Who Is Pika For?

TL;DR

Pika is for teams building production AI applications on AWS. While it provides enterprise-grade capabilities (security, multi-tenancy, compliance), it's designed to be approachable whether you're at a startup or a large organization. If you want to ship AI features fast without building infrastructure, and you're comfortable with AWS, Pika is for you.

The Short Answer

Pika is for you if:

You're building on AWS (or willing to)
You want AI features in production, not just demos
You prefer shipping features over building infrastructure
You need more than a prototype

Pika might not be for you if:

You need to run on other clouds (Azure, GCP)
You're building a highly specialized agent framework
You prefer controlling every infrastructure detail
You're only experimenting (overkill for pure exploration)

For Product Teams

Your Challenge

You need AI capabilities that users actually want to use:

Features that work reliably, not just in demos
Professional UX that doesn't feel like a science project
Fast iteration based on user feedback
Confidence it will scale when adoption grows

But you're competing with:

Long infrastructure buildouts
Engineering capacity constraints
"Works on my machine" demos that aren't production-ready
Uncertainty about whether users will adopt

How Pika Helps

Ship AI features in weeks, not months:

Production-ready UI included
Security and auth integration handled
No infrastructure building required
Start getting real user feedback immediately

Understand what's working:

AI-generated feedback on every conversation
Insights dashboard shows usage patterns
Understand where users get stuck
Data-driven improvement priorities

Iterate rapidly:

Change agent behavior via configuration
A/B test different approaches
Deploy updates in minutes
Measure impact immediately

Scale confidently:

Handles 10 users or 10,000
AWS infrastructure scales automatically
Costs scale with usage
No capacity planning

Real Scenario: E-commerce Product Team

Goal: Add AI shopping assistant to product pages

Without Pika:

Build chat UI and install and learn agent infrastructure
Integrate with product catalog
Add to product pages
Fix issues, polish
Result: Extended development timeline

With Pika:

Deploy Pika, define product agent
Embed in product pages, test
Refine based on beta feedback
Result: Launch quickly, iterate based on real usage

Outcome: Significant time savings, earlier user feedback, faster iteration

What Product Teams Love

Fast time-to-market: Ship features while competitors are still building infrastructure
Real user feedback: Understand actual usage, not assumptions
Professional quality: UI and UX that matches modern expectations
Measurable impact: Track adoption, satisfaction, and outcomes

For Engineering Teams

Your Challenge

You need to add AI capabilities without:

Derailing your roadmap
Creating maintenance nightmares
Learning entirely new infrastructure patterns
Accumulating technical debt

But you're facing:

Pressure to ship AI features fast
Limited time to become LLM experts
Existing systems to integrate with
Production reliability requirements

How Pika Helps

Deploy using familiar patterns:

// Standard CDK deployment
new PikaStack(this, 'Pika', {
  authProvider: new CustomAuthProvider(),
  vpc: existingVpc,
  // ... standard AWS CDK patterns
});

// Define agents as config
const agentConfig = {
  agentId: 'support-agent',
  toolIds: ['order-lookup', 'refund-tool']
};

No new deployment paradigms. If you know CDK, you know how to deploy Pika.

Clean separation of concerns:

your-services/
  order-service/
    src/tools/order-lookup.ts  # Your code
    infra/pika-agents.ts        # Agent config

  payment-service/
    src/tools/refund-tool.ts    # Your code
    infra/pika-agents.ts        # Agent config

  pika/ (deployed once)
    # Platform handles orchestration

Your business logic stays in your services. Pika handles the platform.

Observable and debuggable:

CloudWatch logs for everything
X-Ray tracing through requests
Detailed trace view in admin UI
Standard AWS monitoring patterns

Test like normal code:

// Unit test your tools
test('order lookup works', async () => {
  const result = await orderLookup({ orderId: '123' });
  expect(result.status).toBe('shipped');
});

// Integration test tool invocation
test('Pika can invoke tool', async () => {
  // Standard Lambda invoke testing
});

No special testing frameworks. Standard practices work.

Real Scenario: SaaS Engineering Team

Requirement: Add AI assistant to product without disrupting Q2 roadmap

Concern: "We can't spend months and years building chat infrastructure"

Solution:

Sprint 1: One engineer deploys Pika
Sprint 2: Team defines agents for top use cases
Sprint 3: Wire up existing APIs as tools
Sprint 4: Refine and launch beta

Outcome: Launched quickly with minimal engineering resources. No roadmap disruption.

What Engineering Teams Love

No new operational model: AWS services you already run
Clean architecture: Agent code doesn't pollute your services
Standard tooling: CloudWatch, X-Ray, CDK - familiar tools
Low maintenance: Managed services, no servers to patch
Production patterns: Security, scaling, monitoring included

For Teams of Any Size

Pika isn't just for enterprises. It's designed to be approachable.

