You have a model. You need to deploy it on AWS. You ask which service to use and get three answers: SageMaker, Bedrock, or Lambda. All three can technically work, but picking the wrong one costs you months of unnecessary complexity or thousands in wasted spend.
The decision tree is simpler than AWS’s documentation makes it seem. SageMaker is for teams building custom models who need full ML infrastructure. Bedrock is for teams using foundation models who want managed inference. Lambda is for teams with small models who prioritize simplicity over specialized ML features.
But the real-world decision involves tradeoffs around cost, latency, team skills, and operational overhead that aren’t obvious until you’ve deployed at scale. Most teams pick based on familiarity rather than fit.
When Bedrock is the Right Choice
Amazon Bedrock makes sense when you’re working with foundation models and don’t want to manage infrastructure. You get API access to models from Anthropic, Meta, Cohere, AI21, Stability AI, and Amazon. You pay per token or per image. You don’t provision servers.
The ideal Bedrock use cases are straightforward: text generation, summarization, chatbots, content moderation, RAG applications, and image generation. If your application is “take user input, send to LLM, return response,” Bedrock handles this well.
Bedrock’s strengths become clear at scale. You don’t worry about cold starts. You don’t manage model loading. You don’t tune instance types. AWS handles all capacity planning. Your code makes API calls. The model inference happens somewhere you don’t see.
The pricing model is consumption-based. Claude 3.5 Sonnet costs $3 per million input tokens and $15 per million output tokens. Pricing varies by provider and region, so check the Bedrock pricing page for current rates on other models. You pay for what you use, with no minimum spend.
Bedrock’s model flexibility has expanded significantly. As of 2025, Bedrock supports custom model hosting for organizations fine-tuning foundation models on their own data, bridging the gap with SageMaker for some workloads. You can import custom models or fine-tune base models, though you’re still working within Bedrock’s managed inference framework. If you need complete control over the inference stack or a specific open-source model not in the catalog, you need a different deployment approach.
Latency is acceptable but not exceptional. First-token latency for Claude 3.5 typically runs 200-400ms, though this varies by region and current load. This works fine for chatbots and content generation but might not be fast enough for real-time applications with strict SLAs.
The integration story is smooth if you’re already in AWS. Bedrock connects easily to CloudWatch for logging, IAM for access control, and VPC for network isolation. You can set up guardrails to filter harmful content. Bedrock Agents and Knowledge Bases (now in v2) provide orchestration for complex workflows and RAG applications without building custom infrastructure. These features have become central to production LLM deployments in 2025.
When SageMaker is the Right Choice
SageMaker is AWS’s full-featured machine learning platform. You use it when you’re building custom models, need specialized infrastructure, or require production ML workflows.
The classic SageMaker use case is custom model deployment. You trained a model for fraud detection, recommendation systems, or forecasting. You need to deploy it with specific hardware, autoscaling, and monitoring. SageMaker provides this infrastructure.
SageMaker offers multiple inference options. Real-time endpoints give you persistent instances for low-latency predictions. Serverless inference scales to zero when idle but spins up on demand. Asynchronous inference handles long-running predictions with queuing. Batch transform processes large datasets offline.
Real-time endpoints make sense for applications needing consistent low latency under steady traffic. You provision instances (CPU or GPU), deploy your model, and get an HTTPS endpoint. The instances run continuously, so you pay hourly regardless of traffic. A single ml.g5.xlarge instance costs about $1.41/hour (us-east-1 as of October 2025; check your region), or roughly $1,030/month.
Serverless inference is SageMaker’s answer to variable workload patterns. You don’t provision instances. SageMaker scales capacity based on traffic, down to zero when idle. You pay for compute time and the number of requests. The cold start is typically 1-3 seconds for the first request, which matters for some applications. Note that Serverless inference currently supports CPU instances only – GPU workloads require Real-time or Async endpoints.
The flexibility extends to model formats. SageMaker supports TensorFlow, PyTorch, scikit-learn, XGBoost, Hugging Face, and custom containers. If you can package your model in a Docker container, you can deploy it on SageMaker.
Multi-model endpoints let you deploy multiple models on a single endpoint, reducing costs when you have many small models. SageMaker loads models dynamically based on requests. This works well for scenarios like per-customer models or A/B testing.
The monitoring capabilities go beyond basic metrics. SageMaker Model Monitor tracks data drift, model quality, and bias. CloudWatch provides standard metrics. You can set up automated retraining pipelines when drift is detected. SageMaker Inference Recommender helps right-size your instances for optimal cost-performance, while HyperPod simplifies the operational overhead of training infrastructure for teams building custom models.
The cost structure is straightforward but can add up quickly. You pay for instance hours, data processing, and data transfer. A production deployment with autoscaling across availability zones can easily run $2,000-10,000/month depending on instance types and traffic.
SageMaker makes less sense for simple use cases. If you’re just calling a foundation model API, Bedrock is simpler and cheaper. If your model is small and doesn’t need specialized ML infrastructure, Lambda might be sufficient.
When Lambda is the Right Choice
Lambda becomes viable for AI deployment when your model is small, your latency requirements are reasonable, and you value simplicity over specialized ML features.
The size constraint is real. Lambda functions have a 10GB uncompressed container image limit (stored in ECR) and 10GB ephemeral storage. Your model, dependencies, and runtime need to fit within these limits when decompressed. This works for small BERT models, lightweight classifiers, or models compressed through quantization or distillation.
Typical Lambda AI use cases include text classification (sentiment analysis, spam detection), small NLP models (named entity recognition, text extraction), simple recommendation models, and lightweight computer vision (image classification with MobileNet or similar).
The deployment process is simpler than SageMaker. Package your model and inference code in a container image, push to ECR, create a Lambda function pointing to the image. No ML-specific concepts needed. Standard Lambda skills apply.
