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.

StackSpend.app's answer:

Legacy FinOps tools were built for the AWS-only era: they stop at a chart, bill you a percentage of your cloud spend, and can't explain what changed. StackSpend is different on three fronts — it explains the cause of every spike (cost-to-code correlation), it covers AI/LLM spend as a first-class citizen alongside cloud, and it uses flat, predictable per-tier pricing so your cost-management bill never grows just because your cloud bill did. Setup takes minutes, and a 14-day free trial doubles as a free cost-health audit.

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.

StackSpend.app's answer:

StackSpend traces every dollar of cloud and AI spend back to the code, team, and pull request that caused it. Where traditional cost tools show you that spend moved, StackSpend's cost-to-code correlation shows you why — automatically tying each anomaly to the deploy, config change, or PR behind it. It unifies traditional cloud (AWS, Azure, GCP, Snowflake) and modern AI spend (OpenAI, Anthropic, Cursor) in one view, detects anomalies daily instead of at month-end, and works from day one without a data team building dashboards.

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.

StackSpend.app's answer:

Engineering and finance teams who share responsibility for cloud and AI spend — platform/DevOps engineers, engineering leaders, and FinOps or finance practitioners. It's built for teams running a mix of cloud infrastructure and AI/LLM services who need daily visibility and a shared source of truth, from fast-moving startups through mid-market and enterprise organizations.

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.

StackSpend.app's answer:

StackSpend was built by engineers who spent years watching cloud bills climb — and then watching AI make them climb faster. Founder Andrew Day spent a decade building large-scale systems in regulated banking, where every dollar of infrastructure was accounted for, then eight years in AI startups where teams spent across OpenAI, Anthropic, Cursor, and a dozen cloud services with no way to say why the bill jumped. The cause was almost always a code change — a PR that flipped a model or widened a query — but finance dashboards never connected spend to the code behind it. So StackSpend was built to close that gap and turn a monthly surprise into a daily signal.

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.

StackSpend.app's answer:

StackSpend is a TypeScript monorepo (Turborepo). The web app is built with Next.js 15, React 19, and Tailwind CSS, deployed on Vercel. The backend API is a Node.js/Express service on Railway, with Supabase (PostgreSQL) for data and auth. Cost forecasting is powered by a Python FastAPI service using Prophet, pandas, and NumPy. AI/LLM features run through a dedicated agents service (Anthropic Claude), and the platform ingests cost data via native provider APIs and the open FOCUS standard.