AWS Glue Alternatives: Simpler Ways to Sync API Data to RDS

AWS Glue can transform terabytes of data across S3 buckets, orchestrate complex ETL workflows, and handle schema evolution at scale. It's also one of the most over-engineered ways to get your Stripe customers into a Postgres table.

If you've ever spent an afternoon configuring a Glue connection to your VPC, writing a PySpark script for what should be a simple API call, or debugging a crawler that keeps inferring the wrong schema — you're not alone. "AWS Glue alternative" is one of the most searched terms in the AWS data tooling space, and the reason is simple: most developers don't need what Glue offers.

If you're a solo developer, startup founder, or small team that just needs billing data from Stripe, QuickBooks, Xero, or Paddle in your RDS PostgreSQL database — this post compares the realistic alternatives, from custom scripts to fully managed tools, so you can pick the right one for your situation.

Why Developers Look for Glue Alternatives

AWS Glue was designed for data engineering teams processing large volumes of data across AWS services — S3 to Redshift, DynamoDB to S3, that kind of thing. When your actual need is "call an API and put the JSON into a Postgres table," you run into friction at every step:

Cold start and runtime costs. Glue Spark ETL jobs default to 10 DPUs (minimum 2), at $0.44 per DPU-hour. Even the lightest Python Shell job (0.0625 DPU) still carries a 1-minute minimum per run. For a job that takes 10 seconds to fetch 500 records from Stripe, you're paying for far more infrastructure than the task requires.
PySpark for simple tasks. Glue's default runtime is Apache Spark. Writing PySpark to paginate a REST API and insert rows into RDS is like using a forklift to move a chair.
Python Shell jobs aren't much better. Glue does offer a lighter Python Shell option, but you still need to manage VPC connections, IAM roles, Secrets Manager references, and packaging any dependencies your script needs.
OAuth is your problem. If your data source uses OAuth (QuickBooks, Xero), you need to handle token storage, refresh logic, and error handling yourself. One expired token and your pipeline silently stops producing data.
Connectors don't remove the complexity. Glue now has native SaaS connectors and a REST API connector, but using them still means configuring Glue jobs, IAM roles, and VPC connections. The connector handles the HTTP call — everything else is still on you.

Glue solves a real problem — just not this one.

The Alternatives

1. Custom Lambda Function

The DIY baseline. Write a Lambda function that calls your API, transforms the response, and inserts into RDS. Trigger it on a schedule with EventBridge.

import json
import psycopg2
import urllib3

def handler(event, context):
    # Fetch from API
    http = urllib3.PoolManager()
    resp = http.request('GET', 'https://api.stripe.com/v1/customers?limit=100',
                        headers={'Authorization': 'Bearer sk_live_...'})
    customers = json.loads(resp.data)['data']

    # Insert into RDS
    conn = psycopg2.connect(host='...', dbname='...', user='...', password='...')
    cur = conn.cursor()
    for c in customers:
        cur.execute(
            "INSERT INTO stripe_customers (id, email, name) VALUES (%s, %s, %s) "
            "ON CONFLICT (id) DO UPDATE SET email=EXCLUDED.email, name=EXCLUDED.name",
            (c['id'], c.get('email'), c.get('name'))
        )
    conn.commit()
    conn.close()

Pros: Cheap to run, flexible, no Spark overhead, you control everything.

Cons: You're writing and maintaining all the code — pagination, rate limiting, error handling, connection pooling (RDS Proxy solves this but it's another service to configure and pay for), schema updates when the API changes, and OAuth token management for providers that require it. For one data type from one provider, it's manageable. For multiple providers and data types, you're building and maintaining a custom ETL system.

2. Automated Sync Tools

Purpose-built sync tools handle the entire pipeline — API calls, pagination, table creation, schema management, and scheduling — so you're up and running in minutes instead of days.

Codeless Sync falls into this category. You connect your RDS instance, authorize your billing provider (Stripe, QuickBooks, Xero, or Paddle), and it creates the destination table and syncs the data automatically. There's no infrastructure to manage, no connectors to configure, and syncs run automatically on a schedule.

Pros: Fastest setup time (minutes, not hours). Built specifically for the API-to-PostgreSQL use case. Handles table creation, schema management, OAuth token refresh, and incremental syncs. No infrastructure to host or maintain. Free tier available — no credit card required to start.

Cons: Less flexible than writing custom code — you get the data types and fields the tool supports rather than arbitrary transformations. Not suitable if you need to transform data during extraction or sync from non-supported APIs.

We wrote a step-by-step walkthrough for syncing billing data to AWS RDS that covers the full setup process if you want to see what this looks like in practice.

If you're a solo developer or small team syncing one or two billing providers, this is the fastest path from "I need this data in RDS" to actually querying it. The remaining alternatives below offer more flexibility or scale — but each requires you to build or manage something.

3. Open-Source ETL (Airbyte, Meltano)

Self-hosted tools like Airbyte and Meltano come with pre-built connectors for Stripe, QuickBooks, and other SaaS APIs. They handle pagination, rate limiting, and schema management out of the box.

Pros: Pre-built API connectors, open source, handles the hard parts of API extraction, community-maintained. Supports 600+ data sources if you need more than billing providers.

Cons: You need to host and maintain the tool itself — typically an EC2 instance or ECS cluster running the Airbyte server, scheduler, and worker containers. That's its own infrastructure to monitor, update, and scale. Airbyte's resource requirements aren't trivial either: the recommended spec is 4 CPUs and 8GB RAM for the server alone. For a team that wanted to avoid infrastructure overhead, this trades one kind for another.

4. Managed ETL (Fivetran, Stitch)

Fully managed SaaS platforms that sync data from APIs to databases. No infrastructure to maintain — they handle connectors, scheduling, and error recovery.

