SaaS development in Seattle
Enterprise buyers with a large AWS commitment often prefer to buy software through AWS Marketplace, where the purchase can go on the bill they already have and may count toward spend they have committed to. For a Seattle SaaS company, listing there can shorten procurement considerably, but only if the product can handle marketplace subscriptions, entitlements and metering.
We build SaaS ready for that route as well as direct sales. Tenancy is enforced in every query, plans and entitlements are data rather than code, usage is metered once and reported to whichever billing system owns the customer, and private offers map cleanly to accounts. Alongside that comes the usual enterprise foundation of single sign-on, roles, activity logs and exports, so the listing is not the only thing a buyer checks.
Seattle's software market was shaped by Amazon and Microsoft, and by the cloud platforms they run. Many of the region's startups were founded by people who left one or the other, and they cluster in cloud infrastructure, developer tools, enterprise software and, increasingly, AI. Around them sit life sciences and global health organisations, retail and outdoor brands, and a long tail of companies whose entire estate lives in AWS or Azure. Buyers here have usually run services at scale themselves, and it shows in what they ask for.
The practical effect is more rigour and less explaining. A Seattle buyer expects a written design before code, with the alternatives considered and the reasons one was chosen. They count metrics, alarms and a runbook as part of done, not a later phase, and they want trade-offs stated plainly. Most already have a cloud agreement and committed spend on AWS or Azure, so the job is usually to make that estate tidier, cheaper and better understood. The other common pattern is the founder who left a large company and misses its internal platforms, because at a startup nobody has built them yet.
Built In puts the average base salary for a software engineer in Seattle at around $149,000, and the large employers add stock on top, which is what a startup is really competing against. A senior search can run for months, and the work that waits is often the most concrete on the roadmap: a service split out of a monolith, a migration, a cloud clean-up, a model moved from a notebook into production. Each has a clear finish line, which makes it a good fit for a small team that writes the design first and the runbook as it goes.
We work from Bengaluru with four hours of live overlap every working day, placed across your morning in Seattle. Stand-ups and design reviews happen in that window, with the engineer who writes the code. Everything decided outside it goes into your repository, your tracker and the architecture notes we write as we go, so each day starts with a written record of what changed.
We fit best on work with a clear finish line: a new service, a migration, a cloud clean-up or a model moved into production. We add senior capacity within days while a req stays open, starting with a fixed-price two-week piece. Agencies can bring us in white-label, under their own name.
Four ways this arrives.
Then SCIM, then role mapping, then a security questionnaire. We add SAML and OIDC through a provider or your own identity stack, provision users from their directory, and keep the permission model working for the self-serve customers who never asked for any of it.
Shared tables with a tenant column and every query trusted to filter on it. We enforce isolation below the application, with Postgres row-level security or separate schemas, and write tests that try to read across tenants and must fail.
Moving from seats to usage, adding a free tier, or selling annual contracts beside monthly plans. We model plans and entitlements separately from the payment provider, meter usage into a ledger you can audit, and make proration and dunning tested code rather than a monthly surprise.
Every larger customer asks who changed what, who can reach production and how access ends when someone leaves. We build exportable audit logs, least-privilege access and change control through pull requests, so your answer is a screen, not a spreadsheet.
A multi-tenant platform scoped per organisation, with admin screens for users, access and login alerts, and a way for an administrator to view the app as any user.
Read the write-up →Asked by Seattle teams.
How do you work with teams in Seattle?+
From Bengaluru, with four hours of live overlap every working day placed across your Seattle morning. Stand-ups and design reviews happen in that window, and you talk to the engineer who writes the code. We work in your Slack, your GitHub and your Linear. Designs are reviewed before code, and runbooks and architecture notes are written as we go, so the record lives where your team already works.
Do you work with teams already running on AWS or Azure?+
Yes, and in Seattle that is most teams. We work inside the estate you have: hand-built resources imported into Terraform, serverless functions made idempotent and traced end to end, cloud costs broken down per customer and feature, and AI features built on Bedrock or Azure OpenAI through the access your agreement already covers. The aim is an account that is tidier, cheaper and easier to explain, on the cloud you already pay for.
How does your rate compare to hiring in Seattle?+
Built In puts the average base salary for a software engineer in Seattle at about $149,000, before stock, benefits and recruiting fees. Our published rate is $35 an hour, with a $5,000 minimum. There is no recruiting search and no employment overhead, and we can start within days. The usual first step is a fixed-price two-week piece at $2,800, so you judge us on shipped work before committing to more.
What do the first two weeks look like?+
You share the context, and within two days you have a 30-minute call with the engineer who would do the work. Within a week you get a written plan: the options, the one we recommend and why, and how it rolls out. Then comes a fixed-price two-week piece, built in your repository from the first commit, with the design reviewed before code and the runbook written as it goes.
Shared database, separate schemas, or a database per tenant?+
Shared tables with row-level security for most products, because it is the cheapest to run and the simplest to migrate. Separate schemas or databases when a customer contractually needs isolation, data kept in a particular region, or its own backup schedule. We design so a large tenant can move to its own database later without rewriting the application, because that request usually arrives with the biggest contract.
When should we add SSO and SCIM?+
Before the deal that needs them, not in the first month. Design the user and organisation model so SSO slots in without a data migration, then switch it on when a paying customer asks. A provider such as WorkOS saves you building SAML and SCIM yourself, which rarely pays off until enterprise customers are a large share of your revenue.
Stripe, or a merchant of record like Paddle?+
Stripe gives you the most control over plans, invoices and the checkout. A merchant of record such as Paddle takes a larger fee and becomes the seller, handling tax wherever you sell. For a small team selling internationally from the start, the merchant of record is often the simpler path. We lay out both at your price point before you choose.
Can you make the product ready for enterprise buyers?+
Yes. Enterprise buyers ask for the same things every time: single sign-on, directory provisioning, roles they can define, an audit log they can export, and admin screens their IT team can use. We design those into the data model early, so saying yes to the next large customer is a configuration change rather than a quarter of rework.
What have you built that carries over to SaaS?+
Multi-tenancy, per-organisation access and admin tooling run through most of what we build. FM360 is a multi-tenant platform with all three: organisations, apps and access managed per customer, usage analytics and login alerts. Subscriptions and billing logic we build inside the product, tested like any other code rather than left to a dashboard.