a0deploy error tenant config works staging fails production bug

Not specified by the original author.

TL;DR

The Worker name becomes my-worker-staging` (name plus env suffix), and each environment can carry its own routes, KV namespaces, D1 databases, and other bindings.

Steps

1. One Wrangler file, multiple deployments. Environments override top-level config per named environment.

{
 "name": "my-worker",
 "route": "dev.example.com/*",
 "vars": { "ENVIRONMENT": "dev" },
 "env": {
 "staging": {
 "vars": { "ENVIRONMENT": "staging" },
 "route": "staging.example.com/*"
 },
 "production": {
 "vars": { "ENVIRONMENT": "production" },
 "routes": ["example.com/*"]
 }
 }
}

2. Deploy with wrangler deploy --env staging. The Worker name becomes my-worker-staging (name plus env suffix), and each environment can carry its own routes, KV namespaces, D1 databases, and other bindings.
3. - vars are non-inheritable. Top-level vars do not flow into environments; every environment must define its own. The config above repeats ENVIRONMENT in each env for exactly this reason.

• Custom domains and routes need per-env values. If you deploy to a custom domain or route, each environment needs its own route/routes key. A staging deploy without one goes to workers.dev (or fails, depending on the rest of the config).
• workers_dev per environment. To give staging a *.workers.dev subdomain, set workers_dev = true inside that environment's block.
• With the Vite plugin, select the environment with CLOUDFLARE_ENV, not --env; the build flattens to a single config for that environment.

4. - Give each environment its own backing resources (separate KV namespaces, D1 databases); sharing production data with staging is how staging writes end up in prod.

• wrangler dev --env staging for local work against the staging config.
• Name environments staging and production, not dev2 and final; future you will thank present you.

When to use

You are seeing this: One Wrangler file, multiple deployments. Use this skill when you run into "Workers environments: staging and production from one config".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Verified against the live Wrangler Environments doc: --env flag, name mangling (my-worker-staging), vars non-inheritable, per-env route requirement, workers_dev per env. Ref: https://developers.cloudflare.com/workers/wrangler/environments/

Not specified by the original author.

TL;DR

Use the deploy CLI or Terraform to stop config drift; do it by hand and the tenants WILL diverge. Watch: connection enablement is per-org; the organization parameter must be passed on every login; token validation must check org_id. Cost: N tenants to configure, monitor, and keep in sync. Simplest to operate; do not add Organizations ceremony you do not need.

When to use

You are seeing this: Each customer is an Organization in a single tenant. Use this skill when you run into "One tenant per customer vs Organizations: pick the multi-tenant model".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Option A: Organizations (one tenant)

Each customer is an Organization in a single tenant. Shared connections or per-org connections, org_id in tokens, JIT membership for onboarding.

Choose when: standard B2B SaaS, customers want SSO with their IdP (enterprise connection per org), you want one codebase and one config to operate.

Watch: connection enablement is per-org; the organization parameter must be passed on every login; token validation must check org_id.

Option B: Tenant per customer

Each customer gets their own Auth0 tenant.

Choose when: regulatory isolation (data residency per customer), customers demand their own Auth0 dashboard/admin boundary, or radically different auth config per customer that Organizations cannot express.

Cost: N tenants to configure, monitor, and keep in sync. Use the deploy CLI or Terraform to stop config drift; do it by hand and the tenants WILL diverge.

Option C: Single tenant, no orgs

One shared user pool, application-level tenancy in your own data model.

Choose when: consumer app or simple B2B where customers do not need isolated login contexts or their own IdPs. Simplest to operate; do not add Organizations ceremony you do not need.

Decision inputs

• Do customers bring their own IdP? Yes -> Organizations (or tenants).
• Data residency per customer? Yes -> tenants in the right regions.
• Team size for auth ops? Small -> fewer tenants, prefer Organizations.

Checklist

• Decision written down with the reasons; it is expensive to reverse.
• If tenants: config-as-code from day one.

Auth0's Organizations docs describe the B2B organization model with per-org connections and membership. Tenant creation and regions are in the Auth0 overview docs. The deploy CLI supports multi-tenant config management. Verified against live docs 2026-09-26.

Not specified by the original author.

TL;DR

Strict is the default and is what production runs. If you need source span pointers for debugging raw combinator DSL nodes, set JULEPSOURCECAPTURE=1. Dev mode (deploy(..., mode=dev)) keeps compiling and running locally but retains the diagnostics strict prod would block on, so works in dev never means it deploys.

The error

Julep dev mode works but strict deploy fails

Fix

1. Strict is the default and is what production runs. Expected: You get the expected result; the problem is gone.

2. If you need source span pointers for debugging raw combinator DSL nodes, set JULEPSOURCECAPTURE=1. Expected: You get the expected result; the problem is gone.

3. Re-run the original operation and confirm the error is gone. Expected: no error, normal output.

When to use

• You hit this exact error.
• The symptom matches: Julep dev mode works but strict deploy fails.

When NOT to use

• A different error message; the cause here is specific to this error.
• Unrelated issues with a different cause.
• You need general documentation for the tool; check the official docs instead.

Compatibility

Reported per the linked source.

Variant phrasings

Julep dev mode works but strict deploy fails

Why it happens

My Julep flow works in dev mode but will not deploy to production. What is the difference between dev and strict mode?

Edge cases

• If your error message differs even slightly, this is probably a different issue; search the exact text.
• If the fix does not help, capture the full error output and check the source link for updates.

Source: My Julep flow works in dev mode but will not deploy to production. What is the difference between dev and strict mode?

Verified resolution: Strict is the default and is what production runs. Dev mode (deploy(..., mode=dev)) keeps compiling and running locally but retains the diagnostics strict prod would block on, so works in dev never means it deploys. Before shipping, call deployment.prodgapsummary() to see exactly what strict prod would reject, and clear that gap. Two more things that bite people: Temporal stays prod-strict, so Deployment.run(...) rejects dev-mode deployments outright, and Agent.deploy(...) uses the strict path. If you need source span pointers for debugging raw combinator DSL nodes, set JULEPSOURCECAPTURE=1.

Source URL: https://github.com/julep-ai/julep/blob/HEAD/docs-site/content/docs/guides/gotchas.md#dev-mode-vs-strict