In this article, we compare GlassFlow with AWS Lambda, exploring Lambda's limitations, how GlassFlow addresses them, and the key use cases for each tool.
What is AWS Lambda?
AWS Lambda is a serverless computing service using a Function as a Service (FaaS) application model that runs your code in response to events. It's efficient for handling small, stateless tasks like responding to API calls and processing data. AWS Lambda natively supports JavaScript, Java, Python, Go, C#, F#, PowerShell, and Ruby code. AWS Lambda has a straightforward programming model. A function receives a JSON object as input and may return another JSON as output. Developers can deploy Lambda functions using AWS Console, SDK, or CI/CD. However, its limitations become visible when scaling to complex workflows requiring orchestration(Needs additional services for end-to-end data processing pipelines), stateful transformations, and continuous processing.
What is GlassFlow?
GlassFlow goes beyond being just a serverless transformation function. GlassFlow is a serverless platform designed for building real-time and event-driven applications. It offers a Python-first approach with a built-in message broker and integrated data processing layer. GlassFlow simplifies the creation and management of event-driven pipelines, making it a great fit for teams looking to build scalable, low-latency applications. Developers can deploy new pipelines using UI, Python SDK, or CLI.
Overview of Lambda vs GlassFlow - Feature by Feature
| Feature | AWS Lambda | GlassFlow |
|---|---|---|
| Scalability | Scales by instantiating independent function instances | Scales seamlessly with event-driven pipelines |
| Data Retention | Requires S3/DynamoDB; no built-in retention | Built-in retention and event replay |
| Execution Time | Max 15 minutes per function | Continuous processing without time limits |
| Latency | Cold starts introduce delays | Optimized for real-time with no cold starts. Always ready to serve new events. |
| Connectors | Integrates well with other AWS services. Limited non-AWS connectors via EventBridge | Wide range of built-in connectors: PostgreSQL, Snowflake, ClickHouse, and etc. |
| BYOC | Limited to AWS ecosystem | Native support for BYOC across multiple cloud providers |
| Multifunction | Requires Step Functions for workflows | Multiple functions within one pipeline |
| Stateful Processing | Stateless; state requires DynamoDB | Stateless, but it is easy to add state into transformations without external services |
| Logs | AWS CloudWatch | Integrated logging and monitoring |
What Use Cases is AWS Lambda Good For?
- You’re building simple, stateless applications with singular tasks.
- You build simple workflows within the AWS ecosystem.
- Cost savings for occasional, lightweight tasks are your primary goal. (e.g., S3 uploads, API calls)
Example Use Case
You build a photo-uploading application. AWS Lambda processes images uploaded to an S3 bucket. When a photo is uploaded, Lambda triggers, processes the image, and stores it back in the bucket. For this singular, stateless task, Lambda works well, saving costs by provisioning resources only when needed.
Limitations of AWS Lambda
- Cold Start Delays: The first time a Lambda function is invoked, AWS needs to initialize a container and load the function code, which can lead to latency problems, especially for functions that aren't triggered often.
- Concurrent Execution: AWS Lambda has a limit on the number of concurrent executions that can be running at the same time. This can be a limitation for workloads that experience sudden spikes in traffic. See Lambda concurrency quotas at the account or function level.
- Execution Limits: While most functions typically execute in seconds (or less), certain resource-intensive tasks, such as machine learning or data analysis, can take significantly longer, sometimes minutes or even hours. For AWS max timeout time is 15 minutes which is not ideal for complex data transformations.
- Stateful Tasks: Lambda is stateless by design. Each function execution is independent and doesn’t retain context or state. For scenarios where stateful processing is required, AWS Lambda can be configured with AWS Lambda Layers and other AWS services like Amazon EFS or DynamoDB to retain state information between function executions.
- Complex Pipelines: Serverless functions are nanoservices: small blocks of code doing just one thing. The question is how we orchestrate multiple workflows where the output of one stage needs to be input to another lambda. In AWS, you can only achieve with AWS Step Functions for workflow orchestration.
- Vendor Lock-in: AWS Lambda operates strictly within the AWS ecosystem, which means running your Lambda functions in another cloud provider or on-premises environment is not an option. If you want to offer BYOC options for your clients, you'd need to redesign your architecture using containers or other tools like AWS Outposts, which can be costly and complex.
What Use Cases is GlassFlow Good For?
- You need to build real-time app with multiple event-driven pipelines.
- You need multifunction workflows in a single pipeline.
- You’re working with Python and prefer a streamlined development process with minimal DevOps requirements.
- You are building a startup app where you want to start with low costs and scale as a startup grows.
- You are migrating your existing batch processing pipelines to event-driven.
- For businesses requiring BYOC, GlassFlow offers a clear advantage by providing native support for deployment across diverse environments.
Example Use Case:
For an AI app, GlassFlow detects anomalies in data in the data pipeline before the data reaches the Large Language Model (LLM). You can catch missing fields in customer records or duplicated data entries making sure that only clean data reaches the model. This avoids misleading the LLM during training or usage. This involves capturing new data changes, multiple transformations, and real-time analysis that GlassFlow can handle in a single pipeline.
Limitations of GlassFlow
- Python-only support.
- Abstracted message broker, limiting advanced configurations.
- Best suited for fast-moving teams building event-driven apps.
Conclusion
AWS Lambda is a great choice for lightweight, single-task functions within the AWS ecosystem. GlassFlow marks the new era of real-time data pipelines. It simplifies event-driven application development with built-in connectors, stateful processing, and a user-friendly interface, offering high throughput and low latency without the complexity of Lambda’s fragmented toolset.
If you want industry-grade performance, low operational overhead, and modern event-driven pipelines, get started with GlassFlow.
