The AI Solution Architect Blueprint for Modular Enterprise GenAI

The shift from “LLM integration” to an enterprise AI platform

A request to “integrate an LLM” is rarely a single integration. As soon as an enterprise runs more than one model (or the same model in multiple regions), the hard parts move from application code into architecture: throughput, token budgets, quotas, availability, policy enforcement, observability, and cost attribution. You can see why in provider documentation: OpenAI measures limits across multiple dimensions (including requests per minute and tokens per minute). Microsoft documents that token and request limits for Azure OpenAI (in Foundry Models) vary by region, subscription, and model/deployment. Amazon Web Services documents that tokens are deducted against token quotas and requests can be throttled when quotas are exceeded. Google documents token-per-minute quotas for generative workloads on Vertex AI.

A practical pattern that scales is to treat GenAI consumption as an enterprise platform capability:

• A centralised AI gateway that fronts multiple providers (multi-cloud), normalises contracts, enforces security, rate limits, token budgets, routing/fallback, and observability.

• A reusable consumer SDK that makes the “golden path” easy: standard auth, retries/backoff, streaming, idempotency, correlation IDs, telemetry, and structured outputs.

This is not simply an engineering convenience. It is a risk and governance mechanism aligned to the lifecycle mindset in the National Institute of Standards and Technology AI Risk Management Framework. It also provides a consistent control point for risks enumerated by OWASP (for example, prompt injection and insecure output handling).

From point integrations to an AI control plane

Most “LLM integrations” fail in the same way distributed system integrations fail: every team solves the same cross-cutting concerns differently, the estate becomes inconsistent, and production behaviour surprises you.

The architectural forces are visible in provider mechanics:

• Rate limits are multi-dimensional. OpenAI documents limits across multiple axes (RPM/TPM and others), meaning you can exhaust request capacity even if token capacity remains (or vice versa).

• Quotas vary by geography and SKU. Microsoft documents that TPM/RPM limits are defined per region, subscription, and model/deployment.

• Token accounting is provider-specific. Amazon Bedrock documents that tokens are deducted from TPM/TPD quotas and that throttling occurs when quotas are exceeded.

• Embedding workloads are explicitly token-governed. Google documents that certain embedding quotas are token-per-minute per project rather than request-per-minute.

If each application team integrates providers directly, you multiply the amount of code and operational knowledge required just to be “correct” under throttling, quota exhaustion, and partial outages. A gateway-first architecture is a way to concentrate complexity once so the enterprise doesn’t pay for it repeatedly.

A second force is security posture. OWASP’s Top 10 for LLM applications makes clear that LLM risks are not merely model risks: prompt injection and insecure output handling are application-layer problems, and they become systemic when implemented inconsistently. That naturally pushes towards a shared enforcement point.

The centralised AI gateway pattern

Think of the AI gateway as an “airlock” between enterprise systems and model providers. The gateway’s job is not to make models smarter; it is to make consumption safer, observable, and economically bounded.

A useful way to define the gateway is by the contracts it stabilises:

• Stable internal APIs for chat, embeddings, document extraction tasks, and (optionally) tool/function invocation.

• Stable operational semantics across providers: consistent throttling responses, retry guidance, correlation IDs, and audit logs.

• Stable governance: model allow-lists, data classification routing, and tenant isolation.

This maps directly to what major API management primitives already do for mainstream APIs throttling, quotas, auth, and policy-based transformations except that GenAI adds token economics as a first-class constraint. AWS documents usage plans (throttling and quotas based on API keys), and also notes throttles/quotas are “best-effort” targets rather than guaranteed ceilings.

In Azure’s ecosystem, the link between API management and GenAI is explicit: Azure API Management documents an “AI gateway” capability set for securing, scaling, monitoring, and governing AI backends. It also provides a token-aware policy (azure-openai-token-limit) which enforces token rate and token quota and returns defined HTTP statuses (429/403). Token metrics can be emitted via policy to support observability and reporting.

Routing and fallback

Routing and fallback are often misunderstood as “load-balancing”. In practice, they are policy decisions:

• Route by data classification and residency constraints (for example, “restricted data must stay within approved providers/regions”).

• Route by capacity and quota state (for example, avoid providers returning sustained 429s). OpenAI’s rate limit mechanisms make clear that 429s can occur due to RPM or TPM exhaustion.

• Route by tenant entitlements (e.g., premium tenants get higher budget and stronger models).

One way to keep routing safe is to treat fallback as a controlled behaviour: it is enabled only for traffic that is permitted to cross provider boundaries, and only within token budgets and governance constraints.

Security and “treat the model as untrusted”

The gateway is also where you make the architectural commitment that the model is powerful but untrusted:

• OWASP documents prompt injection as a primary risk; therefore you want a policy boundary between user content and privileged actions (tool calls, data access).

• OWASP documents insecure output handling as insufficient validation/sanitisation before passing LLM outputs downstream; therefore the gateway (and SDK) should enforce safe defaults, such as structured outputs or strict schema validation before downstream consumption.

For network and data exfiltration controls, Google documents that VPC Service Controls can mitigate exfiltration risk for Vertex AI by creating a service perimeter. This type of perimeter control is easier to enforce consistently when egress to AI providers is centralised.

The reusable consumer SDK pattern

A gateway is a policy point; an SDK is an adoption point. If teams bypass the SDK, the enterprise falls back into inconsistent timeouts, retries, and logging. The SDK’s purpose is to harden the “last mile” between application code and the gateway.

A practical SDK focuses on operational semantics, not business semantics:

• Auth bootstrap: obtain/refresh credentials for the gateway and attach standard headers.

