https://open-gpt.app/ VS CloudQuell

CloudQuell's answer:

Cost tools generally hand you an analytics surface and leave the questions to you. CloudQuell is built the other way round — there are three escalating ways to get an answer, and you start at the top. The dashboards arrive curated, so the views most teams need are already built. When you need something they don't cover, the report builder turns any cost question into a report. And on paid plans a hosted MCP server lets an AI agent answer questions directly against your cost data, in plain language.

The second difference is what's in the ledger. AWS, OpenAI, Anthropic and Snowflake spend all land in the same place, allocated by the same cost centers and rules, with the same budgets and anomaly alerts. AI cost sits next to the infrastructure it runs on rather than in a separate tool.

The third is commercial. Flat monthly pricing rather than a percentage of your cloud spend, published on the site, buyable by card. Connection is a scoped read-only cross-account IAM role — no write permissions, nothing to install.

CloudQuell's answer:

You can actually find the answer. Curated dashboards, a report builder for anything they don't cover, and an MCP server on paid plans so an agent can answer cost questions directly. Working out why a bill moved should take a minute, not an afternoon of configuration.

The pricing is aligned. Percentage-of-spend pricing means your tool only gets cheaper when you succeed at the thing it's meant to help with. A flat fee — $99 or $199 a month by spend band, 17% off annually — removes that.

You can just buy it. Published pricing, self-serve by card, free under $10K/month of tracked spend, 14-day trial with no card.

AI and data spend are included. If OpenAI, Anthropic or Snowflake costs are growing faster than your EC2 bill, they're in the same view.

CloudQuell's answer:

Platform and infrastructure leads who own the cloud bill, plus data-platform and AI/ML-platform leads at teams where Snowflake credits or LLM API spend have become their own line item.

Typically 20–250 engineers, 50–500 employees, and roughly $20K–$200K a month of combined AWS, OpenAI, Anthropic and Snowflake spend.

The common thread is a small team that owns cost without a dedicated FinOps function — someone who needs defensible allocation and anomaly alerts without running a programme to get them.

CloudQuell's answer:

I spent twenty years as a data architect inside Fortune 500 companies, and the same problem followed me everywhere: nobody could tell you what the cloud was costing until the invoice arrived.

The failure mode was always identical. One misconfigured system, one runaway job, one environment somebody forgot to shut down — thousands of dollars a month, quietly, with nobody watching. It surfaced when someone opened next month's bill and started asking questions. By then the money was spent and the trail was cold.

So I kept building the same things by hand. The reports that showed where spend actually went. The dashboards that made it obvious at a glance. The alerts that caught a spike while it was still small enough to matter. Different company, same build, over and over.

CloudQuell is that work turned into a product — so a team doesn't need a data architect on staff to see their own bill clearly.

CloudQuell's answer:

CloudQuell is built on AWS with a serverless backend — TypeScript on Node.js, with the heavier ingestion paths written in Rust — and PostgreSQL for application data. The front end is React and TypeScript. Infrastructure is defined in Terraform, authentication is Amazon Cognito, billing is Stripe. The product also ships a hosted MCP server so AI agents can query cost data directly.