Generative AI and serverless: reference architectures for scalable products

Combining generative AI and serverless means seeking the balance between elastic cognitive power and an infrastructure that can adapt without overprovisioning. Product teams value rapid prototyping, while platform teams require an architecture that is manageable, observable, and governed. This article proposes reference architectures and a clear path to move from idea to a robust system.

The key is to orchestrate interactions between models, data, and guardrails while benefiting from serverless: pay-as-you-go billing, autoscaling, and reduced operational time. But these advantages come with hidden costs if you ignore model limits, end-to-end latency, and fine-grained inference cost management. We will detail effective patterns and their variants.

The guide is structured into practical sections: foundations, application patterns, security and reliability, then industrialization. Each concept is explained with the why and the how, along with realistic examples for B2B or B2C products.

GenAI + serverless architectural foundations

A serverless GenAI architecture rests on three pillars: request orchestration, contextual data management, and prompt governance. The why is simple: models are powerful but non-deterministic, so the architecture must absorb uncertainty and guarantee a controlled output. The how relies on stateless functions, context storage, and AI-centered observability services.

The first choice is the execution model. Serverless functions are suitable for preprocessing, enrichment, moderation, and response assembly. A model can be called via a managed API or a private endpoint. In both cases, calls must be instrumented (latency, cost, tokens) and the runtime isolated. This allows each step to be optimized independently and budgets adjusted to load.

Context management is the second pillar. Generative AI needs reliable, fresh, and segmented data. Typically you use document storage, a vector index, and a cache to avoid recomputing embeddings or re-executing identical prompts. This improves relevance and reduces the bill while offering more stable response times.

Finally, prompt governance prevents technical debt. Prompts must be versioned, tested, parameterized, and traced. The orchestration server can store templates and reinject safe variables. This pattern guarantees explainability and reproducibility, two major production requirements.

// Orchestration simple avec validation du prompt et log coût/latence
export async function handler(event) {
  const { userId, question } = JSON.parse(event.body);
  const prompt = buildPrompt({ question, policy: "no_pii" });

  const start = Date.now();
  const response = await callModel({ prompt, maxTokens: 600 });
  const durationMs = Date.now() - start;

  await logAiMetrics({
    userId,
    durationMs,
    tokens: response.usage.total_tokens,
    costUsd: estimateCost(response.usage.total_tokens)
  });

  return {
    statusCode: 200,
    body: JSON.stringify({ answer: response.text })
  };
}

Reference patterns and data flows

Reference architectures generally fall into three patterns. The "serverless RAG" pattern combines an ingestion pipeline, a vector index, and a generation runtime. It is ideal for support assistants or internal copilots. The why: knowledge changes quickly, and models must rely on an external source of truth. The how: each question triggers a vector search, then an enriched prompt.

The "controlled agentic" pattern orchestrates several specialized functions. Planning, execution, and validation are separated. This structure limits drift by making each step observable. For example, one function selects tools, another executes API queries, and a final one synthesizes. This makes testing and traceability easier.

The "batch + streaming" pattern is useful for newsletters, reports, or automatic summaries. Heavy tasks run in batch, while the rendering is streamed to the user. The why is to reduce perceived time and stabilize cost. The how is to split responses into segments with a progress controller.

In all patterns, moderation is essential. A serverless pipeline can include input and output filtering. Define business rules, risk thresholds, and automatic review for sensitive cases. This step protects the brand and supports compliance.

// Pipeline RAG minimal : retrieval + prompt enrichi
export async function ragHandler(event) {
  const { question } = JSON.parse(event.body);
  const queryEmbedding = await embed(question);
  const docs = await vectorSearch(queryEmbedding, { topK: 5 });

  const prompt = `
Tu es un assistant expert. Utilise les documents suivants:
${docs.map(d => `- ${d.title}: ${d.snippet}`).join("\n")}
Question: ${question}
  `.trim();

  const answer = await callModel({ prompt, temperature: 0.2 });
  return { statusCode: 200, body: JSON.stringify({ answer: answer.text }) };
}

// Exemple d’agentique contrôlé avec étapes séparées
export async function agentRouter(event) {
  const { task } = JSON.parse(event.body);
  const plan = await callModel({ prompt: `Planifie: ${task}`, maxTokens: 200 });
  const toolsResult = await executeTools(plan.text);
  const finalAnswer = await callModel({
    prompt: `Résume ces résultats: ${toolsResult}`,
    temperature: 0.1
  });

  return { statusCode: 200, body: JSON.stringify({ answer: finalAnswer.text }) };
}

Security, compliance, and reliability

Security is central because generative AI handles sensitive data and can produce uncontrolled outputs. The why: data leaks and hallucinations directly impact trust and compliance. The how: apply least privilege, encrypt data at rest and in transit, and add a semantic validation layer.

