Submission declined on 20 May 2026 by ChrysGalley (talk). This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. These tools are prone to specific issues that violate our policies: hallucinations: they often invent false information and cite non-existent references. unencyclopedic tone: they tend to be vague, promotional, or essay-like, rather than neutral and factual. copyright issues: they may closely paraphrase existing text, leading to copyright violations. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. This draft's references do not show that the subject meets Wikipedia's criteria for inclusion for organizations and companies. The draft requires multiple published secondary sources that: provide significant coverage: discuss the subject in detail, excluding routine coverage like product launches, staff appointments, or financial reports and listings in databases or listicles; are reliable: from reputable outlets with editorial oversight; are independent: not connected to the subject, such as press releases, the subject's own website, or sponsored content. Please add references that meet all three of these criteria. If none exist, the subject is not yet suitable for Wikipedia. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by ChrysGalley 17 days ago. Last edited by ChrysGalley 17 days ago. Reviewer: Inform author. Resubmit Please note that if the issues are not fixed, the draft will be declined again.

• Comment: Evidence of AI usage, particularly towards the end of the article. In addition the article does not show corporate depth via independent sources. The Register and Reuters sources are reporting matters covered by corporate trivia under WP:SIRS. It would be unusual for a new company of this profile to be able to get over the notability criteria, most companies cannot do so. ChrysGalley (talk) 15:41, 20 May 2026 (UTC)

Neon is an American cloud database company based in San Francisco, California, that develops a serverless PostgreSQL platform.^[1] Founded in 2021 by Nikita Shamgunov, Stas Kelvich, and Heikki Linnakangas, the company built a cloud-native architecture that separates storage from compute and adds features such as database branching, autoscaling, and point-in-time recovery.^{[2][3][4][5][6]} In May 2025, Databricks announced that it had agreed to acquire Neon; by 2026, Neon described itself as a Databricks company and Databricks described Neon as the core technology behind its Lakebase PostgreSQL service.^[7][8][9]

Neon was founded in 2021 by Nikita Shamgunov, Stas Kelvich, and Heikki Linnakangas.^[1] Kelvich later wrote that the company began as an incubation at Khosla Ventures.^[2] Neon's public company timeline places its first commit in March 2021.^[8]

The company released a technical preview on June 15, 2022, opened access more broadly in December 2022, and announced general availability on April 15, 2024.^[8][10] In July 2022, Neon said that it had completed a $30 million Series A-1 round, bringing total disclosed funding to $54.3 million.^[2] In August 2023, it announced another $46 million funding round.^[11]

In August 2024, Neon announced a $25 million strategic investment led by Microsoft's M12 to support expansion into Azure.^[12] TechCrunch reported that the company had raised $130.6 million in total and planned to expand its workforce from about 100 to 120 employees.^[1]

Neon's main product is a managed, usage-based PostgreSQL service with free and paid plans.^[1][13] The company has described the service as a serverless, cloud-native reworking of Postgres in which storage and compute are separated while maintaining compatibility with upstream PostgreSQL.^[3][10] Neon documentation highlights branching, autoscaling, scale to zero, and instant restore as core platform features.^{[4][5][14][6]}

In Neon, a branch is a copy-on-write clone of data that can be created from a current or past state.^[4] Autoscaling adjusts compute resources in response to load, while scale to zero suspends inactive compute after five minutes and resumes it automatically when queried again.^[5][14] Instant restore, which Neon also describes as point-in-time restore, allows a root branch to be restored to an earlier point in time within its restore window.^[6]

On May 14, 2025, Databricks announced that it had agreed to acquire Neon.^[7] Databricks said the deal was intended to give developers and AI systems a serverless Postgres foundation that could better support agent-created databases and other high-frequency workloads.^[7][13] Neon said the transaction would give it the scale and backing to accelerate its mission.^[19]

Databricks' official press release said the proposed acquisition was subject to customary closing conditions and any required regulatory clearances, but it did not state a purchase price.^[7] Reuters and TechCrunch reported the deal at about US$1 billion.^[20][13]

Databricks and Neon both emphasized the platform's role in AI-agent workflows. Databricks said internal telemetry showed that more than 80% of databases provisioned on Neon were being created automatically by AI agents rather than by humans; Neon made a similar claim in its own announcement.^[7][19] At Databricks' Data + AI Summit in June 2025, the company introduced Lakebase, a fully managed PostgreSQL database for data applications and AI.^[21] By December 2025, Databricks said Lakebase's autoscaling, branching, and recovery features were enabled by combining technology from the Neon acquisition with Databricks infrastructure.^[22]

Coverage of Neon in the technology press often framed it as part of a resurgence of Postgres-focused developer tooling. TechCrunch described Neon as an open-source alternative to Amazon Aurora PostgreSQL and reported in 2024 that hundreds of thousands of developers used its free tier, with thousands of startups and small and medium-sized businesses paying for premium services.^[1] Reuters reported in 2025 that Neon had partnered with Vercel, Replit, Cloudflare, GitHub, and Microsoft to integrate its serverless PostgreSQL service into widely used developer tools and platforms.^[20]

Coverage of the Databricks transaction linked it to a broader convergence of operational databases, analytics, and AI infrastructure. Reuters said Databricks wanted Neon to help enterprises develop and use AI agents more easily.^[20] The Register later wrote that Databricks and Snowflake had both moved to bring PostgreSQL-based transactional systems onto their platforms, and in 2026 it quoted IDC analyst Devin Pratt as describing this as a broader trend in which vendors pair operational databases with analytics to support real-time and agentic AI workflows.^[23][24] In January 2026, Databricks said that on Neon, AI agents were creating 80% of databases and 97% of database branches, and described Neon as the core technology behind Lakebase.^[9]