Datadog veterans launch AI coding startup Niteshift on a bet against Big AI lock-in
AI coding agent startup Niteshift has raised a $7 million seed round from a who's who of angels. It's betting companies will want power over, not lock-in with model makers.
AI coding agent startup Niteshift has raised a $7 million seed round led by Greylock’s Jerry Chen. That’s a modest sum by AI standards, but the startup, founded by two former early Datadog engineers, has attracted some big-name angels like Reid Hoffman, Datadog’s Olivier Pomel and Alexis Lê-Quôc, Ankur Goyal of Braintrust, and Misha Laskin of Reflection AI.
Founded by Sajid Mehmood and Conor Branagan, who helped grow Datadog from its early days to a multi-billion valuation, the company has entered the crowded AI coding space with a compelling idea: Why would any company trust its most sensitive assets — code that runs its products — directly to model makers like OpenAI and Anthropic, given that those companies are constantly “killing” startups and businesses by launching competing apps?
Mehmood, who is CEO, likens it to Datadog’s early growth, when the monitoring company won e-commerce customers who refused to build on Amazon Web Services. It was a reasonable concern, given that Amazon was simultaneously putting many of those same retail stores out of business in what became known as the “ retail apocalypse .”
The AI equivalent, as Mehmood sees it, is already underway. Anthropic, OpenAI, and others are moving fast into vertical software markets — what some are calling the SaaSpocalypse .
“At Datadog we saw this clearly,” Mehmood said. “A big part of our multicloud business came from e-commerce businesses who did not want to run on Amazon, right? … We are absolutely going to see the same dynamic as Anthropic goes to compete in legal and healthcare and finance and whatever else.”
The bet is that companies will increasingly seek infrastructure that separates the coding model from all the other orchestration needed to ensure AI-generated code is properly vetted and maintained (and that they’ll want a vendor without a competing agenda).
To be clear, Niteshift isn’t replacing Claude Code or Codex, the two most popular coding agents. It argues that it reduces dependence on them.
Niteshift’s AI coding cloud will route between those models — along with open source options and others — based on the needs of each project.
“Being able to switch between GPT and Claude models is important,” Mehmood said, “Everybody’s worried about getting stepped on by these giants.”
That idea is what got Greylock’s Chen to bite.
“As the frontier labs move up the stack, there’s an opportunity to offer customers an alternate path: unbundling their agents from the infrastructure they run on,” Chen told TechCrunch. “Niteshift is building the platform that enables this for coding agents, letting customers invest deeply in their developer tooling without locking themselves into a single model or agent vendor.”
More than that, Niteshift isn’t selling tokens. It sells infrastructure, charging like a cloud provider, with per-minute usage rates.
“Everybody else is selling labor replacement intelligence,” Mehmood said. “We’re selling software to agents, as opposed to humans — but we’re still out here selling software.”
Even so, Niteshift is entering a crowded market of AI coding tools. Model independence isn’t a novel idea, and Niteshift’s competitors have a massive head start. That includes Cursor, though it could soon be gobbled up by SpaceX ; Cognition, which just raised $1 billion at a $26 billion valuation ; Amazon Bedrock; and AI gateway platform OpenRouter, which just raised $113 million at a $1.3 billion valuation . The list goes on.
Mehmood’s answer to all of that is the founding team’s depth. Mehmood and Branagan didn’t just study these problems — they lived them, scaling Datadog through the exact growing pains that large engineering organizations now face with AI-generated code. Teams, he said, need to run, test, and verify software autonomously in their real production environments, and they need infrastructure built by people who’ve done it at scale.
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Key takeaways
- Niteshift offers an alternative to the lock-in of major AI companies, providing greater control to businesses over their coding solutions.
- The flexibility to switch between different AI models may be an important differentiator for Brazilian companies seeking innovation.
- Niteshift's movement could drive a growing demand for customized and secure AI solutions in Brazil.
Editorial analysis
The founding of Niteshift by former Datadog engineers highlights a growing trend in the tech sector: the search for greater control and autonomy over AI tools. In Brazil, where the startup ecosystem is in full expansion, this approach may resonate with companies that fear dependence on tech giants like OpenAI and Anthropic. The idea that companies should be able to manage their own AI solutions without the risk of being 'replaced' by competing products is particularly relevant in a market where innovation is rapid and competition is fierce.
Moreover, Niteshift enters a crowded space but with a differentiated proposal that may attract companies seeking not only efficiency but also security in their operations. The ability to switch between different AI models, such as GPT and Claude, can provide a flexibility that many Brazilian companies have yet to fully explore. This could be an important differentiator for startups and companies developing solutions tailored to the local market.
Niteshift's movement may also influence how Brazilian companies approach AI adoption in their operations. As more startups and established firms recognize the importance of avoiding lock-in with AI vendors, we can expect an increase in demand for solutions that offer greater control and customization. This could lead to a more diverse and innovative ecosystem where companies feel more empowered to experiment and implement AI technologies independently.
Finally, it is important to observe how Niteshift will position itself in a market that already has established solutions. The ability to differentiate and offer real value will be crucial for its success. What follows will be how the startup manages to scale its operation and gain the trust of companies seeking viable and secure alternatives for their AI coding needs.
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