• Retries with backoff and jitter: recommended as a transient fault handling approach; Azure’s transient fault guidance describes exponential backoff as a typical strategy.

• Circuit breaker compatibility: the retry pattern should not amplify outages; retries must be bounded and should honour Retry-After where provided.

• Streaming: normalise streaming semantics (token-by-token, chunk-by-chunk) so clients don’t need provider-specific implementations.

• Idempotency: enable safe retries for asynchronous job submissions (common in IDP and indexing pipelines). HTTP semantics define idempotent methods; for non-idempotent methods like POST, idempotency tokens are a common strategy and are explicitly recommended in AWS Well-Architected guidance.

  • If you want to standardise this across teams, there is also an active IETF draft defining an Idempotency-Key header for making POST/PATCH fault-tolerant; treat it as a draft, but it provides a useful shared vocabulary.

• Correlation IDs and telemetry: propagate trace context so AI calls can be tied to end-to-end transactions. The W3C Trace Context specification documents traceparent/tracestate as a vendor-neutral format, and OpenTelemetry describes how context propagation enables correlation across signals and services.

• Structured outputs: reduce downstream parsing risk by using schema-bound outputs where supported. OpenAI documents “Structured Outputs” as adhering to supplied JSON Schema; Azure documents structured outputs in Azure OpenAI as well.

Blueprint for Intelligent Document Processing

The IDP blueprint is not about a specific document type; it is about architectural separationbetween ingestion, extraction, validation, and publication.

The reason IDP fits the gateway-first model is that industrial-scale document processing is often asynchronous and batch-oriented:

• Amazon Textract documents that multipage document processing is asynchronous and useful for large documents.

• Google Document AI provides batch (asynchronous) processing that kicks off a long-running operation and stores results in Cloud Storage.

• Azure Document Intelligence documents a batch analysis API that can bulk process up to 10,000 documents and writes results to a storage container.

A realistic, reusable IDP architecture (using the gateway and SDK) looks like this:

• Ingestion: upload to object storage, generate metadata (tenant, classification, retention, checksum), write an event.

• Extraction:

  • deterministic extraction (OCR, layout, tables) using appropriate services;

  • probabilistic extraction/summarisation using LLM calls via the gateway with token budgets and bounded retries.

• Validation: schema validation + business rules + confidence thresholds; optionally human review for low-confidence outputs.

• Publication: store structured outputs with provenance (original doc reference, page/section anchors where available), emit events downstream.

The gateway provides the non-functional guarantees: token quota enforcement, throttling, audit logging, and observability. It also provides a way to apply consistent policy decisions like “large-file summarisation must run asynchronously and consume from a dedicated budget pool”.

Blueprint for vector search and RAG

RAG is best treated as a pipeline plus a query path, not a single feature. The architecture exists to ensure the model’s answer is grounded in retrievable evidence.

Primary sources define the intent clearly:

• AWS defines RAG as optimising LLM output so it references an authoritative knowledge base outside training data.

• Azure documents RAG as grounding responses in proprietary content, while noting that implementations face challenges.

• Google documents that Vertex AI Vector Search can be used with embeddings to enhance context and accuracy by finding more relevant information.

A reusable RAG blueprint is typically separated as:

• Indexing plane: ingest documents → chunk → embed → store vectors + metadata.

• Query plane: embed query → retrieve nearest neighbours (with filters, ACL constraints) → send retrieved context to LLM → produce answer + citations.

Where the gateway-first approach helps:

• Embedding and generation calls are token-governed and rate-limited. Google documents token-per-minute quotas for certain embedding workloads, which makes “embedding ingestion” a capacity planning problem rather than a background script.

• The gateway is also the place to enforce “grounded-only” contracts (e.g., require the model to return a citations array, validate it structurally via schema, and reject answers that do not reference retrieved chunks). This reduces the chance that teams implement “citations” as a UI decoration rather than a verifiable contract.

Why modular reuse reduces duplication, cost, and risk

A gateway-first architecture is a trade: you accept a shared platform dependency to avoid multiplying risk and cost across every product team.

The reduction mechanisms are concrete:

• Duplication drops because provider invariants are centralised. Rate limits and quotas are structural provider behaviours (OpenAI multi-dimensional limits; Azure region/subscription/model quotas; Bedrock token deductions; Vertex token quotas). Centralising these controls stops each team from “learning quotas in production”.

• Cost becomes governable because token budgets become enforceable. Azure’s APIM token-limit policy shows token budgeting can be enforced at the gateway layer with defined outcomes for rate vs quota exhaustion. Bedrock documents token deduction against quota and throttling behaviour, reinforcing that token economics drive system behaviour.

• Risk posture becomes consistent. NIST AI RMF emphasises risk management as a lifecycle discipline; OWASP enumerates key LLM risks (prompt injection, insecure output handling). A gateway becomes the practical place to implement consistent enforcement, audit logging, and safe-by-default output handling.

• Observability becomes comparable across teams. The W3C Trace Context spec provides the cross-vendor mechanism for trace propagation (traceparent/tracestate), and OpenTelemetry describes how context propagation enables correlation across distributed systems. Standardising on these patterns yields consistent attribution for latency and token spend.

• Reliability improves through shared resilience defaults. Retries/backoff for transient errors and bounded retry strategies are standard reliability guidance; a shared SDK reduces variance and the risk of retry storms.

This is the architectural pay-off: you can change provider contracts, quota strategies, and model catalogues behind adapters while keeping the consumer experience stable and governed.

The point is not that a gateway eliminates complexity; it decides where complexity lives. A modular, gateway-first approach chooses to keep complexity in a place where it can be owned, tested, observed, and governed once rather than rediscovered across every team.