AI & Event-Driven Workloads on Serverless

Serverless is a natural fit for AI workloads that are bursty, event-driven, and cost-sensitive, and a poor fit for the ones that are not. SleekDeploy builds LLM-backed APIs and inference pipelines on AWS Lambda, Amazon Bedrock, and the Serverless Framework, and we are candid about where the line is.

What we build

LLM and RAG APIs behind API Gateway and Lambda

The most common request: an HTTP endpoint that takes a user query, retrieves relevant context, calls a foundation model, and returns a grounded answer. We build these with the Serverless Framework's httpApi events, the Bedrock Converse API, and a vector store that matches your scale (Amazon OpenSearch Serverless, Aurora PostgreSQL with pgvector, or Amazon Bedrock Knowledge Bases when you want retrieval fully managed). Every endpoint ships with authentication, per-tenant rate limiting, and a request log you can audit.

Bedrock integration patterns

Amazon Bedrock gives you access to Anthropic, Amazon Nova, Meta, Mistral, and other model families behind one IAM-governed API. We implement the patterns that matter in production: cross-region inference profiles for capacity, model fallbacks, prompt caching where the model supports it, structured output with tool use, and Bedrock Guardrails for content and PII policy. Your prompts live in version control and are deployed with the code, not edited in a console.

Queue-buffered asynchronous inference

Not every AI job needs a synchronous answer. Document classification, embedding generation, summarization of uploaded files, and batch enrichment belong behind SQS or EventBridge, with Lambda consumers that scale to the queue depth and back to zero. This is where serverless wins decisively on cost: you pay for inference seconds, not idle GPUs. We design dead-letter queues, idempotency keys, and partial-batch failure handling so a single bad document never stalls the pipeline.

Streaming responses from Lambda

Users expect tokens to appear as they are generated. Lambda supports response streaming through Function URLs, and Bedrock's ConverseStream API delivers tokens as they are produced. We wire the two together, front the function URL with CloudFront for custom domains and caching of static assets, and handle the edge cases (client disconnects, timeouts, partial responses) that demos skip. See our tutorial: Building an LLM-Powered API on AWS Lambda and Bedrock.

When serverless is the wrong answer

We will tell you when it is:

• Self-hosted models. Lambda has no GPUs. If you need to run open-weight models yourself, that is Amazon SageMaker, ECS on GPU instances, or Bedrock custom model import, not Lambda.
• Long-running agents. Lambda's 15-minute ceiling and per-invocation billing make multi-step agent loops expensive and fragile. We move those to AWS Fargate or Step Functions, with Lambda handling the short tool calls.
• Cold starts on latency-critical paths. A cold Lambda adds hundreds of milliseconds before the first token. For chat interfaces we mitigate with provisioned concurrency, smaller bundles, and SDK client reuse; for hard real-time constraints we recommend a container service.
• Sustained high throughput. Above a steady-state request rate, an always-on container is cheaper than per-request Lambda pricing. We model the crossover point with your traffic numbers.

Guardrails and observability for AI endpoints

An AI endpoint is an API with two extra failure modes: it can be slow and expensive in ways you did not predict, and it can say things you do not want it to say. Our standard build includes:

• Bedrock Guardrails for denied topics, PII redaction, and grounding checks on RAG answers.
• Token and cost metrics emitted per request with Powertools for AWS Lambda, so you can alarm on spend, not just errors.
• Tracing with AWS X-Ray across API Gateway, Lambda, the vector store, and Bedrock, so a slow answer is attributable to a specific hop.
• Prompt and response logging with retention and redaction policies set by your compliance requirements, not by default.

Engagement options

We can scope a two-week proof of concept (one endpoint, your data, your account), build out a production pipeline, or embed a senior engineer with your team for the duration. All work is delivered in the Serverless Framework V4 configuration you already run, or in AWS CDK if you have moved off the framework.

Ready to put an LLM behind an API without a GPU bill? Contact us to start the conversation.