Compare Cloud Costs
Compare Cloud Costs

RAG AI Knowledge Base

A retrieval-augmented generation pipeline for AI chat and search — document storage, a metadata store, an API layer, embeddings, orchestration, and a managed inference endpoint.

Prices

Estimated monthly on-demand costs for this workload architecture.

Workload Costs Comparison

Prices by provider and services that enable users to run this workload. Shape the architecture based on cloud best practices using the four Architecture Priorities below — Capacity, Performance, Reliability, and Security. Components and prices recompute as you adjust each priority (e.g. higher Security adds a WAF, KMS, and threat monitoring), and you can switch region or billing period to compare like-for-like.

How each price was matched
CloseMeets the requirement but over-provisioned — the smallest SKU this provider sells above the requested size is materially larger.ApproximateFalls short on vCPU or memory. The price is real but it buys less than the architecture asks for.No comparable SKUNothing in this provider's catalog is close enough to compare fairly. Excluded from the total — the column is not a like-for-like alternative for that line.Not offeredThe provider does not sell a product in this category at all.
Unbadged prices are exact matches. Hover any badge for the specific reason. Totals cover only the components a provider actually matched — the “N of M tracked” note under a total says how many.
Workload Configuration

Architecture Priorities

Select level (High / Medium / Low) to adjust requirements
Corpus & Query Volume
Speed — instance class, storage media, caching
Redundancy — HA, replicas, backups, load balancing
Hardening — WAF, key management, threat detection
What this builds
Document Storage×100API & Orchestration ComputeEmbeddings ModelEmbedding / Metadata Store×2RAG Pipeline OrchestrationInference EndpointBackup Storage×16

Infrastructure Architecture Blueprint

Copy or download (export) Terraform and OpenTofu architecture blueprints to deploy this workload in a cloud provider of your choice. Add parameters, credentials, or steps for CLI and DevOps CI/CD pipelines.

