Enterprise AI Platforms: How Mayura Compares
What Are Enterprise AI Platforms?
Enterprise AI platforms are the large systems organizations adopt to run AI at scale: durable execution and agent infrastructure like Temporal and Amazon Bedrock AgentCore, and data-and-analytics platforms like Palantir AIP that connect AI to an operational footprint a company already runs. They offer reach and integration in exchange for a deep commitment to one vendor's stack.
Mayura AI approaches the same job edge-first and cloud-optional. Mayura delivers durable Continuous Intelligence on hardware you own, reading the feeds you follow, the inboxes you point it at, the web pages that matter to you, and the PDFs they link to, so the platform decision does not also hand a vendor control over where your data lives and how the bill scales. Each deployment carries one organization and no other.
How Should You Evaluate an Enterprise AI Platform?
Enterprise platform decisions outlast the team that makes them, so weigh them on the terms you will still be living with in three years.
| Dimension | Enterprise Platforms | Mayura |
|---|---|---|
| Data location | Cloud-hosted by default, and genuine on-premises paths exist: Temporal self-host and air-gapped Gemini on Google Distributed Cloud. | It reads the outside world, your files stay on your machine, and anything that goes out, you send. Sensitive data sits on hardware you own from the first run. |
| Economics | Consumption pricing is the norm: Temporal bills per Action plus storage, with a separate capacity tier, and AgentCore per vCPU-hour and per memory event. Committed-capacity tiers exist upmarket; based on public documentation, they are sized for large, sustained volume. | Fixed cost for local work. Cloud routing runs under a monthly spend cap you set. |
| Lock-in | Deep integration raises the cost of leaving. | At exit you keep your data, a full export of your knowledge base, and the hardware it ran on. |
| Durability | A managed control plane ties agent runs to the vendor's uptime and your contract. Temporal self-host removes that dependency, at the cost of operating it yourself. | State you can audit on hardware you own, with system state rebuilt from the stored history. |
| Time to value | A platform rollout is a program: procurement, integration, and enablement. | The pipeline runs from the first day, against the workloads you scope during setup. |
How Mayura Compares
The tradeoff in an enterprise platform shows up when the data is sensitive, the volume is continuous, and the pricing model meters exactly the thing you want running all the time. These platforms are built to scale a vendor relationship, which is a real strength when your workloads already live in one cloud.
Sovereign by architecture. Mayura runs the continuous work on your hardware. The largest platform vendors have credible answers here too, including Google's air-gapped Distributed Cloud, which is generally reached through a formal sovereignty program. Sovereignty here is a property of where the system runs, so data residency and cost predictability follow from the design, and a mid-market team gets that posture on one machine it owns.
Durable, auditable state. Every input, enrichment, and insight is recorded. Trace any output back to the source that produced it and rebuild system state from the stored history.
Flat Economics by design. Consumption pricing meters the continuous, high-volume processing that makes AI worth running. With Mayura, running it harder does not cost more on local models, bounded by the throughput of the machine you own. Raising that ceiling is a hardware decision while the license holds flat.
When Mayura Fits
- Sovereignty is a hard requirement, from regulation, contracts, or risk posture, and a private tier on a vendor's hardware does not clear the bar.
- Continuous, high-volume processing where consumption pricing creates budget unpredictability.
- You want durable, auditable state that rebuilds from the stored history, without standing up and paying for a managed agent control plane.
- The board asked for an AI answer and you need a concrete first step: one machine, one deployment, and a working system to show at the next meeting.
If a platform decision is on your desk right now, request an Opportunity Audit and we will scope which workloads a continuous system on your own hardware should carry before you commit.
When an Enterprise Platform Fits
- Your data and workloads already live in one cloud or data platform, and keeping AI next to them outweighs sovereignty concerns.
- You need one vendor's breadth across storage, analytics, and AI, and are comfortable with the commitment that brings.
- Burst scale matters more than fixed cost, and elastic capacity justifies consumption pricing.
- You have the platform team to stand up and operate a self-hosted or air-gapped deployment of one of these platforms yourself.
Compare the Options
- Agent Infrastructure and Mayura: How Mayura's Continuous Intelligence compares with Temporal, Amazon Bedrock AgentCore, and Northflank.
Learn More
- Solutions: See which problem Mayura solves for your organization.
- Platform: Explore the architecture behind Mayura's edge-first approach.
- Talk to us about an Opportunity Audit: a scoped look at your data streams and what continuous processing would cost next to a platform contract.