Piper Sandler names 5 software stocks cutting AI token costs

For most of the past two years, investors bought nearly every company tied to semiconductors, from chip designers to equipment makers, and those stocks rose across the board. Enterprise software, on the other hand, got treated as collateral damage, priced as though large language models would eventually make the whole category redundant. That assumption is now getting tested, and not by the software companies themselves.

Piper Sandler told clients on Wednesday that five infrastructure software names are positioned to solve the problem chief information officers complain about most: Running AI agents at scale costs far more than anyone budgeted. Piper Sandler's argument is that the customer data these companies already store can cut the number of tokens an AI agent needs to process, which lowers the cost of running it. Why Piper Sandler says these 5 software stocks cut AI token costs The note, led by analyst Rob Owens, named Elastic (ESTC), GitLab (GTLB), MongoDB (MDB), Snowflake (SNOW), and Atlassian (TEAM) as the primary beneficiaries, Investing.

com reported. A token is a chunk of text that is often smaller than a word. AI models charge by the token, counting both what you send in and what you get back.

Related: AI is quietly changing how portfolios are managed Owens wrote that the proprietary data already sitting inside these platforms can make models "significantly more accurate and efficient while dramatically reducing token usage costs." That will let companies expand AI adoption without costs rising too much. Early deployments showed token usage falling by 50% to 75% when clean organizational context was fed directly to the agent.

The mechanism is simple enough. AI uses fewer tokens and answers faster when given clean, organized data instead of messy data. The token math that changed enterprise AI budgets in 2026 Here is the part that confused a lot of investors this year: Token prices fell, yet AI bills went up anyway.

Owens noted that output tokens on newer frontier models run about 50% cheaper than the prior generation, yet improved reasoning capabilities caused consumption to increase. Reasoning models think in tokens, so a single query that once cost a few hundred tokens can now cost tens of thousands. More AI Stocks: AMD just landed its biggest AI deal yet The AI honeymoon appears over amid stock sell-off Morgan Stanley sends strong verdict on memory stocks Snowflake's pricing documentation shows how detailed this has become.

The company splits AI usage onto a separate consumption meter so customers can track token spend against regular processing costs. Story Continues That shift changed corporate behavior. Companies moved away from what Owens calls "Tokenmaxxing," or throwing unlimited model capacity at every problem.

Instead, the companies shifted toward model routing, which sends easy queries to cheap models and hard ones to expensive models. What the consumption pricing model means for revenue Vendors price context layers on consumption rather than per seat. That matters because the per-seat model is exactly what the market fears AI will destroy as headcounts shrink.

Piper Sandler called this an attractive incremental growth opportunity that also strengthens long-term competitive advantages. Put plainly, if a customer's AI agents run more queries next quarter, the vendor gets paid more without signing a single new user. Three things have to hold for that thesis to work: Enterprises must keep expanding agent deployments rather than pausing them.

Context layers must stay difficult enough to replicate that model vendors do not absorb the function. Consumption revenue must grow faster than any decline in traditional seat licenses. Owens said conversations with management teams and channel partners confirmed that organizations are turning to software to make AI more efficient.

How these 5 software stocks have actually traded The stocks Owens named have not moved as a group. MongoDB has been the standout, with a market capitalization near $27.7 billion in mid-July, up more than 62% from last year, according to StockAnalysis data.

The stock traded around $307 on July 21. Elastic went the other direction. Shares sat near $50 in recent trading, and Jefferies cut its target to $75 from $95 while keeping a Buy rating.

GitLab has been the weakest of the five. Analysts carry an average Hold rating with a 12-month target of $34.50, roughly 4% above where shares trade.

Snowflake sits in between, with 33 analysts rating it Strong Buy at an average target of $302.26. Atlassian rounds out the group with shares sitting near $86 as of the time of writing, well below the average analyst target of $139.

70 reported on Yahoo Finance. KeyBanc set the most recent target at $115 on July 8 while keeping an Overweight rating, which points to about 33% above where the stock trades. Piper Sandler says enterprise data platforms are becoming the cost control layer for corporate AI deployments.

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