed usage twice.

AISIX AI Gateway addresses this for OpenAI-compatible Files, Batch, and Fine-tuning APIs by treating the resource lifecycle as a first-class traffic path rather than a one-off proxy call.

Key Takeaways

• Batch inference is a multi-step resource lifecycle, not a large synchronous request.
• The upstream provider executes the batch or training job; the AI gateway governs caller access, provider credentials, routing, policy, and telemetry.
• Follow-up calls often contain only a file or job ID, so routing context must survive after the original upload or create request.
• Usage accounting must distinguish zero-token management calls from completed inference and must remain idempotent across repeated polling.
• Provider-native batch and fine-tuning APIs differ. A gateway should publish an explicit compatibility boundary rather than promise universal translation.

Batch AI Traffic Is Not Just a Large Synchronous Request

A synchronous request carries its routing input in one message. A batch workflow instead creates linked files, jobs, status checks, cancellation requests, and output downloads. Each operation has its own authorization and failure modes, while the provider's work continues independently of the original client connection.

Fine-tuning follows a similar lifecycle. The gateway does not run inference or train the model; the upstream provider owns the compute, job states, and completion target. The gateway keeps submission and follow-up calls on an authenticated, observable route and returns the provider's status without promising its service level.

Map the Files, Batch, and Fine-Tuning Lifecycle

Start by modeling the complete lifecycle rather than implementing only create.

The OpenAI Batch guide uses JSONL input files containing individual requests. The batch names an endpoint and completion window, and the application later retrieves output and error files. Validate limits and pricing against the selected provider instead of copying fixed quotas into application code.

With AISIX Batch, Files, and Fine-Tuning, the application first supplies a routing model when it uploads the file:

curl -sS -X POST "https://gateway.example.com/v1/files" \
  -H "Authorization: Bearer $AISIX_CALLER_KEY" \
  -H "x-aisix-model: batch-openai-prod" \
  -F purpose=batch \
  -F file=@batch-input.jsonl

The gateway returns an opaque file ID. The application uses that ID to create the batch:

curl -sS -X POST "https://gateway.example.com/v1/batches" \
  -H "Authorization: Bearer $AISIX_CALLER_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input_file_id": "aisix-opaque-file-id",
    "endpoint": "/v1/chat/completions",
    "completion_window": "24h"
  }'

The endpoint and completion window remain provider contracts. The Azure OpenAI Batch reference, for example, documents Azure-specific fields and authentication.

Preserve Routing Context Across Long-Running Jobs

The initial upload names an AISIX model alias, but later operations may contain only an ID:

GET /v1/files/{file_id}
GET /v1/batches/{batch_id}
POST /v1/batches/{batch_id}/cancel
GET /v1/fine_tuning/jobs/{job_id}

AISIX-created IDs begin with aisix- and carry the route needed for follow-up calls. Applications should store them unchanged and never treat their internal representation as an authorization claim.

sequenceDiagram
    participant App as Batch application
    participant GW as AISIX
    participant P as AI provider
    App->>GW: Upload file + model alias
    GW->>P: Upload with provider credential
    P-->>GW: Provider file ID
    GW-->>App: Opaque AISIX file ID
    App->>GW: Create batch with AISIX file ID
    GW->>P: Create batch with decoded route
    P-->>GW: Provider batch ID
    GW-->>App: Opaque AISIX batch ID
    App->>GW: Retrieve batch by AISIX ID
    GW->>P: Retrieve from owning provider
    P-->>GW: Current provider status
    GW-->>App: Current status

Resource ownership follows the route encoded at creation, even if an operator later changes a default. For a raw provider ID, supply an explicit model hint instead of relying on a fallback that may select the wrong account.

Fine-tuning adds one naming trap: the create body's model is the provider's base model, not an AISIX alias. The encoded training-file ID carries the gateway route.

Apply Security and Policy to File-Based AI Workloads

Batch files can contain thousands of prompts, customer records, evaluation examples, or proprietary training text. Treat them as sensitive data assets, not temporary transport objects.

A defensible control path should:

• Authenticate every upload, create, list, retrieve, cancel, and content request.
• Grant the caller only the approved model aliases and routes.
• Keep upstream provider credentials in the gateway, separate from caller keys.
• Restrict file purpose, type, size, and accepted endpoint where supported.
• Validate data before upload; encrypt it, set retention, and delete inputs and outputs when their purpose ends.
• Log identity, resource IDs, status, and policy results without copying file bodies into request logs.

