Private LLM and AI Ownership: How Mayura Compares

What Is a Private LLM?

A private LLM is a large language model deployed so that a customer's prompts and data are not shared with a public multi-tenant API, usually through a dedicated instance, a virtual private cloud, or a managed endpoint. For data isolation it is a real improvement on a shared public AI API. Owning the system is a separate question.

Private LLM providers sell a middle ground between a public cloud API and full on-premises ownership. Cohere leads with its Command models and the North agent platform, and in April 2026 moved to combine with Aleph Alpha in a sovereign-AI deal. AI21 Labs offers the Jamba model family and the Maestro orchestration system. Hugging Face provides the model hub plus dedicated Inference Endpoints. Anyscale provides a managed Ray platform for running training and inference workloads on your own or pooled GPUs. Each answers the objection "we will just run a private LLM in our VPC."

How Mayura Approaches Private Deployment

For most of these offerings, "private" means a dedicated or VPC instance running in the provider's or a hyperscaler's cloud, governed by the provider's control plane for licensing, updates, and telemetry, and billed per token or per compute-hour (Cohere pricing, Hugging Face pricing, Anyscale pricing).

Mayura exists for the buyer who reads that objection closely and finds the seam in it. Enterprise-tier on-premises deployment genuinely exists, and you are still licensing their model and assembling the system around it. Cohere offers on-premises deployment behind your firewall at the enterprise tier, which closes part of the gap. What it does not close is everything the model needs to become a working system: ingestion, enrichment, search, scheduling, and audit. Mayura's Continuous Intelligence architecture ships that whole system, running on your hardware, with the model as one swappable component inside it. One install serves one organization and no other.

Cohere, AI21, Hugging Face, Anyscale, and Mayura at a Glance

Dimension	Private LLM Providers	Mayura
What "private" means	Dedicated or VPC instance, usually in their or a hyperscaler's cloud	Processing on hardware you own, inside your network
Control plane	Theirs on managed and dedicated-endpoint offerings, for licensing, updates, and telemetry	Yours, with update checks optional
Pricing model	Per token or per compute-hour (Cohere, Anyscale)	Fixed license plus hardware you own
What you get	Model inference, and RAG or an agent layer in some cases	Ingestion, enrichment, search, and admin UI out of the box
Air-gap	Cohere documents on-premises deployment; the others publish cloud or managed-endpoint deployment as their default (per their own docs, July 2026)	Pull the cable and it keeps running on local models over everything it has already read
Processing model	Request and response endpoints	Continuous 24/7 processing
Ideal buyer	Enterprise IT and security, ML engineering teams	CTOs and VPs of Engineering at data-sensitive firms, where legal and finance sign alongside them

Where Mayura Wins

No vendor control plane. Most VPC deployments keep a vendor control plane for licensing, updates, and telemetry, so the dependency on the vendor survives the move into your tenancy. A Mayura deployment is self-contained. Update checks are optional, and pulling the network connection leaves the local models still working over everything already ingested.

You own the hardware and the system on it. With a private LLM the cloud compute stays on the invoice month after month. With Mayura the hardware is a capital asset you own, and the software runs on it under a fixed license.

Flat Economics on continuous work. These providers meter per token or per compute-hour, so on published list pricing a growing workload carries a growing bill. With Mayura, running local work harder does not cost more, and in our modeling that is the difference that matters for high-volume or 24/7 processing, bounded by the throughput of the machine you own.

A complete platform around the model. Hugging Face and Anyscale position themselves on inference, AI21 on its models and RAG, and Cohere pairs its models with an agent platform. Mayura ships ingestion for email, RSS, and web content, enrichment pipelines, semantic and vector search, cost tracking, and an admin UI as one system, so your team is not starting from an empty repo.

Continuous Intelligence that runs without a prompt. A private LLM waits for a query. Mayura processes your data streams around the clock, surfacing patterns before anyone asks. Every step is recorded, so any insight traces back to the source data that produced it.

Where Cohere, AI21, Hugging Face, and Anyscale Win

Compliance certifications today. Cohere is SOC 2 Type II compliant, and these providers field the sales motion that regulated buyers expect. With Mayura, every insight traces back to the source that produced it and system state rebuilds from the stored history. For a buyer whose checklist requires the badge on day one, that is their advantage.

Model variety and frontier options. Hugging Face hosts an enormous catalog of models, Cohere's Command family is aimed at enterprise deployments, and AI21 positions its Jamba family on long context. Mayura orchestrates open models through a local runner, and quality-critical tasks route to cloud APIs by choice, metered by that provider.

Marketplace procurement. Buying through the AWS, Azure, and GCP marketplaces is typically the path of least resistance for a team already deep in one cloud. Mayura is a hardware and license purchase, which is a different buying motion.

Dedicated support and RAG-as-a-service. Enterprise tiers in this category generally come with dedicated support teams and managed RAG. Mayura's support is tiered and productized, and retrieval runs locally so no data leaves your environment.

Key Differences

Private is a spectrum. A shared public API is the least private; a machine you own and can air-gap is the most private. VPC and dedicated-endpoint offerings land in between, which is a genuine improvement on a public API.

The control plane is the real dividing line. Those offerings still sit inside someone else's cloud, under their control plane and their terms. Even Cohere's on-prem option leaves you licensing their model and assembling the surrounding platform.

Then there is the meter. On published list pricing, per-token and per-hour billing ties your costs to your usage, so the more valuable the workload becomes, the more it charges you. A fixed license breaks that link. This is why the comparison sharpens on continuous, high-volume processing.

What each one ships. These providers sell inference, and in Cohere's case an agent layer on top. Mayura sells the finished pipeline: capture, enrich, search, and audit, ready to run.

Choose Mayura If

• You read "private LLM" closely and concluded you want the processing on hardware you own
• Your workload is continuous or high-volume and per-token pricing scales against you
• You need the processing to keep running on local models with no control plane phoning home
• You want ingestion, enrichment, search, and audit as one system
• Source-to-insight traceability matters more to your reviewers than a certification badge

If that is where you have landed, request an Opportunity Audit and we will scope what a private deployment on hardware you own would actually cost and cover.

Choose a Private LLM Provider If

• Your procurement checklist requires SOC 2 or ISO 27001 on day one
• You need first-party frontier models or an extremely long context window
• You buy through a cloud marketplace and want to stay inside an existing AWS, Azure, or GCP commitment
• Your team wants managed inference and will build the surrounding application itself

Plenty of teams run both, and that split works well. Use a private LLM provider where a certification or a specific model is the binding requirement. Use Mayura as the Continuous Intelligence system that processes sensitive, high-volume streams on hardware you own.

Learn More

• Solutions: see which problem Mayura solves for your organization
• Platform: explore the edge-first architecture behind Mayura
• Sovereign AI: How Mayura Compares: the model layer and the infrastructure layer, separated
• Cloud AI: How Mayura Compares: the category overview
• Talk to us about an Opportunity Audit: a scoped look at your data, hardware, and compliance requirements