The cost advantage appears at low to moderate traffic. Lambda pricing is $0.0000166667 per GB-second. A function with 10GB memory running for 2 seconds costs $0.00033 per invocation. At 10,000 monthly invocations, that’s $3.30. A SageMaker real-time endpoint with the smallest GPU instance costs $1,030/month regardless of usage.
Cold starts remain the main challenge. For naive setups, the first invocation after a period of inactivity can take 10-30 seconds for a Lambda function loading a large model. Enable Lambda SnapStart (available for Java 11+, .NET 8, and Python 3.12+ in select regions) to cut cold starts to sub-second in many cases. For large models without SnapStart support, expect several seconds to load unless you redesign to stream or offload model hosting. Provisioned Concurrency eliminates cold starts but adds hourly costs that quickly approach SageMaker pricing.
Optimization techniques help. Store model weights in EFS and mount to Lambda. Use smaller models through distillation. Quantize models to reduce size. Load models lazily only when needed. These approaches can reduce cold starts to 3-5 seconds.
Lambda lacks specialized ML features. No model monitoring. No A/B testing infrastructure. No built-in autoscaling for GPU workloads. You implement these yourself or accept their absence.
The integration story is standard AWS. Lambda connects to S3 for data, DynamoDB for state, API Gateway for HTTP endpoints, EventBridge for event-driven inference. If you already use Lambda, adding AI inference feels natural.
Quick Decision Heuristic
Here’s a fast rule-of-thumb to guide your initial choice:
If your model is: – Under 1GB → Start with Lambda – A foundation model → Use Bedrock – Custom and GPU-intensive → Choose SageMaker
If your traffic pattern is: – Sporadic or low-volume → Lambda – Moderate and unpredictable → Bedrock or SageMaker Serverless – High and consistent → SageMaker Real-time
If your team strength is: – General DevOps → Lambda – API integration → Bedrock – ML Engineering → SageMaker
These are starting points, not absolute rules. Your specific requirements around latency, cost constraints, and operational complexity will refine the choice.
The Decision Matrix
Different factors matter depending on your situation.
For foundation model applications: Bedrock simplifies everything. Use SageMaker only if you need a model Bedrock doesn’t offer or if you’re fine-tuning extensively. Lambda doesn’t compete here.
For custom small models (under 1GB): Start with Lambda if cold starts are acceptable and traffic is low to moderate. Move to SageMaker Serverless if you need better cold start behavior or traffic grows. SageMaker Real-time makes sense at high sustained traffic.
For custom large models (1GB+): SageMaker is your only real option. Choose Real-time for consistent traffic, Serverless for variable traffic, Async for batch workloads.
For cost optimization: Lambda wins at low traffic. Bedrock wins at moderate traffic for foundation models. SageMaker wins at high sustained traffic with custom models.
For team skills: Lambda requires standard DevOps skills. Bedrock requires API integration skills. SageMaker requires ML engineering skills. Match the service to your team’s capabilities.
For latency requirements: SageMaker Real-time provides the most predictable low latency. Bedrock is acceptable for most applications. Lambda works if cold starts are tolerable or eliminated with Provisioned Concurrency.
Real-World Architecture Patterns
Production deployments often combine services rather than choosing just one.
A common pattern uses Bedrock for primary inference and Lambda for preprocessing. User requests hit Lambda, which cleans input, enforces business rules, calls Bedrock, and formats results. This splits concerns cleanly.
Another pattern uses SageMaker for model hosting and Lambda for orchestration. Lambda handles request routing, A/B testing logic, and fallback strategies. SageMaker provides specialized model inference. This lets you change models without redeploying orchestration logic.
Step Functions and EventBridge often serve as glue between these services in production architectures. Step Functions orchestrate complex multi-step AI workflows (preprocessing, inference, post-processing, human review). EventBridge routes inference requests based on content type or priority. These AWS-native orchestration tools complete the picture for enterprise deployments.
Some teams use Bedrock for prototyping and SageMaker for production. Build your application using Bedrock’s managed models. Once requirements stabilize and volume justifies it, move to SageMaker with a fine-tuned or custom model. The API surface can stay similar with a facade layer.
Edge cases exist. If you need specific hardware (like Inf2 instances for models optimized for AWS Inferentia), SageMaker is required. If you need model deployment across regions with complex routing, SageMaker’s multi-model endpoints help. If you need integration with SageMaker Pipelines for MLOps, the choice is obvious.
Hybrid Approaches and Migration Paths
You don’t need to commit forever to your initial choice. Start with the simplest option that meets requirements. Migrate when economics or technical needs change.
A typical evolution: prototype with Bedrock, move to Lambda when you need custom models, migrate to SageMaker Serverless when traffic grows, switch to SageMaker Real-time when traffic becomes predictable. Each step adds complexity but optimizes for current needs.
The reverse path happens too. Teams sometimes over-engineer by starting with SageMaker when Bedrock would suffice. Moving from SageMaker to Bedrock means rewriting model hosting code but can dramatically simplify operations.
Monitor your costs and performance continuously. Many teams discover they’re paying for SageMaker features they don’t use, or that Lambda cold starts impact fewer users than expected. Data-driven migration decisions beat architectural purity.
Most AI deployments won’t move millions of requests per second. For typical applications (customer support chatbots, content generation, internal tools), the service choice matters less than execution quality. Pick what your team can build and maintain successfully.
Ready to deploy your AI application on AWS? At ZirconTech, we’ve helped enterprises migrate between these deployment paths while cutting infrastructure costs by 40-60%. Whether you’re deploying foundation models with Bedrock, custom models on SageMaker, or lightweight inference with Lambda, we can help you make the right architectural decisions. Get in touch to discuss your specific requirements.