The move from proof of concept or prototype to production is a common stumbling block for AI projects, and one that database services provider pgEdge is keen to help enterprises overcome.

AI applications often start with developers reaching for Postgres because it gives them a database that can handle an application’s operational data while supporting AI-related capabilities such as vector search, enabling them to build a prototype without adding another specialty vector database to the stack.

When that prototype matures into something that enterprises want to run at scale, though, the Postgres setup that worked for experimentation may not meet the enterprise’s requirements for security, high availability, geographic distribution, data sovereignty, or deployment control.

In that scenario, developers find themselves reworking the infrastructure beneath an application that is otherwise ready to ship, turning the move from prototype to production into an architectural exercise of its own.

Database services provider pgEdge aims to help developers avoid that difficult transition with a new Postgres cloud platform named Starfleet that will enable developers to build AI applications on Postgres and carry them into production without having to rework the underlying database infrastructure.

Developers can start on pgEdge’s cloud while building the prototype and move the same Postgres platform to their own cloud or on-premises infrastructure, including air-gapped environments, the company said.

Starfleet also packages pgEdge’s distributed Postgres database with agentic AI capabilities such as an MCP server to connect to coding agents and RAG servers, enabling developers to build agentic applications that scale from a single Postgres instance to multi-region clusters for high availability and zero downtime, it added.

Starfleet could help enterprises scale agentic apps

Analysts see Starfleet addressing a genuine enterprise problem, particularly around scaling agentic applications.

“The prototype-to-production gap is real, and it’s one of the most common places AI projects stall today. Enterprise users build applications with AI coding tools on hosted Postgres, and those apps reach IT without high availability, compliance controls, or an on-premises option. So, IT ends up killing the app, rebuilding it on a different database, or negotiating the security risk,” said Michael Leone, principal analyst at Moor Strategy and Insights.

In contrast, Starfleet’s flexible deployment options give IT teams a way to take those applications into enterprise environments without having to replace the underlying Postgres platform, helping them move applications into production faster and scale them more quickly, he said.

That reduction in the need to rewrite applications can also help CIOs address concerns around shadow IT, according to Ashish Chaturvedi, executive research leader at HFS Research.

“Enterprise teams are presently building applications without waiting for IT, and a CIO can either pursue those applications after the fact or provide a platform that meets security standards from the outset, creating a sanctioned destination for citizen-built AI applications, and a single Postgres standard across every environment with Starfleet,” Chaturvedi said.

The same standardization could also help CIOs address challenges around moving workloads while meeting data sovereignty requirements.

Since Starfleet can run in pgEdge’s cloud, a customer’s cloud, or an air-gapped environment, it enables enterprises to move workloads between those environments without switching to a different database platform in order to meet regulatory requirements, Chaturvedi said.

That can be particularly important for government, defense, and financial services organizations, where sensitive workloads may otherwise require a separate database stack for restricted environments, he said.

There are advantages for developers too. “The biggest win for developers is not having to rewrite the app for production,” Leone said.

Starfleet’s appeal may be strongest in regulated enterprises

Taken together, those advantages could make Starfleet more attractive to teams building AI applications for enterprise use, particularly in regulated sectors, primarily because Starfleet addresses developers’ concerns around working with modern coding tools through its MCP server and provides the flexibility to move applications into production without rewrites, Chaturvedi said.

However, it might face a “scale and visibility” challenge from competitors such as Neon and Crunchy Data as they have significantly larger resources behind them in the form of Databricks and Snowflake respectively, he added.

Starfleet is generally available.

This article first appeared on InfoWorld.

Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services.

Enterprises may not be willing to take that bet.

It’s been nine months since Microsoft unveiled HorizonDB, but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle?

AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB, which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications.

Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase, which became generally available on AWS and Azure this year, and Snowflake Postgres, which was made generally available in February 2026.

As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and a database-as-log design. The hyperscaler also positioned native vector search and deep integration with Foundry and Fabric as key differentiators for AI-heavy workloads.

No reason to wait

Those architectural differences may not be compelling enough for CIOs to wait for HorizonDB to become generally available, though.

“Most enterprises with urgent needs will not wait. A long preview window creates uncertainty around SLAs, pricing, operational maturity, and roadmap confidence,” said David Linthicum, an independent cloud consultant.

And, said Stephanie Walter, practice lead of AI stack at Hyperframe Research, enterprises cannot build mission-critical production plans around an undefined GA date, regional footprint or support commitment.

Given the difficulty of unwinding a poor database choice, enterprises will approach unknown quantities with caution.

“Database platforms eventually become sticky control points. Once the database is connected to the rest of the application, analytics, AI, and governance stack, switching becomes a business transformation rather than just an infrastructure swap,” said Michael Ni, principal analyst at Constellation Research.

In the case of a cloud database, there’s also the unwelcome possibility of “huge egress fees” in case of change, said Bradley Shimmin, lead of the data and analytics practice at The Futurum Group.

All that uncertainty is likely to lead enterprises to restrict HorizonDB to experimental use cases for now, Shimmin added.

Performance anxiety

Analysts also questioned whether HorizonDB’s technical differences will show up in performance benchmarks.

Microsoft has said HorizonDB can deliver up to three times the throughput of open-source PostgreSQL, but makes no comparisons with rival offerings such as Aurora or AlloyDB that it will compete with, Walter said.

The bigger question, according to Igor Ikonnikov, advisory fellow at Info-Tech Research Group, is whether those performance advantages, still largely on paper, translate into a meaningful difference in production.

“A database with a better compute benchmark can still be more expensive once resilience and ecosystem costs are included,” Ikonnikov said.

The economics also point to another HorizonDB limitation, particularly for workloads that are not continuously running, said Advait Patel, senior site reliability engineer at Broadcom.

HorizonDB currently uses provisioned compute rather than a serverless, scale-to-zero model, meaning customers continue to incur compute charges while an instance is provisioned, even if its workload is intermittent or idle, Patel said.

There are developer considerations too.

HorizonDB’s PostgreSQL compatibility does not necessarily mean every existing PostgreSQL application will move cleanly as in its current form the database supports only an approved set of PostgreSQL extensions rather than arbitrary ones, Walter said.

Who should wait?

For enterprises already deeply invested in Microsoft’s Azure ecosystem, those limitations may not be enough to rule out waiting for HorizonDB, Patel said: The chance to integrate the database with AI services and the wider Microsoft stack may outweigh immediate availability, he added.

That calculus also reflects how enterprises typically make database decisions in the first place: not by comparing databases in isolation, but by weighing how well they fit into the broader technology stack, including the cloud platform they have standardized on, Ikonnikov said.

For Azure shops, the choice may therefore be less about moving an existing workload away from Aurora or AlloyDB and more about whether a new Azure workload should start on Azure Database for PostgreSQL today or wait for HorizonDB when it becomes available, he said.

That may be an open question for some enterprises, said Devin Pratt, research director at IDC. “Plenty of organizations are still mid-decision, not locked in,” he said.

Microsoft finally offers a timeframe

Microsoft still won’t say exactly when HorizonDB will launch, with Shireesh Thota, corporate vice president for Azure Databases at Microsoft, saying only, “General availability for Azure HorizonDB is currently targeted for the second half of 2026.”

That narrows it down to a period of a little over four months, including Microsoft’s FabCon and Ignite conferences — an eternity in AI.

This article first appeared on InfoWorld.