Pros: Truly hands-off. Reliable connectors, automatic schema handling, monitoring dashboards, alerting. The most mature option if you need dozens of data sources across an organization.

Cons: Pricing. Fivetran charges based on Monthly Active Rows (MAR) — rows that are created or updated in a billing period. They offer a Free tier (up to 500K MAR) and a Starter tier for smaller teams, but the Standard plan starts at roughly $1,200/month base — firmly enterprise territory. Stitch (now part of Qlik, via the Talend acquisition — though Qlik is directing new users toward Qlik Talend Cloud) has similar volume-based pricing. If you only need one or two data sources synced to RDS, these tools are overkill for the job.

5. Step Functions + Lambda

If you need orchestration — retries, parallel execution across providers, conditional logic between multiple data types — Step Functions can coordinate a workflow of Lambda functions.

Pros: Proper retry logic, error handling, and parallelism without custom orchestration code. Good fit if you already have Lambda functions and need to chain them together.

Cons: The most complex alternative on this list. You're maintaining state machine definitions, multiple Lambda functions, IAM roles for each, and the Step Functions execution costs on top. The individual Lambda functions still have all the same problems listed in option 1 — Step Functions just coordinate them. Only worth it if your pipeline genuinely needs orchestration.

Side-by-Side Comparison

	AWS Glue	Lambda	Codeless Sync	Airbyte (Self-Hosted)	Fivetran	Step Functions
Setup time	Hours	Hours	5 min	Hours	30 min	Hours
Code required	PySpark / Python	Python / Node	None	None (config)	None	Python / Node
Infrastructure	Managed (AWS)	Managed (AWS)	Fully managed	Self-hosted (EC2/ECS)	Fully managed	Managed (AWS)
API connectors	REST + SaaS (limited)	None (custom)	Stripe, QB, Xero, Paddle	600+ pre-built	700+ pre-built	None (custom)
OAuth handling	Manual	Manual	Built-in	Built-in	Built-in	Manual
Table creation	Manual	Manual	Auto	Auto	Auto	Manual
Incremental sync	Manual	Manual	Built-in	Built-in	Built-in	Manual
Cost (low volume)	~$15-30/mo	~$1-5/mo	Free tier available	Free (+ EC2 ~$30/mo)	Free tier / $1,200+/mo	~$5-15/mo
Best for	Large-scale ETL	Full control	API-to-PostgreSQL	Many data sources	Enterprise	Complex orchestration

When AWS Glue Actually Makes Sense

This post isn't about Glue being a bad tool — it's about using the right tool for the job. Glue is the right choice when:

You're moving data between AWS services at scale — S3 to Redshift, DynamoDB exports, cross-account data sharing. This is what Glue was designed for.
You need complex transformations — deduplication, joining multiple sources during extraction, applying business logic before loading. Spark's processing model handles this well.
Your data volumes are genuinely large — millions of records per run, where Spark's distributed processing actually provides a performance benefit over single-threaded scripts.
You already have a data engineering team — if you have dedicated engineers who know Spark and manage Glue jobs daily, the operational overhead isn't incremental.

For syncing a few thousand records from a billing API to RDS on a schedule, Glue is the wrong abstraction. You don't need distributed computing for a task that a single HTTP request and a database INSERT can handle.

Wrapping Up

The best Glue alternative depends on what you actually need. If you want full control and don't mind maintaining code, a Lambda function is the cheapest path. If you're syncing dozens of data sources across an organization, Fivetran or Airbyte earns its cost. If you need billing data in your RDS database without the overhead of building or managing a pipeline, a focused sync tool gets you there in minutes.

The common thread across all these alternatives: none of them require you to learn PySpark, configure Glue connections, or debug crawler schemas for what is fundamentally a simple data movement task.

Give it a try: codelesssync.com

Frequently Asked Questions

Is AWS Glue free?

No. Glue charges $0.44 per DPU-hour with a 1-minute minimum per job run. Spark ETL jobs default to 10 DPUs (minimum 2), while Python Shell jobs start at 0.0625 DPU. There's a small free tier for the Glue Data Catalog, but the ETL jobs themselves always cost. For a lightweight API sync that runs daily, expect $15-30/month — mostly wasted on idle compute.

Can AWS Glue connect to REST APIs?

Glue now has native SaaS connectors and a REST API connector, so it can make API calls natively. But using them still means configuring Glue jobs, IAM roles, VPC connections, and dealing with Glue's operational complexity. For SaaS APIs like Stripe, QuickBooks, or Xero, you're still handling pagination logic, authentication, and error handling within the Glue framework — the connector doesn't remove the overhead, it just handles the HTTP layer.

What's the cheapest way to sync API data to RDS PostgreSQL?

A custom Lambda function triggered by EventBridge is the cheapest option at $1-5/month for low volumes. The trade-off is development and maintenance time — you're writing and maintaining the sync code yourself. If your time is more valuable than a few dollars a month, Codeless Sync has a free tier and automates the entire pipeline — setup takes minutes.

Do I need AWS Glue for a simple ETL pipeline?

For most API-to-database syncs, no. Glue was designed for large-scale data processing across AWS services — not for calling a REST API and inserting rows into Postgres. If your pipeline is "fetch JSON from an API, put it in a table," every alternative in this post is simpler and cheaper than Glue.

Can I use Airbyte with AWS RDS?

Yes. Airbyte supports PostgreSQL as a destination, including RDS instances. The main consideration is hosting — you'll need to run the Airbyte server on an EC2 instance or ECS cluster within your VPC. If you're already running other self-hosted tools on AWS, adding Airbyte is straightforward. If not, you're taking on new infrastructure to manage.

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