⚡ Beam - Real-Time AI Cost Enforcement

Production-grade system for enforcing AI spending limits in real-time

Features • Quick Start • Architecture • API Reference • Contributing

🎯 The Problem

B2B SaaS companies charge customers flat monthly fees ($500/month) but AI costs are completely variable. Some customers cost $5/month, others cost $500/month in AI expenses. Without real-time enforcement, you discover which customers are unprofitable 30 days later when the bill arrives.

Beam solves this by:

⚡ Pre-flight validation: Check if customer can afford a request before it starts
🔄 Streaming enforcement: Count tokens and deduct balance in real-time during streaming
🛑 Kill switch: Immediately terminate streaming when balance hits zero
💯 Perfect reconciliation: Use provider's exact token counts for final billing

✨ Features

Core Engine

Sub-5ms balance checks via Redis Lua scripts
Atomic operations prevent race conditions at scale
Dual storage: Redis for speed, PostgreSQL for durability
Auto-reconciliation between estimated and actual costs
Multi-provider support: OpenAI, Anthropic, Google AI

Production Ready

gRPC API with Protocol Buffers for efficiency
REST API for easy integration without gRPC clients
CLI tool for manual operations and testing
Docker support with docker-compose for instant setup
TimescaleDB integration for time-series analytics
Prometheus metrics for observability
Comprehensive logging with structured JSON output

Developer Experience

Standalone: Works without SDK - direct API calls
Well documented with examples and guides
Easy setup: One command to run locally
Type-safe Protocol Buffer definitions
Tested: Unit and integration tests included

🚀 Quick Start

Prerequisites

Docker & Docker Compose
Go 1.25+ (for building from source)
Make (optional, for convenience)

1. Start Infrastructure

# Clone the repository
git clone https://github.com/kelpejol/beam
cd beam

# Start PostgreSQL and Redis
docker-compose up -d

# Wait for services to be ready (about 10 seconds)
docker-compose ps

2. Build and Run

# Build the binary
make build

# Run the server
./backend/bin/beam-api

# Or use Docker
docker-compose up -d beam-api

3. Test the API

# Using the CLI tool
./backend/bin/beam-cli balance get --customer-id test_customer_1

# Or with grpcurl
grpcurl -plaintext \
  -H "authorization: Bearer beam_test_key_1234567890" \
  -d '{"customer_id": "test_customer_1"}' \
  localhost:9090 beam.balance.v1.BalanceService/GetBalance

# Or with REST API
curl -H "Authorization: Bearer beam_test_key_1234567890" \
  http://localhost:8080/v1/balance/test_customer_1

Response:

{
  "balance": "100000000",
  "reserved": "0",
  "available": "100000000"
}

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                     YOUR APPLICATION                             │
└───────────────────────┬─────────────────────────────────────────┘
                        │
                        │ gRPC/REST: CheckBalance()
                        ↓
┌─────────────────────────────────────────────────────────────────┐
│                   BEAM BACKEND (Go)                              │
│                                                                   │
│  ┌──────────────┐    ┌─────────────┐    ┌──────────────┐       │
│  │  gRPC/REST   │───→│   Ledger    │───→│ Redis (Hot)  │       │
│  │    API       │    │  (Atomic)   │    │  <1ms ops    │       │
│  └──────────────┘    └──────┬──────┘    └──────────────┘       │
│                              │                                    │
│                              ↓                                    │
│                      ┌──────────────┐                            │
│                      │  PostgreSQL  │                            │
│                      │  (Durable)   │                            │
│                      │ +TimescaleDB │                            │
│                      └──────────────┘                            │
└─────────────────────────────────────────────────────────────────┘
                        │
                        │ Forward to provider after approval
                        ↓
┌─────────────────────────────────────────────────────────────────┐
│              OpenAI / Anthropic / Google AI                      │
└─────────────────────────────────────────────────────────────────┘

Key Components

Ledger - The heart of Beam

Atomic balance operations using Redis Lua scripts
Prevents race conditions with reservation system
Automatic reconciliation of estimates vs actuals

Storage Layer

Redis: Sub-millisecond balance checks, in-memory state
PostgreSQL: Durable storage with complete audit trail
TimescaleDB: Time-series optimizations for analytics

API Layer

gRPC: High-performance binary protocol for production
REST: HTTP/JSON for easy integration and testing
CLI: Command-line tool for operations and debugging

