As user expectations rise and product surfaces multiply, personalization systems are under more pressure than ever. But many teams still operate with rigid, monolithic architectures that make every change slow, risky, and expensive. 

Updating a ranking strategy, testing a new model, or even adding a new content source can require changes across the entire stack.

That’s why forward-looking teams are moving toward modular, composable personalization infrastructure — systems built from loosely coupled components that can be developed, deployed, and iterated upon independently. Modularity isn’t just an architectural preference. It’s how you unlock speed, flexibility, and control at scale.

We’ll break down what a composable personalization stack looks like, the core modules it includes, and how to design AI systems that evolve with your product, not against it. Whether you’re building from scratch or modernizing legacy systems, modular AI helps you move faster without rebuilding everything from the ground up.

What Is a Composable Personalization Stack?

A composable personalization stack is an architecture made up of modular components, each responsible for a distinct part of the personalization process. Instead of relying on a tightly coupled system that handles ingestion, modeling, ranking, and feedback in one unit, a composable stack breaks these functions into separate services or layers. Each module can evolve independently, be swapped out, or reused across teams and use cases.

This approach contrasts with traditional monolithic systems, where even minor changes to one part of the pipeline necessitate coordination across the entire stack. In a modular system, you can test a new re-ranking strategy, onboard a new content source, or refine your feature set without touching unrelated components.

Why it matters:

• Faster experimentation: You can deploy new models, strategies, or rules without full retrains or system redeployments.
• Greater flexibility: Mix and match components depending on the surface, audience, or objective (e.g. relevance vs. diversity).
• Simpler maintenance: Isolate bugs, monitor specific pipeline stages, and scale bottlenecks independently.
• Better collaboration: Product teams, data scientists, and engineers can own different parts of the stack without stepping on each other’s work.

Composable stacks aren’t just a technical convenience—they’re a strategic advantage for organizations that need to personalize at scale across diverse product surfaces.

The Core Modules of a Personalization Stack

Composable personalization systems typically follow a modular pattern that reflects the lifecycle of relevance: from collecting signals to delivering ranked outputs. Below are the foundational components that make up a flexible, production-ready stack.

1. Data Ingestion and Event Tracking

Everything starts with signal collection. You need to capture real-time user behavior, item metadata, and event streams to fuel downstream models.

• Examples: page views, purchases, searches, content interactions
• Requirements: real-time ingestion, schema normalization, idempotency
• Tools: Kafka, Segment, Snowflake, Airbyte, Postgres

2. Identity and User State

A strong personalization system must resolve identities across sessions, devices, and platforms while respecting privacy boundaries.

• Support for anonymous users, logged-in sessions, and merged profiles
• Use cases: warm starts, consistent ranking across devices
• Tools: internal identity graph, CDP integrations, hashed identifiers

3. Feature and Embedding Stores

Personalization depends on feature-rich user and item representations. These stores centralize access to embeddings, metadata, and context for real-time inference.

• Should support fast lookups and caching
• Common formats: user vectors, content embeddings, categorical features
• Tools: Redis, Pinecone, Weaviate, Feast, in-house vector DBs

4. Candidate Generation

This module narrows millions of possible items to a few thousand using filters, heuristics, or simple ML models.

• Surface-aware logic (e.g. “new arrivals,” “similar to X,” “popular now”)
• Input features: user embeddings, category match, recency, availability
• Should support modular plug-in strategies

5. Ranking and Re-Ranking

The ranking layer applies advanced models to score and order candidates based on predicted relevance to the user.

• Supports ML-based scoring, hybrid models, or manual rules
• May optimize for multiple objectives (watch time, CTR, diversity)
• Importance of observability and explainability at this layer

6. Feedback and Learning Loops

Capturing both explicit and implicit feedback helps refine personalization over time.

• Explicit: likes, skips, “not interested”
• Implicit: dwell time, repeat engagement, bounce rate
• This data feeds into retraining loops, fine-tuning, or strategy weighting

Each of these modules can be versioned, tested, and swapped without touching the rest of the system, enabling fast iteration while keeping personalization quality high.

Design Principles for Modular AI Infrastructure

Building a composable personalization stack requires more than just breaking things into pieces. 

The way those pieces interact—and how they’re designed—determines whether your system is truly modular or just fragmented. Below are core design principles that help ensure your infrastructure remains flexible, testable, and resilient as it scales.

1. Loose Coupling

Each module should operate independently, communicating through well-defined APIs, message queues, or data contracts. This allows teams to upgrade or replace components (e.g. a re-ranking model or embedding store) without rewriting the entire stack.

2. Clear Interfaces and Data Contracts

Every module, whether it's ingestion, candidate generation, or ranking, should define:

• Input and output formats
• Schema expectations
• Failure behavior and validation rules

Strong contracts help prevent schema drift, improve cross-team handoffs, and simplify testing.

3. Swappability and A/B Testability

Design modules so they can be versioned, A/B tested, or run in parallel. For example:

• Test a new ranking strategy without disrupting existing ones
• Evaluate candidate generation methods on different product surfaces
• Roll out embedding model updates gradually

This accelerates experimentation and reduces risk.

4. Observability by Design

Each module should emit logs, metrics, and traces that allow you to answer:

• What decisions were made, and why?
• How did inputs influence outputs?
• Where did latency or failure occur?

This level of insight is critical for debugging, tuning, and performance monitoring.

5. Separation of Concerns

Avoid blending responsibilities. For example, candidate generation should not handle ranking or user preference scoring. Keeping each module focused simplifies reasoning and makes pipelines more maintainable.

Benefits of Going Modular

Modular AI systems deliver tangible business and technical advantages. 

As teams scale and personalization needs become more dynamic, modularity becomes the key to maintaining systems' flexibility, resilience, and alignment with evolving goals.

1. Faster Innovation

With modular infrastructure, teams can ship improvements to one part of the system, like a new ranking model or feedback strategy, without waiting on full pipeline retrains or engineering-wide coordination. This leads to shorter iteration cycles and more frequent experimentation.

2. Flexibility Across Surfaces

Different surfaces (e.g., homepage, search, notifications) often require different personalization logic. Modular systems let you tailor strategies per surface without duplicating infrastructure. For example:

• Lightweight heuristics on low-engagement touchpoints
• Deep ML models on high-impact feeds

3. Better Collaboration

Modular stacks reduce cross-team dependencies. Product managers, ML engineers, and backend developers can each own specific components without stepping on each other’s work. This speeds up delivery and improves clarity around ownership.

4. Easier Debugging and Maintenance

When something breaks, you shouldn’t have to sift through the entire stack to find the root cause. With clear interfaces and observability per module, it’s easier to isolate issues and fix them without introducing regressions elsewhere.

5. Optionality for Model Strategy

Modularity gives you the freedom to blend models, try off-the-shelf tools, or bring your own (BYO) embeddings or scoring logic. You’re not locked into a single vendor, framework, or approach, which is critical for long-term agility.

Build Personalization That Evolves With Your Product

Modular, composable personalization enables teams to move faster, experiment safely, and deliver better user experiences across every touchpoint. By decoupling your stack into focused, flexible components, you reduce complexity, speed up iteration, and position your infrastructure to scale with the business, not against it.

Shaped was built for teams taking this modular approach. With plug-and-play APIs for ingestion, ranking, feedback, and measurement, Shaped makes it easy to build composable, real-time personalization systems without managing the underlying infrastructure.

Ready to personalize faster, with more control and less overhead? Start your free trial.