Shaped vs AWS Personalize — The Faster Alternative

You’ve hit the AWS Personalize ceiling

AWS Personalize was great for getting started. But now you’re spending more time managing ETL pipelines than improving recommendations. Sound familiar?

2-4 week iteration cycles

Every ranking change requires updating Glue jobs, re-exporting CSVs to S3, creating filter ARNs, and waiting for retraining. Your team ships slower than your competitors.

Data pipeline complexity

Your “recommendation system” is really 6 different AWS services duct-taped together. Every schema change breaks in a new way.

Black box recipes

When recommendations underperform, you can’t see why. No visibility into feature weights, embeddings, or model internals. Just guesswork and hope.

Optimizing for clicks, not revenue

You need to optimize for conversion × margin, but AWS makes you retrain entirely different recipes with different schemas. It’s a multi-week project, not a query change.

Side-by-side comparison

Here’s what actually changes when you migrate from AWS Personalize to Shaped

Capability	AWS Personalize	Shaped
Time to implement ranking change	2-4 weeks (ETL + retrain)	Same day (query update)
Data transformation	External (Glue, Lambda, dbt)	Built-in SQL views
New item indexing time	15-60+ minutes	<60 seconds
Model transparency	✕ Black box recipes	✓ Inspect embeddings + weights
Multi-objective optimization	— Requires retraining	✓ Query-time expressions
Blend behavioral + semantic retrieval	✕ Single recipe only	✓ Multi-source in one query
Version control ranking logic	✕ Spread across AWS services	✓ Single YAML file (GitOps)
Explanation/debugging API	✕ None	✓ Per-result explanations
Query latency (P95)	<100ms	<50ms
Team required	Data eng. + ML eng.	Anyone who knows SQL

The same task in both systems

Here’s what it takes to implement: “Show recommendations prioritizing high-margin items in stock”

AWS Personalize 2-3 weeks 8 steps across 3 teams

AWS Personalize 2-3 weeks to production

Week 1: Data Pipeline

1

Update Glue ETL job
Join products + inventory + margins tables. Update transformation logic. Test locally.

2

Export to S3
Schedule Glue job → wait for completion → CSV export → upload to S3 bucket.

3

Update Personalize dataset schema
Add margin field to Items dataset schema definition.

4

Import new data
Trigger import job. Wait 15-60 minutes for data processing and validation.

Week 2: Model & Filters

5

Create filter ARN
Define filter: INCLUDE WHERE stock > 0. Wait for filter to provision.

6

Retrain solution
Trigger retraining with new data. Wait 2-8 hours (often scheduled overnight).

Week 3: Deploy & Test

7

Update campaign
Point campaign to new solution version. Update production configs.

8

Update application code
Add post-processing logic to sort results by margin. Deploy app changes.

ζ 10X FASTER Shaped Same day 3 steps, 1 engineer

Shaped Same day to production ζ 10X FASTER

9:00 AM: Product makes request

1

Update SQL view (2 minutes)
Add margin field to existing view definition.

CREATE OR REPLACE VIEW products AS
SELECT p.*, m.margin
FROM products_raw p
LEFT JOIN margins m USING(product_id)

10:00 AM: Update query logic

2

Modify ranking query (5 minutes)
Update ORDER BY expression to include margin weighting.

WHERE stock_level > 0
ORDER BY score(
  expression='ctr * margin'
)

11:00 AM: Deploy to production

3

Push via CLI (1 minute)
Deploy updated query configuration to production.

shaped deploy

✓ A/B test live by lunch Product team gets results same afternoon instead of waiting 3 weeks.

3 weeks → 3 hours

The same ranking change. The difference is AWS makes you wait for Glue jobs, S3 exports, schema updates, retraining, and filter provisioning.

Shaped lets you query your data directly.

The real cost comparison

AWS Personalize pricing looks simple until you calculate the hidden costs

AWS Personalize

Medium-sized e-commerce (12M requests/month)

AWS Service Costs

Data ingestion (200 GB/month)$10

Training (8 retrains × 15 hours)$29

Inference (12M requests/month)$685

+ Glue ETL jobs~$50

+ S3 storage~$10

+ Lambda functions~$20

Hidden Engineering Costs

Engineer time (12 hrs/week × $150/hr)$7,200

Managing Glue jobs, debugging S3 imports, coordinating retraining, updating schemas

Total Monthly$8,004

59% Cheaper

Shaped

Same workload, same performance

Platform Costs

Platform minimum$500

Data layer (200 GB × $2/GB)$400

Intelligence layer (training)$60

Query layer (12M calls)$504

+ External ETL infrastructure$0

Engineering Costs

Engineer time (3 hrs/week × $150/hr)$1,800

Update SQL views, monitor performance

Total Monthly$3,264

Save $4,740/monthThat’s $56,880 per year in your pocket

The AWS costs they don’t advertise

Minimum TPS Charges

AWS bills you for at least 1 request/second even during zero-traffic hours. That’s ~$388/month just for keeping campaigns active—whether anyone uses them or not.

Infrastructure Sprawl

Glue jobs ($50+), S3 storage ($10+), Lambda functions ($20+). These “small” costs add up to $80-150/month in supporting infrastructure you wouldn’t need with Shaped.

