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One shot to the right code. Atelier is a context runtime and cost-optimization layer for AI coding agents. Instead of passive indexing or simple output compression, it manages the full context window end-to-end — making it 25% faster while cutting cost by 30% (up to 67% on some workloads), audited head-to-head on SWE-bench. It also ships the strongest code search we've measured: 0.727 MRR vs the best of 10 rival tools' 0.557. Works with Claude Code, Codex, Cursor, and any MCP-compatible agent.
If you use AI coding agents heavily, you have probably tried tools that promise "efficient context" or big token savings, but still noticed your subscription limits disappearing fast.
I got tired of measuring only tiny slices of the problem, so I built Atelier.
Atelier is a context runtime for AI coding agents. It manages what gets sent to the model end-to-end, with the goal of reducing real task cost instead of only reducing tokens in one step.
The core idea is simple: measure the full workflow.
Across six audited benchmarks — SWE-bench, Terminal-Bench, and real exploration/Q&A workloads — Atelier shows about 30% average end-to-end cost reduction, with up to 67% on some workloads. The full per-task breakdown is public, including wins and losses.
It is not just cheaper either. On SWE-bench, Atelier resolved more tasks than the baseline: +12pp on Verified, +6.7pp on Lite, and +2pp on Pro. So the cost reduction is not coming from simply doing less work.
Atelier also includes code search evaluation. It achieved 0.727 MRR against 10 named tools, with the best rival at 0.557, measured on the same ~7,200 queries (15 repos, including linux kernel) instead of a cherry-picked set.
Why it works:
Atelier reduces cost on both sides of the LLM call.
It cuts noisy input by skipping irrelevant context and giving the model what it actually needs. In exploration tasks, this cuts cache-read context by up to 92%.
It also reduces output cost through telegraphic responses (short, no fluff), helping the model respond with less unnecessary text. In Q&A tasks, this reduces output tokens by up to 45%.
The result is a more practical metric: real end-to-end task cost.
the end to end savings numbers are compelling, especially the public per-task breakdown. curious about the other side of the tradeoff though - if it's actively evicting/compressing context that looks irrelevant right now, what happens when something from way earlier turns out to matter again later, like an architectural decision or constraint mentioned once and never repeated? does it ever have to re-derive that, or is some amount of that risk just the cost of the token savings?
finally a context layer that actually shows measured wins on swe-bench instead of vague claims, and the 0.727 mrr on code search jumped out after i kept hitting dead ends with other tools
I like that you're optimizing the runtime instead of the model.
As coding models continue to converge, I think the bigger advantage shifts toward whoever controls context efficiency and execution costs rather than who has the smartest underlying model.
That feels like a more durable layer.
The SWE-bench numbers look legit, nice work. One thing I'd love to see is a local replay mode where I can dry-run a session against a cached snapshot of my repo to compare token spend before and after tuning context strategies. Would make it way easier to convince the rest of my team the savings are real before we wire it into our CI pipeline.
the SWE-bench results are genuinely impressive, and love that it plays nice with MCP out of the box. one thing that would help adoption on my team: a dry-run mode that shows which files would be evicted or compressed before you commit, so we can see the cost savings in real time without worrying about a bad truncation on a tricky refactor. would make it way easier to trust in production pipelines.
Plugged it into Claude Code on a messy monorepo and the cost drop showed up the same day, no tuning. The search numbers sound about right too, it kept finding the right file without me babysitting the agent.
How does the 30% cost cut actually hold up on longer sessions where context grows well past 200k tokens, or is the savings mostly on smaller windows?
Plugged it into Claude Code on a refactor I was already dreading and the cost drop was noticeable by the end of the session. The code search results felt sharper than what I usually get with Cursor's built-in.
About Atelier faster runtime for coding agents on Product Hunt
“25% faster while cutting token costs by 30%-67%”
Atelier faster runtime for coding agents was submitted on Product Hunt and earned 20 upvotes and 19 comments, placing #21 on the daily leaderboard. One shot to the right code. Atelier is a context runtime and cost-optimization layer for AI coding agents. Instead of passive indexing or simple output compression, it manages the full context window end-to-end — making it 25% faster while cutting cost by 30% (up to 67% on some workloads), audited head-to-head on SWE-bench. It also ships the strongest code search we've measured: 0.727 MRR vs the best of 10 rival tools' 0.557. Works with Claude Code, Codex, Cursor, and any MCP-compatible agent.
Atelier faster runtime for coding agents was featured in Software Engineering (42.7k followers), Developer Tools (515.9k followers) and SDK (800 followers) on Product Hunt. Together, these topics include over 83.8k products, making this a competitive space to launch in.
Who hunted Atelier faster runtime for coding agents?
Atelier faster runtime for coding agents was hunted by pankaj kumar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Atelier faster runtime for coding agents stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
pankaj kumar
Hey Product Hunt!
If you use AI coding agents heavily, you have probably tried tools that promise "efficient context" or big token savings, but still noticed your subscription limits disappearing fast.
I got tired of measuring only tiny slices of the problem, so I built Atelier.
Atelier is a context runtime for AI coding agents. It manages what gets sent to the model end-to-end, with the goal of reducing real task cost instead of only reducing tokens in one step.
The core idea is simple: measure the full workflow.
Across six audited benchmarks — SWE-bench, Terminal-Bench, and real exploration/Q&A workloads — Atelier shows about 30% average end-to-end cost reduction, with up to 67% on some workloads. The full per-task breakdown is public, including wins and losses.
It is not just cheaper either. On SWE-bench, Atelier resolved more tasks than the baseline: +12pp on Verified, +6.7pp on Lite, and +2pp on Pro. So the cost reduction is not coming from simply doing less work.
Atelier also includes code search evaluation. It achieved 0.727 MRR against 10 named tools, with the best rival at 0.557, measured on the same ~7,200 queries (15 repos, including linux kernel) instead of a cherry-picked set.
Why it works:
Atelier reduces cost on both sides of the LLM call.
It cuts noisy input by skipping irrelevant context and giving the model what it actually needs. In exploration tasks, this cuts cache-read context by up to 92%.
It also reduces output cost through telegraphic responses (short, no fluff), helping the model respond with less unnecessary text. In Q&A tasks, this reduces output tokens by up to 45%.
The result is a more practical metric: real end-to-end task cost.