What will SerenDB do differently from other Postgres DB companies to make it the David against the Goliaths?

My job-switch LinkedIn post to SerenAI founder sparked two great questions. The questions were so provocative. I will reflect on them below because I think they are helpful to stay focused on what we're doing at SerenAI.

Here's the blurb with any sensitive information removed.

"Hey, Taariq! Your post about SerenAI piques my curiosity. My last company, an enterprise analytics startup, was a large and sophisticated Postgres shop. Our CTO really believed in that platform for our geospatial use cases, and I learned all about scaling, developing for, and operating it in production.

Here are my two questions for you:

1. Why should a data store platform be agentic itself rather than a service (like an MCP server) that agentic workflows can plug into?

2. And what will SerenAI do differently from Supabase to make it the David against the Goliath?"

The answers to question #1:

1. Latency & Performance: It is the SerenDB view that in an era of equal access for most companies to agentic tooling, speed will become an issue that differentiates the AI enterprise winners from losers. You want to be faster than the competition. You don't want to be slow if all your competition has Claude Code, Codex, or Gemini as well.

Why is that? Well, AI agents make 100-1,000 database calls per workflow. They don't just make 1-10 like calls like we do with traditional applications. Remember, AI agents are tool calls from the LLM. These tools' calls are faster than human manual calls, and they can scale better than typical automated calls. Imagine your Architect Agent, Implementation Agent, and QA Agent, all working together to execute on your codebase or your workspace. They will execute calls to external functions and share information throughout the workflow. The call count will increase. Now, you add an external MCP server that handles these calls. What happens? Well, External MCP servers add network round-trips on every call. At enterprise scale, what happens when you add latency to high-frequency database operations? Yes, your agents must slow down calling the API. Not only will your agents slow down, but they will slow down fast. Native agentic database integration eliminates the middle layer of the MCP server. You can go faster, or your competition will. This is the vision of SerenDB.

2. Security & Cost Controls at the Data Layer: We've all been warned. Agents are autonomous and unpredictable. They can run runaway queries, exhaust connection pools, or rack up massive compute costs, accidentally, maliciously, or unintentionally. These controls MUST be at the database layer. Think about implementing rate limiting, cost caps, and query governors, per agent you spawn. You can't bolt these on via an external service. You also need to rewrite your MCP server, which will waste more time. Agent calls need to be intercepted at query execution time.

3. Agent-Aware Query Optimization: We're working on SerenDB as an agentic-first Postgres database. We're not trying to force our old business into AI. We see that the agentic workloads are fundamentally different: bursty, multi-tenant, unpredictable access patterns. The database needs to understand our rule: "This is agent traffic, not user traffic". We want the database to be responsive and to optimize connection pooling, query planning, and resource allocation in ways that differ from traditional OLTP/OLAP systems. Let's go back to the MCP servers. Well, External MCP servers can't see inside the database to tune this. This is not what MCP servers were built to do. Think of it this way: MCP servers are the API layer. MCP servers are great for tool calling. But the database itself needs to be agent-native to handle the volume, unpredictability, and security requirements agents create. SerenDB is meant to do just that.

Recapping Q2: What will SerenDB do differently from Supabase to make it the David against the Goliath?

Here are our answers:

The answers to question #2:

1. First off. We love Supabase, Neon, and Tiger Data: These companies are the best in distributed database engineering today. We at SerenAI deeply respect these companies. However, SerenDB wants to push a greater focus on performant databases at the Edge, not just functions. It's our understanding that Supabase has edge functions (compute at edge), but the database still lives in centralized AWS regions.

For geospatial queries that may involve extensive lat/long lookups, spatial joins, and bounding box queries on Supabase, every query still routes to us-east-1 or a similar region. With SerenDB, we're partnering with cloud providers to deploy the PostgreSQL database at hundreds of edge locations. Your transportation data lives close to the queries that access it. For real-time agent workload processing of location data globally, this is the difference between 20ms and 200ms of latency per query. Remember. Speed. Agents. Competition is all using the same LLM models as you.

2. Agent-Specific Resource Isolation: In the database-intensive enterprise, you know workloads. You have analytics jobs, API queries, batch processing, and many more. You could tune connection pools, vacuum schedules, and resource allocation for those patterns. AI agents are different: They are unpredictable, bursty, and multi-tenant by nature. One rogue agent can exhaust your connection pool or run a Cartesian join that kills performance for everyone else. We built agent-aware resource isolation at the database layer. Think resource isolation per AI Agent. We use query governors, connection limits, cost caps, and other tools. Supabase gives you general-purpose Postgres; we give you agent-hardened Postgres built from the ground up.

3. Native Cost & Security Controls for Autonomous Workloads: In the past, engineers wrote the queries. Engineers knew what was running. In the future, the AI agents will write their own queries autonomously. Engineers won't control what SQL they generate! We predict that, in the near future, billions of agents will run trillions of queries. This scale creates two new problems: (a) runaway costs from inefficient agent-generated queries, and (b) security risks from agents accessing data they shouldn't. We are building these controls into SerenDB. We deliver per-agent spending limits, query-complexity governors, and row-level security that understands agent context. We are also working on delivering new prompt and role security primitives that we'll announce soon. Supabase's RLS is app-level. SerenDB is agent-aware security and context security.

Bottom line: If you built today with AI agents running your geospatial analytics (instead of human engineers), SerenDB will give you a database purpose-built for the unpredictability, scale, and security challenges of autonomous agent workloads.

SerenDB has so much more to offer than what I've outlined here, and our customers and design partners are also requesting more from the database. Let's share a convo. Schedule a chat with me so we can get SerenDB into your company: https://calendly.com/taariq/30min

Cheers,
Taariq & Team SerenAI