📊 How It Works

The Flow

CheckBalance - Pre-flight validation
- Your app calls Beam before making an AI request
- Beam checks if customer has enough balance
- If yes, reserves grains and returns approval token
- Latency: 2-4ms
Make AI Request - Your responsibility
- Your app proceeds to call OpenAI/Anthropic/etc
- Stream the response to your end user
- Count tokens as they arrive
DeductTokens - Real-time deduction (optional but recommended)
- Call Beam every ~50 tokens during streaming
- Beam deducts from balance atomically
- If balance hits zero, Beam returns success: false → kill the stream
- Latency: 1-3ms per call
FinalizeRequest - Final reconciliation
- Call once with exact token counts from provider
- Beam reconciles estimated vs actual costs
- Refunds overcharges, releases reservation
- Latency: 3-8ms

🔌 API Reference

REST API Endpoints

Get Balance - Query current balance

GET /v1/balance/:customer_id
Authorization: Bearer <api_key>

Response:
{
  "balance": "100000000",
  "reserved": "5000000",
  "available": "95000000"
}

Check Balance - Pre-flight validation

POST /v1/balance/check
Authorization: Bearer <api_key>
Content-Type: application/json

{
  "customer_id": "cus_123",
  "estimated_grains": 50000,
  "buffer_multiplier": 1.2,
  "request_id": "req_xyz",
  "metadata": {
    "model": "gpt-4",
    "max_tokens": 1000
  }
}

Response:
{
  "approved": true,
  "remaining_balance": "99950000",
  "request_token": "secure_token_xyz",
  "reserved_grains": 60000
}

Deduct Tokens - Real-time deduction

POST /v1/balance/deduct
Authorization: Bearer <api_key>
Content-Type: application/json

{
  "customer_id": "cus_123",
  "request_id": "req_xyz",
  "request_token": "secure_token_xyz",
  "tokens_consumed": 50,
  "model": "gpt-4",
  "is_completion": true
}

Response:
{
  "success": true,
  "remaining_balance": "99900000"
}

Finalize Request - Final reconciliation

POST /v1/balance/finalize
Authorization: Bearer <api_key>
Content-Type: application/json

{
  "customer_id": "cus_123",
  "request_id": "req_xyz",
  "status": "COMPLETED_SUCCESS",
  "actual_prompt_tokens": 234,
  "actual_completion_tokens": 487,
  "total_actual_cost_grains": 48700,
  "model": "gpt-4"
}

Response:
{
  "success": true,
  "refunded_grains": 11300,
  "final_balance": "99911300"
}

gRPC API

Full Protocol Buffer definitions in proto/balance/v1/balance.proto

service BalanceService {
  rpc CheckBalance(CheckBalanceRequest) returns (CheckBalanceResponse);
  rpc DeductTokens(DeductTokensRequest) returns (DeductTokensResponse);
  rpc FinalizeRequest(FinalizeRequestRequest) returns (FinalizeRequestResponse);
  rpc GetBalance(GetBalanceRequest) returns (GetBalanceResponse);
}

CLI Tool

# Check balance
beam-cli balance get --customer-id cus_123

# Add balance (credit)
beam-cli balance add --customer-id cus_123 --amount 1000000 --description "Monthly top-up"

# Deduct balance (debit)
beam-cli balance deduct --customer-id cus_123 --amount 50000

# List recent requests
beam-cli requests list --customer-id cus_123 --limit 10

# Show request details
beam-cli requests show --request-id req_xyz

# Create new customer
beam-cli customers create --customer-id cus_new --name "New Customer" --balance 10000000

# Verify balance integrity
beam-cli admin verify-integrity --customer-id cus_123

# Sync Redis from PostgreSQL
beam-cli admin sync-all

💾 Database Schema

Core Tables

customers - End customers with their balances

CREATE TABLE customers (
    customer_id VARCHAR(255) PRIMARY KEY,
    platform_user_id VARCHAR(255) NOT NULL,
    current_balance_grains BIGINT NOT NULL DEFAULT 0,
    lifetime_spent_grains BIGINT NOT NULL DEFAULT 0,
    buffer_strategy VARCHAR(20) DEFAULT 'conservative',
    created_at TIMESTAMP NOT NULL DEFAULT NOW(),
    updated_at TIMESTAMP NOT NULL DEFAULT NOW(),
    CONSTRAINT positive_balance CHECK (current_balance_grains >= 0)
);

transactions - Append-only ledger (complete audit trail)

