AWS Startup Deals & Discounts

The world's most comprehensive cloud platform - compute, storage, databases, and production-scale AI

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Other Cloud & Infrastructure

Website6 deals

Deal Overview

Best Offer

$300K

Total Value

$417K

Available Deals

6

Available Deals

Subscribe to unlock deals - promo codes, redemption steps and exact eligibility are for subscribers. Create an account to get started.

Open Access300K

Up to $100,000 in credits or 20-50% off your monthly spend through an AWS partner (must be spending $100+/month)

Up to $100,000 in credits or 20-50% off your monthly spend through an AWS partner (must be spending $100+/month)

Promo code, redemption steps & eligibility

Open Access100K

25% off to 65% off your AWS expenses and up to $100K in credits

$100K in credits

Promo code, redemption steps & eligibility

Open Access5K

$5,000 in AWS Activate Credits (new users only)

$5,000 in AWS Activate Credits (new users only)

Promo code, redemption steps & eligibility

Open Access5K

Up to $5,000 in credits

Up to $5,000 in credits

Promo code, redemption steps & eligibility

Open Access5K

$5,000 in credits offered

$5,000 in credits

Promo code, redemption steps & eligibility

Open Access2K

25% to 65% off your AWS expenses and $2,000 in credits

$2,000 in credits

Promo code, redemption steps & eligibility

AWS Startup Deals - Overview

AWS runs application-based startup programs that provide cloud credits and support to help early-stage companies build without burning runway on infrastructure. Credit amounts scale with stage and how a startup connects to the program. Self-serve credits for early founders typically start around $1,000, while startups backed by a participating accelerator, incubator, or venture fund can qualify for substantially larger allocations - in some cases up to $100,000 - often bundled with elevated technical support and training.

These programs matter because cloud spend is one of the largest and most front-loaded costs for a software startup. Credits let teams prototype, load-test, and scale on production-grade compute, storage, and AI services before revenue catches up, and they buy time to find product-market fit. Allocations are generally valid for up to two years and applied automatically against eligible usage.

Eligibility is judgment-based rather than automatic: programs typically favor companies that are relatively young, have a live product or website, an active cloud account, and a clear startup profile. Larger tiers usually require an affiliation with a recognized startup-development organization. Treat these as competitive, application-based programs - approval and amounts depend on stage, profile, and how a company qualifies, not on a coupon.

About AWS

Amazon Web Services (AWS) is the world's most widely adopted cloud platform, offering more than 240 fully featured services from data centers across the globe. It gives startups and enterprises on-demand access to compute, storage, networking, databases, analytics, security, and machine learning without the cost of owning physical infrastructure.

The core building blocks remain the foundation of ...

Key Features

Elastic compute (EC2 and serverless)

Spin up virtual servers in minutes with Amazon EC2, scale automatically with Auto Scaling, or run code without managing servers using AWS Lambda. Pay only for what you use, down to the second.

Durable object and block storage

Amazon S3 provides virtually unlimited, highly durable object storage, while EBS offers block storage for EC2. Tiered storage classes automatically optimize cost as data ages.

Managed databases

Run relational workloads on Amazon RDS and Aurora, key-value and document workloads on DynamoDB, plus caching, graph, and time-series options - all fully managed, backed up, and scalable.

Amazon Bedrock for generative AI

Build AI applications and agents at production scale with a choice of foundation models, including Amazon Nova plus models from Anthropic, Meta, and others, accessed through a single managed API.

Amazon SageMaker for machine learning

Cover the full ML lifecycle - data labeling, training, tuning, and deployment - in one managed platform, with integration into Bedrock for generative AI workloads.

Purpose-built AI silicon

AWS Trainium chips deliver scalable, cost-efficient training and inference for demanding generative AI workloads, now used by leading AI labs running on AWS infrastructure.

Global infrastructure and networking

Deploy across multiple regions and availability zones with low-latency networking, content delivery via CloudFront, and isolated virtual networks through Amazon VPC.

Security, identity, and compliance

Granular access control with IAM, encryption by default, and a broad set of compliance certifications make AWS suitable for regulated and security-sensitive workloads.

Pros & Cons

Pros

Unmatched breadth and maturity - With 240+ services and the largest market share of any cloud provider, AWS can support almost any architecture, from a simple web app to large-scale AI training, without forcing you onto a second platform.
Pay-as-you-go with a real free tier - You pay only for what you consume, and a generous free tier (permanent allowances on Lambda, S3, and more, plus introductory credits for new accounts) lets you build and test at near-zero cost.
Deep generative-AI stack - Amazon Bedrock, Nova foundation models, SageMaker, and Trainium silicon give startups a credible, production-grade path to ship AI features without standing up their own model infrastructure.
Reliability and global reach - Multiple regions and availability zones, mature SLAs, and a vast ecosystem of integrations and documentation make it a low-risk default for scaling products worldwide.

Cons

Cost complexity - Pricing spans hundreds of services with many dimensions (compute, storage, data transfer, requests), and bills can climb unexpectedly without disciplined monitoring, tagging, and cost controls.
Steep learning curve - The sheer number of services and configuration options can overwhelm small teams, and getting architecture and security right often requires real expertise.
Egress and lock-in concerns - Data-transfer-out fees and reliance on proprietary managed services can make migrating away expensive, so teams should weigh portability up front.

Use Cases

Hosting web and mobile backends - Run scalable application servers, APIs, and serverless functions for products of any size, scaling up and down with demand.
Building and serving AI features - Use Amazon Bedrock to add generative AI, chat, and agentic workflows, or train and deploy custom models with SageMaker and Trainium.
Data storage and analytics - Store unlimited data in S3, run managed databases, and analyze large datasets with services like Redshift, Athena, and EMR.
Startup MVPs and scaling - Launch a minimum viable product cheaply on the free tier, then scale the same architecture to millions of users without re-platforming.
Disaster recovery and backups - Replicate workloads and back up critical data across regions for resilience and compliance.
Dev/test environments - Spin up and tear down isolated environments on demand for development, testing, and CI/CD pipelines.

Pricing

AWS uses a pay-as-you-go model with no upfront commitment for most services - you are billed for the exact compute, storage, requests, and data transfer you consume.

Free Tier - New accounts get introductory credits (up to roughly $200 in 2026, with a portion earned by trying services like Amazon Bedrock), plus 12-month free allowances (e.g. EC2 t2.micro hours) and permanently free tiers on services such as AWS Lambda (1M requests/month), Amazon S3 (limited storage and requests), IAM, and VPC.
On-Demand - Standard pay-per-use rates with no commitment; ideal for variable or unpredictable workloads.
Savings Plans and Reserved Instances - Commit to a consistent level of usage (1- or 3-year terms) for significant discounts versus on-demand, well-suited to steady production workloads.
Spot Instances - Deeply discounted spare capacity for fault-tolerant, interruptible workloads.

Generative AI services like Amazon Bedrock are typically priced per token or per request, and AgentCore and SageMaker have their own usage-based pricing. Because billing spans many services, AWS provides cost-management tools (Budgets, Cost Explorer) to monitor and control spend.

Frequently Asked Questions

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