A compliance pipeline should include prompt audits, PII detection, and response logging. For example, store logs in an anonymized way and apply a retention policy. This approach meets internal requirements and eases legal reviews.

Reliability is built with guardrails: timeouts, retries, circuit breakers, and fallback options. If the model is unavailable, a predefined response or a classic search engine can take over. The goal is to avoid user-visible outages and maintain a realistic SLA.

Finally, supply chain security is often overlooked. Third-party models, SDKs, and tools must be versioned and validated. It is recommended to freeze production versions and plan upgrade cycles like any critical component.

// Masquage simple de PII avant journalisation
function sanitizePrompt(prompt) {
  return prompt
    .replace(/\b\d{10,16}\b/g, "[ID_REDACTED]")
    .replace(/\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b/gi, "[EMAIL_REDACTED]");
}

Industrialization, costs, and observability

Industrializing a serverless GenAI architecture means making performance predictable, costs manageable, and quality measurable. The why is operational: without fine-grained observability, costs explode and incidents multiply. The how relies on dedicated metrics: tokens, latency per step, prompt success rates, and automatic quality scores.

Cost management must be integrated into the design. Choose models suited to criticality, set budgets per feature, and adjust prompt length. Simple techniques like context compression or pre-answering with a lighter model reduce spend without degrading perceived value.

Monitoring should be product-oriented, not just technical. You can track human correction rate, resolution time, or adoption rate of an assistant. These KPIs provide a clear view of ROI and guide iterations.

Continuous prompt delivery is a specific topic. You need semantic non-regression tests and validation datasets. Serverless makes it easy to isolate versions to compare performance. This creates a continuous improvement cycle that safeguards quality.

// Extrait de calcul de coût et budget de tokens par requête
const MAX_TOKENS = 800;
function enforceTokenBudget(prompt) {
  const tokens = estimateTokens(prompt);
  if (tokens > MAX_TOKENS) {
    return trimPrompt(prompt, MAX_TOKENS);
  }
  return prompt;
}

Use cases and scenarios

In a support center, a serverless GenAI assistant can propose instant answers. The why: reduce handling time and standardize quality. The how: ingest articles, build a vector index, then generate with a strict prompt. In case of doubt, the assistant hands off to a human agent.

For a marketing service, personalized content generation can be automated. Ingest customer segments, generate variants, and control legal mentions. Serverless allows batch campaigns without overprovisioning. The benefit is faster execution and more consistent tone.

In the industrial sector, an engineering copilot can summarize technical reports and propose actions. The model is useful, but it must be constrained by guardrails. Outputs are validated by a rules pipeline and, if necessary, by human review.

These cases show that architecture is not an end in itself. It must serve a clear value proposition: time saved, higher quality, or better personalization. The reference architectures described here provide a foundation to adapt these systems to each context.

// Stream de réponse pour améliorer l’expérience utilisateur
export async function streamHandler(event) {
  const { question } = JSON.parse(event.body);
  const stream = await callModelStream({ prompt: question });

  return new Response(stream, {
    headers: { "Content-Type": "text/event-stream" }
  });
}

Deployment checklist and best practices

Before deployment, validate functional quality: answer accuracy, prompt stability, and edge-case control. The why: a GenAI system is sensitive to input variations. The how: build a test corpus with real examples, then automate periodic evaluations.

Then, secure the runtime. API keys must be managed in a vault, logs must be encrypted, and sensitive traces masked. The orchestration server should be isolated and observed, with alerts on latency or authentication errors.

Finally, document limits. An assistant should explicitly state what it can do, what it cannot do, and how it uses data. This reduces risk and improves user adoption.