無料ツール
システムの計画、インフラのサイジング、SLAの作成時に出てくる疑問のための実用的な計算ツールです。登録もメールアドレスの入力も不要で、計算式もすべて公開しているのでご自身で検証できます。
Why we publish these
Most of the questions that decide whether a software project goes well are asked before anyone writes code. How much will this cost? How long will it take? What availability can we actually commit to? Is automating our regression suite worth it? These are arithmetic problems dressed up as judgement calls, and the arithmetic is usually simple once someone shows you which numbers matter.
The problem is that the answers are normally locked behind a sales conversation. We would rather publish them. A team that can size its own problem before talking to anyone has a better conversation when it does — and if it turns out not to need us, that is a fair outcome too.
What we think matters when planning software
The tools encode a handful of opinions we have formed from delivering this work. They are worth stating plainly, because they are also the reason our numbers sometimes differ from other calculators.
A range is more honest than a number
Any estimate produced from a paragraph of description carries real uncertainty, and a narrow range on vague input is a lie that gets discovered halfway through delivery. Our estimators widen the range when the input is thin, and tell you which questions would narrow it. A tool that answers a one-line brief with a precise figure is selling confidence, not an estimate.
Elapsed weeks, not coding hours
The most common reason projects overrun is that they were estimated in idealised development time. Code review, testing, deployment, meetings and rework are not overhead on the work — they are the work. Every effort figure here is elapsed calendar time for a real team, which is why our numbers often look higher than an internal guess.
QA, DevOps and project management are not optional line items
Estimates that quietly omit them are the ones that need a difficult conversation in month three. If a plan has no testing effort, no deployment effort and nobody coordinating, it is not a cheaper plan — it is the same plan with three costs deferred to a worse moment.
Recovery time beats failure frequency
On the infrastructure side, teams tend to optimise for having fewer incidents. Availability budgets punish long incidents far more than frequent ones: a single slow recovery can consume an entire year's downtime allowance while a dozen fast ones cost nothing. Notice faster, recover faster, and the percentage takes care of itself.
How to use a result you get here
Treat the output as a starting position for a conversation, not a quotation. Specifically:
-
Take the range seriously, including the top of it. The upper bound is not padding — it is what happens when the risks the tool listed actually occur.
-
Read the assumptions before the numbers. Most disagreements about an estimate turn out to be disagreements about scope that the assumptions list already exposed.
-
If the result surprises you, the useful question is which input is wrong rather than whether the tool is. Both answers are informative, and the arithmetic is on the page either way.
None of these tools can see your codebase, your team, your existing contracts or your deadline. They are calibrated on ordinary delivery work, and unusual constraints move the answer more than any input you can type into a form.
Who these are for
Founders and product owners sizing a build before committing budget. Engineering leads who need a defensible number for a planning conversation. Operations teams working out what availability they can honestly promise. Developers who want the reference table without the surrounding article.
They are free and ungated deliberately. There is no email form between you and the result, nothing is stored from what you type, and no follow-up is triggered by using one.
Common questions
Are these tools really free?
Yes. There is no sign-up, no email gate and no usage limit for ordinary use. Nothing you type is stored, and using a tool does not trigger any follow-up.
How accurate are the estimates?
The deterministic calculators are exact — they are arithmetic, and the formula is shown on the page. The AI-assisted estimators are as accurate as the description you give them, which is why they return a range and state their confidence rather than a single figure.
Where do the cost figures come from?
From Ryware's own published rate card, not from market averages. The AI reads your description and breaks it into effort by role; the money is then calculated in code from published rates. The arithmetic is fully repeatable — the AI's reading of your description can vary a little between runs, which is part of why you get a range rather than a figure.
Related reading
Longer pieces on the same decisions, from our engineering blog.
Data Engineering Pipeline Architecture: A 2026 Guide
Design reliable data engineering pipeline architecture with clear trade-offs between batch, streaming, and hybrid patterns.
Legacy Software Modernization: A 2026 Practitioner's Guide
A practitioner's guide to legacy software modernization, covering strategy, data pipelines, architecture, and delivery patterns that work in production.
What Is ETL? a Complete Guide for Data Teams
Learn what is ETL and how extract, transform, load pipelines work. Explore batch vs streaming, ETL vs ELT, tools, and migration strategies.
Want a second opinion on the number you got?
If a result raised a question worth a conversation, we are happy to have it — including the version where the answer is that you do not need help. Ryware builds and operates software for organisations across healthcare, fintech, government and retail.