10 expert Lambda Vs Sagemaker LLM answers researched from official documentation. Every answer cites authoritative sources you can verify.
Lambda: general compute, 15min max timeout, 10GB memory limit, ephemeral storage, $0.0000166667/GB-sec. SageMaker Serverless: ML-optimized, no timeout, 6GB memory max per inference, persistent model loading, $0.000133/sec + $0.0002/inference. Lambda for: small models (<1GB), API gateways. SageMaker for: large models (>1GB), batch processing.
Fully managed inference without provisioning instances (2024-2025). Auto-scales 0→N based on traffic, charges per inference + idle time. Memory: 1GB-6GB, model size: <10GB compressed. Cold start: 10-60s (loads model into memory). Best for: sporadic traffic, cost optimization vs real-time endpoints. Not for: <200ms latency SLA.
Use Lambda when: (1) Model <1GB (DistilBERT, small fine-tunes), (2) Inference <15min, (3) Simple API (no complex pre/post processing), (4) Low request volume (<100/min), (5) Cold start <10s acceptable. Example: sentiment analysis with DistilBERT (250MB), text classification with small BERT variants.
Use SageMaker Serverless when: (1) Model 1GB-10GB (Llama 7B quantized, FLAN-T5), (2) Sporadic traffic (batch jobs, dev/test), (3) Cold start <60s acceptable, (4) Need GPU acceleration, (5) Existing SageMaker pipeline. Example: Llama 2 7B 4-bit quantized (4GB), FLAN-T5 XL (3GB).
Lambda: 5-10s for <500MB models (download from S3 + load). SageMaker Serverless: 20-60s for models >1GB (pull Docker image + load model). Optimization: Lambda Provisioned Concurrency (eliminates cold start, $0.0000041667/GB-sec), SageMaker provisioned instances (not serverless). Both cache models between invocations within 15min (Lambda) / 15min (SageMaker).
Example (1000 inferences/day, 2s inference time, 4GB memory): Lambda: $0.0000166667/GB-sec × 4GB × 2s × 1000 = $0.133/day. SageMaker Serverless: $0.000133/sec × 2s × 1000 + $0.0002 × 1000 = $0.466/day. Lambda cheaper for: small models, high request rate. SageMaker cheaper for: large models with idle time (no charge when scaled to 0).
Use Lambda container images (10GB limit): (1) Create Dockerfile with model + dependencies, (2) Push to ECR, (3) Create Lambda function from ECR image. Example: FROM public.ecr.aws/lambda/python:3.12, COPY model/ /opt/ml/model/, CMD ["app.handler"]. Benefits: bypass 250MB deployment package limit, use custom runtimes. Cold start: 10-30s for large images.
Quantization reduces model size for Lambda/SageMaker: (1) INT8 quantization (50% size reduction, <1% accuracy loss), (2) 4-bit quantization (75% reduction, bitsandbytes, GPTQ), (3) ONNX Runtime (optimized inference). Example: Llama 2 7B (14GB fp16) → 4GB (4-bit) fits SageMaker Serverless. Tools: Hugging Face Optimum, bitsandbytes.
Lambda can mount EFS for large models (>10GB). Benefits: share model across functions, bypass storage limits. Drawbacks: cold start +2-5s (EFS mount), throughput limits (100MB/s baseline), cost ($0.30/GB-month). Use case: multiple Lambda functions sharing 20GB+ model. Alternative: S3 mount via FUSE (s3fs), but slower.
Monitor: (1) Cold start frequency (CloudWatch metric: ColdStartDuration), (2) Inference latency (custom metric in logs), (3) Memory usage (Lambda: MaxMemoryUsed, SageMaker: ModelLatency), (4) Error rates (invocation errors, model errors), (5) Cost (AWS Cost Explorer filtered by service). Alert on: >5s P99 latency, >1% error rate, cost spike >$50/day.