Why not Temporal, Trigger.dev, DBOS, BullMQ or Inngest?

qlane is trying to be small and narrow. It leads with three things in a single package: spend limits across users, plans and apps, an honest record of side effects with an "unknown outcome" state, and crash tests. It runs from one SQLite file with nothing else to operate. Whether that is worth a new tool is exactly what is unproven.

Saving each finished step so a crash does not repeat paid work is the baseline, not a difference. Temporal, Trigger.dev, DBOS, Restate, Inngest, Cloudflare Workflows and LangGraph checkpoints all describe durable steps or checkpoints, so qlane does not claim to be first or unique at them. The parts that the author did not find bundled elsewhere are spend caps inside the job runner (with partial results kept), a first-class "unknown outcome" state that a person resolves, and a crash-test harness with a fake model provider. That is still the author's reading: the search was shallow and an absence is not proof.

Each product below carries a "last checked" date and a source link. 2026-10-10 is the date of the review these notes come from, and a row says so only where that review read the page. Rows with no source say unverified. Every row is to be re-checked before launch, because this market moves.

Last checked 2026-10-10. Sources: its OpenAI Agents SDK integration post and its activities page. Reading of those pages, not run side by side; unverified.

Does well

A well-known tool for durable workflows. Its own pages describe resuming where work left off, including for AI agents, and advise making activities idempotent. The author has not run it side by side with qlane, so nothing more is claimed here.

Where qlane differs

This is about qlane's own design, not a claim about Temporal. qlane saves each step's result and reuses it, and deliberately does not rely on replaying your code. It also puts AI spend caps, provider fallback and the side-effect ledger in the box, and aims at a much smaller thing to run. The price of that is a much smaller scope and, today, no code at all.

Last checked 2026-10-10. Source: its pricing page, which says tasks survive crashes through durable execution. Unverified reading.

Does well

It is open source and self-hostable, with retries, queues and checkpointing, which already covers a lot of the same ground.

Where qlane differs

Qlane's bet is the AI-cost layer: caps by user, plan, app and queue, a scheduler that counts tokens, and the unknown-outcome record for side effects, plus a crash-test harness. If Trigger.dev already handles your case, qlane may add little.

Last checked 2026-10-10. Sources: its documentation home and its queue tutorial, which describe Postgres-backed durable workflows and queues partitioned by a key such as a user id. Unverified reading.

Does well

DBOS is an open-source library that stores state in Postgres, which is a good fit if you already run Postgres and want durable workflows in your own code.

Where qlane differs

Qlane's first storage is a single SQLite file and it is a queue with workers, not only a library over your database. It also adds the spend-cap and side-effect parts. If you want your workflow state in Postgres, DBOS is the more natural choice.

Last checked: not checked against a source for this page; unverified. To be checked before launch.

Does well

It is Redis-based and has parent-child flows.

Where qlane differs

Qlane would add saved steps, spend caps and the effect ledger on top of the queue idea, and does not need Redis. Release 1 has an importer for BullMQ queue settings and jobs, but not for flows; flows are listed as unsupported there. qlane's own parent and child tickets use a different, smaller model (wait for all children, or, in milestone M3b, wait for any).

Last checked 2026-10-10. Source: its pricing page, which lists no token or cost limits (an absence, unverified). Unverified reading.

Does well

A tool in the same area. The author knows it least, so nothing specific is claimed here.

Where qlane differs

Mostly in focus (AI spend caps and the side-effect ledger) and in being a small, self-contained thing you can run from one file. This is the part of the comparison where the author knows least, so the claim is kept small.

Last checked 2026-10-10. Sources: Restate durable steps, Cloudflare Workflows limits and LangGraph durable execution. Unverified readings.

Each describes durable steps or checkpoints. Restate's page does not discuss exactly-once effects. Cloudflare publishes concrete limits (step result size, state size, retention), which is why qlane plans a limits and defaults page of its own. The author has not run any of them.

Last checked 2026-10-10. Sources: LiteLLM, Portkey and Helicone. Unverified readings.

These sit in a proxy and describe budgets per key, user or team. qlane is not a gateway and does not proxy calls. Its cap is cooperative and applies per ticket or step with partial results kept, not per API key, so it is meant to work alongside a gateway rather than replace one.

A plain list, so you can tell early whether you need something else alongside qlane. For each item, use your own tool or a third-party tool; this page does not recommend one.

• Guardrails and defense against jailbreaks or prompt injection. qlane does not inspect or filter prompts or model output.
• Access control for your tools (RBAC, OAuth). qlane does not decide who may use a tool. Whatever credentials a tool needs are passed through to it by your own code. qlane's own keys and roles are about access to qlane only.
• Retrieval (RAG, vector search).
• Evals and prompt testing. Replaying saved tickets against a new prompt or model is a parked idea, not designed.
• A chat UI.
• A model gateway or proxy. qlane does not sit in the path of your provider calls (see the gateways note above).

The honest edge. qlane limits what its own tickets spend and records what its own effects did. It does not stop a model from being tricked into asking for a tool call. An approval pause, on a step you mark for it, is the only brake qlane offers.

• You need a battle-tested system today. qlane has no code.
• You need to scale across many machines. qlane's first version is single node.
• Your jobs are cheap to repeat and safe to run twice. You probably do not need any of this.