One file can repeat a policy mistake thousands of times. Current AISIX Files routes do not content-scan multipart uploads, upload responses, or downloaded files with guardrails, although Batch and Fine-tuning JSON requests retain their route coverage. Scan every JSONL record before upload and outputs after download, then recheck this boundary for the deployed release.

Access must follow the full resource lifecycle. An opaque ID is an identifier, not a secret or authorization grant. Fine-tuning also needs dataset provenance, licensing, sensitivity, approval, evaluation, and deletion controls outside the gateway.

Attribute Batch Usage and Cost Correctly

Counting API calls does not measure batch cost. Management calls may consume no inference tokens, while one completed batch can contain thousands of billable lines.

A useful accounting model separates three event classes:

1. Management events: upload, create, retrieve, cancel, or download operations that may carry zero tokens.
2. Provider job state: queued, running, completed, failed, expired, or cancelled, using the provider's actual vocabulary.
3. Completed usage: billable consumption aggregated from successful output when the provider supplies it.

AISIX records zero-token usage events for file and job management calls. When a batch retrieval first observes a completed job, AISIX downloads the output, aggregates per-line token usage by provider-billed model, and emits usage events. A deterministic batch request ID prevents repeated retrieval or a gateway restart from double-counting the same completed work.

This supports the allocation model in AI Gateway Cost Control, but provider billing remains authoritative. Preserve an “unknown” state when output lines fail or usage is missing instead of reporting missing data as zero cost.

Budgets and rate limits need asynchronous semantics. A request limit on GET /v1/batches/{id} controls polling, not queued compute, and token usage may be known only after completion. Submission-time budget checks cannot prevent an already accepted job from finishing above the threshold.

Design Reliable Polling, Cancellation, and Recovery

Polling should back off with jitter, honor provider retry guidance, and stop on terminal states. Fixed, frequent polling wastes gateway and provider capacity.

Cancellation is a request, not always an immediate terminal state. Preserve provider states, partial output, and per-line errors; work completed before cancellation may remain billable.

Test recovery across process boundaries:

• restart the application or gateway while work is active;
• retrieve a completed job repeatedly;
• race cancellation with completion;
• handle raw provider IDs and missing usage;
• rotate credentials while jobs remain active.

AI Gateway observability should distinguish authorization, routing, credentials, and upstream failures while recording endpoint family, operation, status, provider, alias, caller, latency, and correlation IDs—never file contents.

Run Batch and Fine-Tuning APIs Through AISIX

AISIX exposes first-class routes for Files, Batch, and Fine-tuning lifecycle operations. The supported endpoints reference is the source of truth for the deployed release.

Current first-class support covers providers configured through the OpenAI-compatible adapter, including custom compatible API bases, and Azure OpenAI. Anthropic-native, Amazon Bedrock, and Google Vertex AI batch or training APIs use different request, storage, and job models and are not translated through these routes.

This boundary reflects real differences in file ownership, job states, storage, output formats, cancellation, and training artifacts. Use an explicit passthrough route for unsupported provider-native APIs and document which policies and telemetry still apply.

Production Checklist for Batch AI Jobs

• Model upload, create, poll, cancel, output, and deletion operations.
• Treat gateway-created resource IDs as opaque and persist them unchanged.
• Require deterministic routing for raw provider IDs.
• Separate the routing alias from a fine-tuning job's provider base-model field.
• Keep caller keys and upstream provider credentials independent.
• Define file purpose, sensitivity, retention, deletion, and ownership.
• Validate JSONL and policy before submitting large workloads.
• Back off polling and preserve provider terminal states and partial results.
• Distinguish management events from completed inference usage.
• Make completion accounting idempotent across retries and restarts.
• Reconcile gateway usage with provider billing.
• Test credential rotation and restarts while jobs are active.
• Publish the supported provider and endpoint matrix for the deployed version.

Batch inference and fine-tuning expand AI traffic beyond the request-response path most gateways were designed to handle. The durable unit is a resource lifecycle: files and jobs continue to exist while callers, credentials, deployments, and policies change.

AISIX AI Gateway gives that lifecycle stable caller authentication, provider credential isolation, route continuity, policy, and usage attribution for supported OpenAI-compatible and Azure OpenAI endpoints. The provider still owns the compute and job contract. Keeping that division clear is what makes long-running AI workloads governable rather than merely proxied.