Startups & Small Teams

Why Pika fits:

Fast to production: Ship features in weeks
One engineer can manage: No dedicated AI platform team needed
Scales with you: Start small, costs scale with usage
No premature optimization: AWS handles scale when you need it

Example: 3-person startup

Deploy Pika in week 1
Define agents for core features
Focus on product differentiation
Scale when users arrive (automatically)

What you avoid: Building infrastructure before product-market fit

Mid-Size Companies

Why Pika fits:

Multiple use cases: Platform supports many agents/chat apps
Team independence: Different teams deploy their own agents
Shared infrastructure: One deployment, many applications
Clear costs: Understand what drives spending

Example: 30-person company, 3 product teams

Each team defines their own agents
Shared tools library (user lookup, analytics, etc.)
Platform team manages Pika deployment
Product teams iterate independently

What you avoid: Each team building their own infrastructure

Enterprise Organizations

Why Pika fits:

Multi-tenancy built in: Support multiple orgs on one deployment
Enterprise security: SSO, RBAC, audit logs, compliance
Internal & external use cases: Same platform, different audiences
Governance: Infrastructure as Code, review processes

Example: Enterprise with multiple business units

Shared Pika deployment
Per-BU entity isolation
Internal tools for employees
Customer-facing apps for clients
Central platform team, distributed agent development

What you avoid: Proliferating point solutions, governance nightmare

For Different Use Cases

Internal Tools

Building AI assistants for employees:

Support agents for internal help desk
Technical documentation assistants
Code review helpers
Business intelligence

What Pika provides:

User type: internal-user
Role-based access (admin, power-user, etc.)
Integration with employee SSO
Full feature access (traces, debug tools)

Customer-Facing Applications

Building AI assistants for users:

Customer support chat
Product advisors
Shopping assistants
Self-service tools

What Pika provides:

User type: external-user
Entity-based isolation (multi-tenancy)
Customer auth integration
Feature controls (hide traces, limit capabilities)
Quality controls (verification, feedback)

Both (Most Companies)

Reality: You'll have both internal and external use cases.

What Pika provides:

Same platform, different configurations
Fine-grained access control
Shared tools, different agents
One deployment to manage

The Technical Requirements

Be honest about these prerequisites:

AWS Comfort

You need:

Willingness to run on AWS exclusively
Basic understanding of core services (Lambda, DynamoDB, etc.)
Comfort with CDK or CloudFormation

You don't need:

Deep AWS expertise (Pika configures services)
Solutions Architect certification
Previous serverless experience (but it helps)

LLM Understanding

You need:

Basic understanding of how LLMs work
Willingness to iterate on prompts
Understanding that agents aren't perfect

You don't need:

ML/AI expertise
Prompt engineering mastery (learn as you go)
Understanding of model internals

Development Capability

You need:

TypeScript/JavaScript for tool development
Ability to write Lambda functions
API integration experience

You don't need:

AI framework expertise
Specialized ML libraries
PhD in computer science

When Pika Is Overkill

Be realistic about when Pika isn't the right choice:

Pure Experimentation

If you're just exploring LLMs:

Try ChatGPT, Claude, or similar
Build a simple script
No need for full platform

When to graduate to Pika: When experiments become products

Highly Specialized Needs

If you need:

Custom agent architectures outside Bedrock's model
Specialized LLM deployment
Research-grade flexibility

Alternative: Build custom or use research frameworks

Non-AWS Environments

If you:

Must run on Azure/GCP
Have strict on-premise requirements
Can't use AWS for regulatory reasons

Alternative: Pika won't work (it's AWS-only by design)

Tiny Scope

If you need:

Single-purpose chatbot
No user management
No scaling requirements
Throwaway prototype

Alternative: Simpler solutions exist

The Decision Framework

Ask yourself:

1. Are we building for production or experimenting?

Experimenting → Simpler tools first
Production → Pika shines

2. Are we on AWS or willing to be?

Yes → Continue
No → Pika won't work

3. Do we want to build infrastructure or ship features?

Build infrastructure → Custom might be better
Ship features → Pika accelerates

4. Will we have multiple agents/use cases?

One simple chatbot → Probably overkill
Multiple agents → Platform approach pays off

5. Do we need enterprise features (auth, multi-tenancy, security)?

Yes → Pika provides these
No → Simpler options exist, but Pika still works

The Honest Assessment

Pika is a great fit if 3+ of these are true:

✅ Building on AWS
✅ Need production-quality (not demo)
✅ Multiple use cases or agents
✅ Enterprise security/auth requirements
✅ Want to ship fast
✅ Prefer structure over total flexibility

Pika might not fit if 2+ of these are true:

❌ Must run on other clouds
❌ Pure research/experimentation
❌ Single simple chatbot
❌ Want to build everything custom
❌ Not comfortable with AWS

Getting Started

Still here? Pika might be right for you.

Next steps:

Try It Out

Deploy Pika and build your first agent in 15 minutes

Quick Start Guide

Understand the Approach

Learn how Pika solves the production problem

The Pika Approach

See What's Included

Explore capabilities and features

Coming soon

Still unsure? That's okay. Pika isn't for everyone, and that's by design. We built for teams who want to ship production AI on AWS fast. If that's you, we'd love to have you try it.

If you're looking for something different, we hope this doc saved you time figuring that out.