MCP For Claude Marketplace
Vector Database TCO Calculator

Vector Database TCO Calculator MCP Connector for Claude

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Calculate the total cost of ownership for vector databases, comparing managed vs self-hosted models.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides specialized tools to model the financial impact of vector database deployments. It accounts for critical factors like vector storage density, indexing overhead, and replication requirements. Use calculate_three_year_tco to project long-term expenses, compare_deployment_models to decide between managed services and self-hosted infrastructure, project_scaling_costs to predict growth expenses, and estimate_resource_requirements to determine necessary hardware or cloud capacity.

vector-databasetcoinfrastructureai-infrastructurecost-modeling

4 tools expose this connector's capabilities to your AI agent.

calculate_three_year_tco

Calculates the total estimated cost for a vector database over a 3-year period

compare_deployment_models

Compares the total cost of managed services versus self-hosted infrastructure

estimate_resource_requirements

Estimates the hardware or cloud capacity required to support a workload

project_scaling_costs

Projects how costs will grow as the vector database expands

See how to talk to your AI agent using Vector Database TCO Calculator.

What is the 3-year TCO for 10 million vectors with 1536 dimensions at 50 QPS?

The estimated 3-year TCO for your workload is $45,200.00, including storage, compute, and operational costs.

Should I use a managed service or host it myself for 1 million vectors?

Based on your parameters, a managed service is the preferred model, offering a total cost saving of $1,200.00 over 3 years compared to self-hosting.

How much memory do I need for 5 million vectors at 128 dimensions?

To support this workload, you will require 64.5 GB of memory and 120 GB of storage.

The tool includes an index multiplier to account for the additional RAM and disk space required by searchable indexes like HNSW.

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