Provider:
Engine:
1# ==============================================================================
2# ARCHITECTURE BLUEPRINT (TERRAFORM / OPENTOFU)
3# Copyright (c) 2026 Cosell Plus, LLC. All Rights Reserved.
4#
5# DISCLAIMER & TERMS OF USE:
6# This IaC blueprint is generated by CompareCloudCosts (CCC) for educational,
7# planning, and directional architectural purposes. Provided "AS IS" without
8# warranty of any kind, express or implied. Cosell Plus, LLC assumes no
9# liability for operational costs, misconfigurations, or service interruptions.
10# Always review and validate security, IAM, and compliance parameters before
11# deploying to production environment.
12# ==============================================================================
13#
14# ------------------------------------------------------------------------------
15# IDENTITY & CREDENTIAL PLACEHOLDERS (CLI & DEVOPS CI/CD PIPELINE)
16# ------------------------------------------------------------------------------
17# Steps to execute this blueprint using Terraform:
18# 1. Save this file as main.tf
19# 2. Set provider authentication credentials:
20#
21# For AWS Local CLI Execution:
22# export AWS_ACCESS_KEY_ID="<YOUR_AWS_ACCESS_KEY_ID>"
23# export AWS_SECRET_ACCESS_KEY="<YOUR_AWS_SECRET_ACCESS_KEY>"
24# export AWS_REGION="<REGION>"
25#
26# For AWS DevOps CI/CD Pipeline (GitHub Actions / GitLab CI / Azure DevOps):
27# Recommended: Use AWS OpenID Connect (OIDC) Role Assumption:
28# - role-to-assume: "arn:aws:iam::123456789012:role/GitHubActionsDeployerRole"
29# - aws-region: "<REGION>"
30#
31# 3. Initialize and apply:
32# $ terraform init
33# $ terraform plan
34# $ terraform apply
35# ------------------------------------------------------------------------------
36
37# ------------------------------------------------------------------------------
38# PROVIDER CONFIGURATION (TERRAFORM)
39# ------------------------------------------------------------------------------
40🔑 terraform {
41 required_version = ">= 1.5.0"
42 required_providers {
43 aws = {
44 source = "hashicorp/aws"
45 version = "~> 5.0"
46 }
47 }
48}
49
50🔑 provider "aws" {
51 region = var.aws_region
52
53 default_tags {
54 tags = {
55 ManagedBy = "Terraform"
56 Environment = var.environment
57 Workload = var.workload_id
58 Vendor = "Cosell Plus LLC Blueprint"
59 }
60 }
61}
62
63🔑 variable "aws_region" {
64 type = string
65 default = "us-east-1"
66 description = "Target AWS Deployment Region"
67}
68
69🔑 variable "environment" {
70 type = string
71 default = "production"
72 description = "Deployment environment (e.g. dev, staging, production)"
73}
74
75🔑 variable "workload_id" {
76 type = string
77 default = "workload-blueprint"
78 description = "Identifier tag for the workload"
79}
80
81# ------------------------------------------------------------------------------
82# WORKLOAD COMPONENT RESOURCES (RAG AI KNOWLEDGE BASE)
83# ------------------------------------------------------------------------------
84# Component: Document Storage
85🔑 resource "aws_s3_bucket" "document_storage" {
86 bucket_prefix = "ccc-document_storage-"
87 force_destroy = false
88}
89
90🔑 resource "aws_s3_bucket_server_side_encryption_configuration" "document_storage_enc" {
91 bucket = aws_s3_bucket.document_storage.id
92 rule {
93 apply_server_side_encryption_by_default {
94 sse_algorithm = "AES256"
95 }
96 }
97}
98
99
100
101# Component: API & Orchestration Compute (Serverless compute serving the retrieval/generation API)
102🔑 resource "aws_custom_🔑 resource" "api_compute" {
103 # Add required parameters for API & Orchestration Compute
104}
105
106# Component: Embeddings Model (Embeds incoming queries and ingested documents)
107🔑 resource "aws_custom_🔑 resource" "embeddings" {
108 # Add required parameters for Embeddings Model
109}
110
111# Component: Embedding / Metadata Store (Indexes embeddings and document metadata)
112🔑 resource "aws_custom_🔑 resource" "metadata_store" {
113 # Add required parameters for Embedding / Metadata Store
114}
115
116# Component: RAG Pipeline Orchestration (Serverless compute & message routing that coordinates retrieval and prompt assembly)
117🔑 resource "aws_custom_🔑 resource" "orchestration" {
118 # Add required parameters for RAG Pipeline Orchestration
119}
120
121# Component: Inference Endpoint (Generates responses from retrieved context)
122🔑 resource "aws_custom_🔑 resource" "inference" {
123 # Add required parameters for Inference Endpoint
124}
125
126# Component: Backup Storage
127🔑 resource "aws_s3_bucket" "backup_storage" {
128 bucket_prefix = "ccc-backup_storage-"
129 force_destroy = false
130}
131
132🔑 resource "aws_s3_bucket_server_side_encryption_configuration" "backup_storage_enc" {
133 bucket = aws_s3_bucket.backup_storage.id
134 rule {
135 apply_server_side_encryption_by_default {
136 sse_algorithm = "AES256"
137 }
138 }
139}
140
141
142
143
144# ------------------------------------------------------------------------------
145# OUTPUTS
146# ------------------------------------------------------------------------------
147🔑 output "blueprint_summary" {
148 value = {
149 workload_id = "rag-ai-knowledge-base"
150 🔑 provider = "aws"
151 region = "us-east-1"
152 components = 7
153 priorities = {
154 capacity = "medium"
155 performance = "medium"
156 reliability = "medium"
157 security = "medium"
158 }
159 }
160}
161

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Disclaimer: These price results are conceptual, for comparison only. The algorithm auto-selects the cheapest matching infrastructure that meets the memory and compute minimums from your scale parameters, without accounting for licensing, egress fees, custom integrations, or platform limitations. Check official provider documentation for real-world sizing. Visit our Terms of Use for data coverage details.