Open Source Models for Enterprise AI — Complete Guide

The cost gap at scale

Pay per token forever — or own the stack.

At 100K agent runs, the difference is thousands of dollars per month.

Model	Type	Input / Output per MTok	~Cost per run*	10K runs	100K runs
Closed / Frontier
Opus 4.6	Closed	$5.00 / $15.00	$0.055	$550	$5,500
GPT-5.4	Closed	$2.50 / $10.00	$0.033	$325	$3,250
Sonnet 4.6	Closed	$3.00 / $15.00	$0.045	$450	$4,500
Gemini 3.1 Pro	Closed	$2.00 / $8.00	$0.026	$260	$2,600
Open / Open-weight
DeepSeek V4 Flash	Open	$0.14 / $0.28	$0.001	$13	$126
MiniMax M2.7	Open	$0.30 / $1.20	$0.004	$39	$390
Kimi K2	Open	$0.60 / $2.50	$0.008	$80	$800
GLM-5	Open	$1.00 / $3.20	$0.011	$114	$1,140
Qwen 3.5 Flash	Open	$0.10 / $0.40	$0.001	$9	$90

*Per agent run: ~5K input, ~2K output tokens. Sources: official API pricing, May 2026.

Eliminate shadow AI

When employees have every model, they stop going outside.

Shadow AI happens because approved tools are too limited. Seclura gives every team access to the broadest range of models — so there is no reason to use unapproved tools.

Why shadow AI happens

✗ Approved tool only supports one model

✗ No access to the latest open-source models

✗ Employees need different models for different tasks

✗ IT says no, teams find workarounds

Result: company data leaks to unapproved AI tools. Adds $670K to average breach cost.

How Seclura eliminates it

✓ Access to frontier and open-source models in one place

✓ Best model for each task — coding, writing, analysis, extraction

✓ New models available as they release — no waiting for IT

✓ All within governed, isolated infrastructure

Result: no reason to go outside. Every use case covered. Full audit trail.

The privacy spectrum

Your AI vendor sees everything.

Where your data goes — and who trains on it — depends on how you deploy AI.

👤 Your Team

Prompts, documents, data

🖥️ Vendor Server
Closed-source AI providers

🧠 Model Training

Your data improves their model

👁️

Sees all

Highest risk

Your data trains their models.

Free-tier and consumer AI chatbots — Every prompt goes to the provider’s servers. By default, your conversations improve their next model. Data retained 30+ days.

Stored on their servers · Trains future models unless you opt out · Staff can review flagged conversations

👤 Your Team

Prompts, documents, data

🖥️ Vendor Server
Processes & deletes

✕

No training

🛡️

Policy-based
trust only

Better, not enough

They promise not to look.

Enterprise API tiers from major providers — Zero retention, no training. But data still transits their infrastructure. You’re trusting a policy, not physics.

Not stored at rest · Not used for training · Still processed on their servers · Can be compelled by subpoena

👤 Your Team

☁️ Inference Provider
ZDR · No storage

✓

Private

🖥️ Model Creator
(the company that built the model)

✗ BLOCKED — zero access

Strong privacy

The model creator never sees your data.

Open-weight models via serverless inference providers — Open-source model on a separate inference provider with ZDR. The company that built the model has zero access. Same API experience, 10–50× cheaper.

Model creator has zero access · Inference provider has ZDR · No training · Same speed and quality

YOUR INFRASTRUCTURE

👤 Your Team

🔲 Your GPU
Open-source model

🔒

Locked

Nothing leaves this boundary

Maximum control

Data never leaves your network.

Self-hosted Llama, Mistral, Qwen, DeepSeek on your cloud or on-prem — No third party involved. For regulated industries, defence, research IP, and anything where “trust us” isn’t good enough.

Dedicated isolated infrastructure per org · No shared databases or compute · Air-gapped deployment available · Physically separated from other customers