CREATE TABLE transactions (
    transaction_id VARCHAR(255) PRIMARY KEY,
    customer_id VARCHAR(255) NOT NULL,
    amount_grains BIGINT NOT NULL,  -- Positive=credit, Negative=debit
    transaction_type VARCHAR(50) NOT NULL,
    reference_id VARCHAR(255),
    description TEXT,
    created_at TIMESTAMP NOT NULL DEFAULT NOW()
);

requests - Detailed AI request tracking

CREATE TABLE requests (
    request_id VARCHAR(255) PRIMARY KEY,
    customer_id VARCHAR(255) NOT NULL,
    model VARCHAR(100) NOT NULL,
    estimated_cost_grains BIGINT NOT NULL,
    reserved_grains BIGINT NOT NULL,
    streaming_deducted_grains BIGINT DEFAULT 0,
    actual_cost_grains BIGINT,
    status VARCHAR(50) NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT NOW(),
    completed_at TIMESTAMP
);

🔐 Security

API Authentication

Every request requires an API key:

Authorization: Bearer beam_sk_live_xxxxxxxxxxxxx

Keys are hashed with SHA-256 before storage
Stored in Redis for sub-millisecond authentication
Plaintext keys never logged or stored

Best Practices

Use different API keys for development and production
Rotate keys regularly
Enable TLS in production
Set appropriate rate limits
Monitor for unusual activity

📈 Performance

Latency Targets

CheckBalance: < 5ms (typically 2-4ms)
DeductTokens: < 3ms (typically 1-2ms)
FinalizeRequest: < 10ms (typically 3-8ms)

Throughput

10,000+ concurrent requests per server
100,000+ balance checks/second with horizontal scaling
Sub-millisecond Redis operations via Lua scripts

Scaling Strategies

Horizontal Scaling

# Run multiple instances
docker-compose up -d --scale beam-api=3

# Use load balancer (nginx, haproxy, etc)
# Configure health checks on /health endpoint

Redis Scaling

Single Redis handles 100k+ operations/second
If needed, shard by customer_id using Redis Cluster
Use Redis Sentinel for high availability

PostgreSQL Scaling

Read replicas for analytics queries
Single primary for writes (sufficient for most use cases)
Connection pooling prevents bottlenecks

🛠️ Development

Project Structure

beam/
├── backend/
│   ├── cmd/
│   │   ├── api/              # Main server (gRPC + REST)
│   │   └── cli/              # CLI tool
│   ├── internal/
│   │   ├── api/              # gRPC service implementation
│   │   ├── rest/             # REST API handlers
│   │   ├── auth/             # API key authentication
│   │   ├── ledger/           # Core balance logic
│   │   └── sync/             # Redis-PostgreSQL sync
│   ├── pkg/proto/            # Generated protobuf code
│   └── migrations/           # Database migrations
├── scripts/
│   ├── lua/                  # Redis Lua scripts
│   └── load-test.js          # k6 load testing script
├── docs/                     # Detailed documentation
├── docker-compose.yml        # Local development environment
├── Dockerfile                # Production Docker image
└── Makefile                  # Build automation

Building from Source

# Install dependencies
go mod download

# Generate protobuf code (requires protoc)
make proto

# Build all binaries
make build

# Binaries created at:
# - backend/bin/beam-api
# - backend/bin/beam-cli

Running Tests

# Unit tests
make test

# Integration tests (requires Docker)
make test-integration

# Test coverage report
make test-coverage

# Benchmark tests
make benchmark

Development Workflow

# 1. Start infrastructure
docker-compose up -d postgres redis

# 2. Run server in dev mode (with auto-reload)
make dev

# 3. In another terminal, test the API
./backend/bin/beam-cli balance get --customer-id test_customer_1

# 4. View logs
docker-compose logs -f beam-api

# 5. Clean up
make clean

🧪 Testing

Manual API Testing

Complete example workflow:

# 1. Check initial balance
curl -H "Authorization: Bearer beam_test_key_1234567890" \
  http://localhost:8080/v1/balance/test_customer_1

# 2. Pre-flight check (reserve grains)
curl -X POST -H "Authorization: Bearer beam_test_key_1234567890" \
  -H "Content-Type: application/json" \
  -d '{
    "customer_id": "test_customer_1",
    "estimated_grains": 50000,
    "buffer_multiplier": 1.2,
    "request_id": "req_test_'$(date +%s)'",
    "metadata": {"model": "gpt-4"}
  }' \
  http://localhost:8080/v1/balance/check

