Self-Hosted ChromaDB on AWS: Building a Production-Grade Vector Database

Vector databases have become essential infrastructure for AI applications. When a client needed semantic search and RAG capabilities at scale, we built a production-grade, self-hosted ChromaDB solution on AWS that could handle millions of embeddings with high availability.

The Challenge

Our client was building an AI-powered platform that required:

• Semantic search across millions of documents
• Low-latency retrieval for RAG (Retrieval-Augmented Generation) pipelines
• High availability with automatic failover
• Cost efficiency compared to managed vector database services
• Full control over data residency and security

While managed solutions like Pinecone exist, the client needed the flexibility and cost control that comes with self-hosting. ChromaDB emerged as the ideal choice—it's open-source, Python-native, and designed for production workloads.

Architecture Overview

We designed a highly available architecture using AWS services:

Compute Layer: ECS Fargate

ChromaDB runs as containerized services on Amazon ECS with Fargate. This gives us:

• Serverless container management—no EC2 instances to maintain
• Automatic scaling based on CPU and memory utilization
• Task-level isolation for security
• Easy rolling deployments with zero downtime

Persistent Storage: EFS

ChromaDB's data persistence is handled by Amazon EFS (Elastic File System):

• Shared storage accessible by all Fargate tasks
• Automatic backups and point-in-time recovery
• Scales automatically as the vector index grows
• Multi-AZ redundancy for durability

Load Balancing: Application Load Balancer

An Application Load Balancer (ALB) distributes traffic across ChromaDB instances:

• Health checks ensure traffic only routes to healthy containers
• SSL termination with AWS Certificate Manager
• Path-based routing for API versioning

Networking: Private VPC

The entire stack runs in a private VPC with:

• Private subnets for ChromaDB (no public internet access)
• VPC endpoints for AWS service communication
• Security groups restricting access to application layer only
• NAT Gateway for outbound traffic (pulling container images)

Infrastructure as Code

We defined the entire infrastructure using Terraform, enabling:

• Reproducible deployments across environments
• Version-controlled infrastructure changes
• Easy disaster recovery—spin up the entire stack in a new region

Key Terraform modules included:

• VPC with public/private subnet configuration
• ECS cluster with Fargate capacity providers
• EFS file system with mount targets in each AZ
• ALB with target groups and health checks
• IAM roles with least-privilege permissions
• CloudWatch log groups and alarms

Scaling Strategy

Production workloads require intelligent scaling. We implemented:

Horizontal Scaling

ECS Service Auto Scaling adjusts the number of ChromaDB tasks based on:

• CPU utilization: Scale out when average CPU exceeds 70%
• Memory utilization: Scale out when memory exceeds 80%
• Request count: Scale based on ALB request metrics

Vertical Scaling

For the Fargate task definition, we optimized resource allocation:

• Started with 2 vCPU / 4GB memory per task
• Increased to 4 vCPU / 8GB for larger embedding operations
• Monitored CloudWatch metrics to right-size over time

Performance Optimizations

Several optimizations improved query performance:

EFS Performance Mode

We configured EFS with Max I/O performance mode to handle high throughput from multiple concurrent tasks. For latency-sensitive workloads, we also tested Provisioned Throughput mode.

Connection Pooling

Application-side connection pooling reduced overhead when making frequent queries to ChromaDB.

Batch Operations

Instead of inserting embeddings one at a time, we batched operations—inserting 100-500 vectors per request significantly improved throughput.

Collection Design

We designed ChromaDB collections strategically:

• Separate collections per data type (documents, images, user content)
• Metadata indexing for filtered queries
• Embedding dimensionality matched to the model (1536 for OpenAI, 768 for smaller models)

Monitoring and Observability

Production systems need comprehensive monitoring:

CloudWatch Metrics

• ECS task CPU/memory utilization
• ALB request counts, latency, and error rates
• EFS throughput and IOPS
• Custom metrics for query latency (p50, p95, p99)

CloudWatch Alarms

Automated alerts for:

• High error rates (5xx responses)
• Elevated latency (p95 > 500ms)
• Task failures or unhealthy targets
• EFS burst credit depletion

Centralized Logging

All ChromaDB container logs stream to CloudWatch Logs, with log insights queries for debugging and analysis.

Security Implementation

Security was paramount for this deployment:

Network Security

• ChromaDB runs in private subnets with no public IP
• Security groups allow only ALB traffic on the ChromaDB port
• VPC Flow Logs for network traffic analysis

Authentication

ChromaDB's built-in authentication was enabled with:

• API token authentication for all requests
• Tokens stored in AWS Secrets Manager
• Automatic token rotation via Lambda

Encryption

• EFS encryption at rest using AWS KMS
• TLS encryption in transit via ALB
• Secrets encrypted in Secrets Manager

Cost Analysis

Self-hosting delivered significant cost savings compared to managed alternatives:

Component	Monthly Cost
ECS Fargate (2 tasks, 4vCPU/8GB)	~$280
Application Load Balancer	~$25
EFS Storage (100GB)	~$30
NAT Gateway	~$45
Total	~$380/month

For the same capacity on managed vector databases, costs would be $500-1,500+/month depending on the provider and query volume.

Lessons Learned

1. EFS Latency Matters

EFS adds latency compared to local storage. For ultra-low-latency requirements, consider EBS with a single-instance deployment or caching layers.

2. Right-Size Early

Start with larger Fargate tasks than you think you need. Under-provisioning causes OOM kills during large batch operations.

3. Plan for Growth

Vector databases grow quickly. We implemented automated EFS storage monitoring and alerts at 80% capacity.

4. Test Failure Scenarios

We ran chaos engineering tests—killing tasks, simulating AZ failures—to validate our high availability design.

Results

The production deployment achieved:

• 99.9% uptime over 6 months of operation
• Sub-100ms p95 latency for similarity searches
• 5M+ vectors stored and queryable
• 60% cost reduction vs. managed alternatives
• Full data control with encryption and audit trails

When to Self-Host vs. Use Managed

Self-hosting ChromaDB makes sense when you:

• Need full control over data residency and security
• Have DevOps expertise to manage infrastructure
• Want to optimize costs at scale
• Require customization not available in managed services

Consider managed solutions if you:

• Need to move fast without infrastructure overhead
• Don't have dedicated DevOps resources
• Are still validating product-market fit

Conclusion

Building a production-grade ChromaDB deployment on AWS requires thoughtful architecture across compute, storage, networking, and security. The result is a highly available, cost-effective vector database that scales with your AI workloads.

If you're considering self-hosting a vector database for your AI applications, we'd love to help design and implement the right solution for your needs.