Netflix wiz creates app to slash AI bills, then open sources it

AI + ML Netflix wiz creates app to slash AI bills, then open sources it Project Headroom could save you big money, too Joab Jackson Joab Jackson Published sun 31 May 2026 // 08:00 UTC As the COOs from both Uber and Microsoft recently learned, encouraging company engineers to use AI aggressively can lead to hefty usage bills, perhaps even offsetting all the gains from laying off employees.  The AI bills at Netflix may not be so eye-popping thanks to company senior engineer Tejas Chopra, who has created software to prune agent instructions, as measured in tokens, before they hit the LLM.  Chopra has estimated that as much as 90% of tokens are redundant to the giant thinking machine of your choice.  REG AD Although not an official Netflix project, several teams there already use  Project Headroom , and a number of external projects rely on it as well. REG AD In a talk at the Open Source Summit last week, Chopra said that Headroom has saved an estimated $700,000 for its users, who collectively now have 200 billion tokens to spend elsewhere.  Not bad for an open source application that’s been out only since January. Headroom, currently at a still-raw v0.22, has gathered 2,000 stars on GitHub and has been forked over 120 times.   “A lot of our users are people who have been really burned by token costs, more than anything else,” Chopra said in his presentation.  Lossless context compression A $287 bill from Claude Sonnet first brought Chopra’s attention to the idea of token economization. The bill was typical home project stuff: a bit of debugging, some refactoring, MCP tools querying a database. At the time, Claude Sonnet’s token-based pricing seemed pretty generous: $3 for every million input tokens, or $6/million if you went over the 200,000 token limit for your context window. Still, that $287 added up quickly.  Upon deeper inspection, Chopra found a lot of this data was highly redundant to the LLM. By and large, his own ha