# Save the request_token from response, then:

# 3. Simulate streaming deductions (repeat as needed)
curl -X POST -H "Authorization: Bearer beam_test_key_1234567890" \
  -H "Content-Type: application/json" \
  -d '{
    "customer_id": "test_customer_1",
    "request_id": "req_test_'$(date +%s)'",
    "request_token": "YOUR_TOKEN_HERE",
    "tokens_consumed": 50,
    "model": "gpt-4",
    "is_completion": true
  }' \
  http://localhost:8080/v1/balance/deduct

# 4. Finalize with exact costs
curl -X POST -H "Authorization: Bearer beam_test_key_1234567890" \
  -H "Content-Type: application/json" \
  -d '{
    "customer_id": "test_customer_1",
    "request_id": "req_test_'$(date +%s)'",
    "status": "COMPLETED_SUCCESS",
    "actual_prompt_tokens": 234,
    "actual_completion_tokens": 487,
    "total_actual_cost_grains": 48700,
    "model": "gpt-4"
  }' \
  http://localhost:8080/v1/balance/finalize

# 5. Verify final balance
curl -H "Authorization: Bearer beam_test_key_1234567890" \
  http://localhost:8080/v1/balance/test_customer_1

Load Testing

# Install k6
brew install k6  # macOS
# or: https://k6.io/docs/getting-started/installation

# Run load test
k6 run scripts/load-test.js

# Custom scenario
k6 run --vus 100 --duration 30s scripts/load-test.js

📚 Documentation

Comprehensive guides available in docs/:

API Reference - Complete API documentation with examples
Architecture Deep Dive - System design and decisions
Integration Guide - How to integrate Beam into your app
Operations Guide - Production deployment and monitoring
Performance Tuning - Optimization strategies
Database Guide - Schema details and queries

🤝 Contributing

We love contributions! See CONTRIBUTING.md for guidelines.

Quick Contribution Guide

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Test thoroughly (make test)
Commit (git commit -m 'Add amazing feature')
Push (git push origin feature/amazing-feature)
Open a Pull Request

Development Standards

Follow Effective Go guidelines
Write tests for new features (maintain >80% coverage)
Update documentation for API changes
Use conventional commit messages
Add examples for new features

🐛 Known Issues & Roadmap

Known Issues

None! (Please report any issues you find)

Roadmap

v1.0 (Current)

✅ Core balance operations
✅ gRPC and REST APIs
✅ CLI tool
✅ Docker support
✅ TimescaleDB integration

v1.1 (Planned)

WebSocket API for real-time balance updates
GraphQL API
Multi-region deployment support
Advanced analytics dashboard

v2.0 (Future)

Multi-currency support
Automatic cost optimization recommendations
Machine learning for cost prediction
Stripe/payment provider integrations

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

What this means:

✅ Commercial use allowed
✅ Modification allowed
✅ Distribution allowed
✅ Private use allowed
⚠️ No warranty provided
⚠️ No liability

🙏 Acknowledgments

Built with amazing open source tools:

Go - Efficient, concurrent backend language
gRPC - High-performance RPC framework
Protocol Buffers - Type-safe serialization
Redis - Lightning-fast in-memory database
PostgreSQL - Rock-solid relational database
TimescaleDB - Time-series superpowers for PostgreSQL
zerolog - Zero-allocation structured logging

Special thanks to all contributors and users!

💬 Community & Support

GitHub Issues: Report bugs
GitHub Discussions: Ask questions
Documentation: Read the docs
Examples: See examples

⭐ Star Us!

If you find Beam useful, please consider giving it a star on GitHub! It helps others discover the project.

Made with ⚡ by developers, for developers

Documentation • Contributing • License • Changelog

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
cmd		cmd
internal		internal
migrations		migrations
proto/balance/v1		proto/balance/v1
scripts/lua		scripts/lua
.env.example		.env.example
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
changelog.md		changelog.md
ci.yml		ci.yml
docker.compose.yaml		docker.compose.yaml
dockerfile		dockerfile
go.mod		go.mod
go.sum		go.sum
handler.go		handler.go
main.go		main.go
summary.md		summary.md
test_seed.sql		test_seed.sql

License

Kelpejol/Beam

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