Training Hour Confusion

AWS charges “training hours” based on instance time, not clock time. A 2-hour retrain on the clock can cost 4-6 training hours. Budget estimates get blown quickly.

Engineer Time Tax

12 hours/week managing ETL pipelines, debugging S3 imports, coordinating retraining. At $150/hr, that’s $7,200/month in opportunity cost—4× your AWS bill.

Customer Story

“We needed a solution that delivered the best user experience. After evaluating the RecSys landscape, Shaped was the clear choice.”

Han YuanCTO, Outdoorsy

Company Outdoorsy RV Rental Marketplace

Why teams choose Shaped over AWS Personalize

Beyond velocity—here’s what else changes when you migrate

Real-time user context

AWS Personalize doesn’t know what happened in the current session. A user adds items to cart, clicks products, filters by category—none of that affects recommendations until the next day’s retrain.

With Shaped: Pass session context directly in queries. Recommendations update based on what the user just clicked, searched, or added to cart. No waiting for batch processing.

// Include session context
session_items=['item_123', 'item_456']

Bring your own models

AWS Personalize locks you into their pre-built recipes. Want to use your custom embeddings from an internal ML model? Want to blend your existing recommendation logic? Tough luck.

With Shaped: Upload your own embeddings (from BERT, your custom model, etc.). Combine Shaped’s collaborative filtering with your domain-specific models. Use Shaped as the serving layer for models you train yourself.

custom_embeddings='internal_model_v2'

One API call, not six services

AWS Personalize requires: Glue (ETL), S3 (storage), Lambda (processing), Personalize (training), Campaigns (serving), CloudWatch (monitoring). Six services to wire together and maintain.

With Shaped: One API endpoint. One dashboard. One bill. Connect your data source, write SQL queries, deploy. Your application makes one HTTP request to get ranked results.

POST /v1/rank → get results ✓

Explain why items ranked

AWS Personalize is a black box. When a weird item appears in recommendations, you can’t debug it. No feature importance, no embeddings inspection, no explanation API.

With Shaped: Built-in explanations for every result. See which features influenced the ranking. Inspect embeddings. Debug edge cases. Show users “why we recommended this” if needed.

include_explanations=true

Blend multiple signals

AWS Personalize forces you to pick ONE recipe. Collaborative filtering OR content-based. Can’t combine behavioral signals + semantic embeddings + business rules in one query.

With Shaped: Combine collaborative filtering, semantic search, trending items, and business rules in a single ranking expression. Weight them however you want. A/B test different blends same-day.

0.5*collaborative + 0.3*semantic + 0.2*trending

Git-based workflow

AWS Personalize ranking logic lives in the AWS console, scattered across Glue scripts, filter ARNs, and campaign configs. No version control, no code review, no rollback.

With Shaped: Your entire ranking configuration is a YAML file in git. Code review ranking changes like you review code. Roll back bad changes instantly. See full history of what changed and when.

git commit -m "Increase margin weight"

Architecture

The architecture difference

Fewer moving parts. Faster iteration. Less to break.

Click the Shaped flow to explore

AWS

3-10 hrs

Shaped

<1 min

AWS Personalize

Your Data

~30 min

AWS Glue

~5 min

S3 Bucket

~10 min

Lambda

~2-8 hrs

Personalize

~15 min

Campaign

Your App

7 services · 3-10 hrs

Shaped

Your Data

Shaped Data · Intelligence · Serving

Your App

1 service · <1 min

Click boxes to explore · Click background to pause

Works for your specific use case

Whether you’re running an e-commerce site, marketplace, or media platform—Shaped handles it

E-commerce: Optimize for profit, not just clicks

The challenge: AWS Personalize optimizes for clicks. But you need to balance engagement with margin. High-click items might be low-margin.

With Shaped: Optimize for click_rate × margin_dollars at query time. Test different optimization strategies (engagement vs. profit) same-day via A/B tests.

Result: One marketplace increased revenue per user by 8.7% by prioritizing high-margin items—without decreasing engagement.

Default: Click-through RateAWS

1Wireless Headphoneshigh margin$42

2Phone Caselow margin$3

3Premium Speakerhigh margin$67

4USB Cablelow margin$1

Marketplaces: Handle unique inventory at scale

The challenge: One-of-a-kind items with no click history can’t be recommended by collaborative filtering alone.

With Shaped: Shaped blends semantic and behavioral signals so new listings surface immediately based on content similarity.

Result: Marketplaces see 3-5x improvement in new listing engagement within the first 24 hours.

New listing added
Vintage Leather Bag — just now

Indexing & matching similar users...0%

Semantic + behavioral signals processing...

Similar stylePrice range matchCategory interest

Media: Real-time content freshness

The challenge: News content goes stale in hours. AWS Personalize retrains overnight—by then the story is old.

With Shaped: Shaped indexes new content in under 60 seconds and re-ranks in real time with freshness decay built in.

Result: Media platforms increase time-on-site by 15-20% with real-time personalization.

