How Midjourney started looping prompts with “Job timed out (504 Gateway Timeout)” and the retry interval tuning that finally stabilized large queue generations

On its rise to becoming one of the most beloved AI art generators, Midjourney has undergone immense innovation and scaling. However, this technological growth came with growing pains—particularly when handling large volumes of prompt generations. A curious and frustrating issue began to emerge for users and developers alike: prompts were looping endlessly with the dreaded message, “Job timed out (504 Gateway Timeout).” This article dives into how Midjourney confronted and resolved this problem, from uncovering the root cause to implementing smart retry intervals that brought stability back to the platform.

TL;DR

Midjourney faced a serious issue where queued prompts began looping due to server timeouts, displaying a “Job timed out (504 Gateway Timeout)” error. The problem was traced to high server load and poorly tuned retry mechanisms. Engineers implemented adaptive retry intervals based on queue size and job complexity, breaking the timeout loop. Thanks to this, prompt generation is now more stable even during peak usage.

The Emergence of the 504 Gateway Timeout Loop

Midjourney operates with powerful AI models that require substantial backend processing power. With the exponential growth of the platform’s user base, particularly after the release of each new model version, an underlying scalability issue began to surface. Users noticed that some of their prompts weren’t just delayed—they were never completed. Instead, these prompts would eventually return with a glaring error:

“Job timed out (504 Gateway Timeout)”

This wasn’t just a one-time failure. Many users reported the same prompt being re-queued and retried automatically by Midjourney’s backend, only to fail again with the same error in a repeating cycle. This created the illusion of the system attempting to be resilient but ultimately falling into an endless loop.

Root Cause Analysis: More Than Just Server Overload

At first glance, it seemed like Midjourney’s servers were simply overwhelmed. And while that was partly true, further investigation by the engineering team revealed a more nuanced cause. Here’s what was uncovered:

• Retry Loops Weren’t Smart: Midjourney’s prompt retry logic was based on fixed intervals and didn’t account for queue size or current system load.
• Queue Feedback Was Missing: The backend lacked a feedback loop to assess whether retrying at that moment had any realistic chance of succeeding.
• Timeouts Compounded the Issue: Instead of spacing out retries or deprioritizing failed jobs, the system pushed them back to the front of the queue too quickly, overloading the process.

So rather than simply being a server capacity issue, the real culprit was a configuration flaw in the retry mechanism that handled prompts in error states.

When Midjourney’s Prompt Queue Cracked Under Pressure

At its peak, Midjourney saw tens of thousands of concurrent users generating prompts simultaneously. The volume was magnificent but unrelenting. During such high-traffic periods, prompts would queue up in long lines, waiting for GPU resources to be allocated. Normally, some delay is expected. But the situation devolved when failed jobs started looping back automatically, clogging and eventually freezing specific job pipelines.

Here’s an example of the cycle that ensued:

1. User submits prompt → enters job queue.
2. Job attempts to generate but fails → 504 Timeout returned.
3. Retry loop re-adds the same prompt to the queue.
4. Prompt fails again → same error returned.
5. Repeated until the prompt is manually canceled or the system halts the loop.

The persistent failure to generate even basic images naturally affected user trust, prompting Midjourney’s engineering team to take action. Users also began sharing screenshots of these errors in public forums, shining a spotlight on the problem.

Rethinking Retry Logic: Smarter Intervals

The turning point came when engineers revamped how the system dealt with failed prompts. Instead of employing brute force retries, they introduced an intelligent retry system guided by contextual delay strategies.

Key improvements included:

• Dynamic Retry Intervals: Rather than using a fixed 1-minute retry, the system began evaluating the current queue length and adjusted delay intervals accordingly—sometimes skipping retries altogether during peak times.
• Error Categorization: Not all failures were treated equally. Prompts returning identical errors within a short window were flagged, avoiding reprocessing until supporting conditions had changed.
• Backoff Algorithms: Exponential backoff with jitter was integrated to randomize retry times, preventing massive rehits to the server in synchronized waves.

This new strategy was informed in part by distributed systems best practices common in high-availability services such as AWS and Google Cloud.

Stabilizing Large Queue Generations

As soon as the retry interval logic was tuned to reflect real-time queue health and job behavior, Midjourney began to stabilize. Rather than creating pressure points, the system spread out retries in a way that respected CPU/GPU availability and rejected jobs that failed for unsolvable reasons.

Technical adaptations included:

• Load Monitors: Implementations were added to sense when Midjourney’s rendering clusters reached critical mass, preventing new jobs from retrying until load was normalized.
• Dynamic De-prioritization: Repeatedly failing jobs were tagged as “low priority” and placed at the back of the queue.
• Job Metrics Logging: Engineers built a dashboard to view retry patterns in real time, helping them tweak the system with data-driven decisions.

This level of refinement meant that users submitting similar prompts wouldn’t all crash the system at once. And when a genuine problem occurred—like hardware outages or slow model responses—the system adapted instead of overwhelming itself with retries.

An Unexpected Bonus: Better Time-to-Render

Interestingly, after retry logic was tuned, another benefit emerged: faster average render times. Because the job queue was no longer congested with looping prompts, valid jobs reached GPUs quicker. The optimized retry schedule freed up system resources and boosted prompt success rates.

Community feedback reflected the improvement. Reddit and Discord channels saw a decline in thread complaints about missing images and stuck queues. The error messages dwindled, and GPU usage stabilized.

Lessons Learned and Influence on Future Releases

The 504 Gateway Timeout bug and its chaotic effects taught Midjourney’s dev team some crucial lessons:

• Retry logic must be adaptive, not static.
• Queue health monitoring is as important as GPU capacity planning.
• Feedback loops are essential in automated systems prone to failure loops.

These insights not only resolved a crisis but laid the groundwork for better infrastructure scaling in future Midjourney versions. The retry interval update has since been baked into newer releases, and its sophistication continues to evolve alongside Midjourney’s ever-expanding feature set.

Conclusion

The infamous “Job timed out (504 Gateway Timeout)” error shook Midjourney users and gave engineers a moment of reckoning. By diagnosing the root cause and implementing smarter retry intervals, Midjourney managed to stabilize its generation system even under intense load. This episode serves as a case study in how smart retry logic, observability, and careful queue management can turn a significant backend failure into a launchpad for system resilience and optimization. As AI platforms grow, these lessons will likely resonate far beyond Midjourney alone.