Real-time freshness rankingUpdated 0s ago

Breaking: Market Update2m ago

95%

Tech Earnings Report15m ago

72%

Weekend Sports Recap3h ago

34%

B2B SaaS: Surface the right content

The challenge: Knowledge bases, help docs, and internal tools need personalized ranking across sparse user signals.

With Shaped: Shaped combines semantic search with user behavior patterns, even with limited interaction data.

Result: B2B platforms reduce support tickets by 25% with smarter content surfacing.

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Zero-risk parallel evaluation

You don’t rip and replace. You run Shaped alongside AWS Personalize, measure real performance with your users, and make a data-driven decision.

No User Impact

Setup

Connect Shaped to your existing data sources. Configure a baseline model matching your AWS recipe. Zero code changes to your app.

Week 1

Traffic Split

AWS

100%

Shaped

0%

Traffic Split

AWS

100%

Shaped

0%

Week 2

Validation

Shadow Mode

Shaped queries run in the background—no user sees Shaped results yet. Validate data quality, model performance, and query latency.

Real Data

A/B Test

Measure click-through rate, conversion, revenue per user, and iteration velocity. Real users, real data.

Week 3–4

Traffic Split

AWS

50%

Shaped

50%

Traffic Split

AWS

0%

Shaped

100%

Decision

Data-Driven

Your Call

If Shaped wins: gradual rollout to 100%. If AWS wins: stay with AWS. Either way, you have real data—not guesswork.

Every risk, mitigated

Teams worry about these things when migrating. Here’s how we address each one.

“What if Shaped goes down?”

99.95% uptime SLA. Our infrastructure is multi-region, auto-scaling, and monitored 24/7. During your evaluation, keep AWS warm as an instant fallback.

✓ Automatic failover to AWS during migration period

“What if performance is worse?”

A/B test proves it before you commit. You’ll have 2-4 weeks of real user data comparing CTR, conversion, and revenue. No guessing.

✓ Full analytics dashboard during evaluation

“We can’t afford downtime”

Gradual rollout, not rip-and-replace. Start with 10% traffic, monitor for issues, scale up slowly. Instant rollback if anything looks wrong.

✓ Zero-downtime migration guaranteed

“Our team doesn’t have time”

We do the heavy lifting. Shaped’s solutions team helps with data connector setup, model configuration, and A/B test instrumentation. You review, we build.

✓ White-glove setup included (Standard+ plans)

“What if it costs more?”

You’ll know before migrating. During the parallel eval, you’re running both systems. Compare actual bills, not estimates. Most teams save 30-60% on TCO.

✓ Transparent usage dashboard during trial

“What about data security?”

SOC 2 Type II, GDPR, HIPAA compliant. Your data is encrypted in transit and at rest. We only access what’s needed for ranking—no PII required.

✓ Enterprise security certifications

What you get during your evaluation

$300Free CreditsCover most evaluations entirely

2 weeksSetup SupportSolutions engineer helps you configure

ZeroCommitmentNo contract, cancel anytime

Schedule Your Parallel Evaluation

Most teams complete evaluation in 2-4 weeks. Setup support included.

Common questions

What teams ask when evaluating the migration

Do I need to rip out AWS Personalize to try Shaped?

Does my team need ML expertise to use Shaped?

What if AWS Personalize is performing well for us?

How much does migration cost (engineering time)?

What about cost? Is Shaped more expensive?

Can Shaped handle our scale?

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Ready to evaluate Shaped?

Run a 2-4 week parallel comparison with your existing AWS Personalize setup. Get real performance data. Make an informed decision.

Start Parallel Evaluation Talk to Us

$300 free credits. No credit card required. 2-week setup support included.

Move from AWS Personalize to Shaped. Ship faster. Learn faster.

You’ve hit the AWS Personalize ceiling

2-4 week iteration cycles

Data pipeline complexity

Black box recipes

Optimizing for clicks, not revenue

Side-by-side comparison

The same task in both systems

Week 1: Data Pipeline

Week 2: Model & Filters

Week 3: Deploy & Test

The real cost comparison

AWS Personalize

AWS Service Costs

Hidden Engineering Costs

Shaped

Platform Costs

Engineering Costs

The AWS costs they don’t advertise

Minimum TPS Charges

Infrastructure Sprawl

Training Hour Confusion

Engineer Time Tax

Why teams choose Shaped over AWS Personalize

Real-time user context

Bring your own models

One API call, not six services

Explain why items ranked

Blend multiple signals

Git-based workflow

The architecture difference

AWS Personalize

Shaped

Databases

Data Warehouses

Event Streams

Analytics

File Storage

Works for your specific use case

E-commerce: Optimize for profit, not just clicks

Marketplaces: Handle unique inventory at scale

Media: Real-time content freshness

B2B SaaS: Surface the right content

Zero-risk parallel evaluation

Setup

Shadow Mode

A/B Test

Your Call

Every risk, mitigated

“What if Shaped goes down?”

“What if performance is worse?”

“We can’t afford downtime”

“Our team doesn’t have time”

“What if it costs more?”

“What about data security?”

What you get during your evaluation

Common questions

Ready to evaluate Shaped?

Move from AWS Personalize to Shaped.
Ship faster. Learn faster.