Private AI Pricing in 2026: Real Plans From €55 to €15K

Private AI pricing comes in two shapes: per-token metering, where you pay for every input and output token, and flat-rate managed inference, where a fixed monthly fee buys dedicated capacity. For the steady, high-volume workloads most teams run in production, flat-rate is easier to budget and usually cheaper. Tessera prices private inference as a flat monthly rate on dedicated EU and LATAM GPUs, running open-weight models like Qwen3.6-35B-A3B behind an OpenAI-compatible API, with no per-token overages and zero training on your data.

Two Ways to Pay for Inference

Per-token pricing scales your bill directly with usage. You pay a rate per million input tokens and a higher rate per million output tokens, so costs rise and fall with every prompt, retry, and long context window.

Published per-token rates vary widely by model. OpenAI’s GPT-5.4 Nano runs $0.20 per million input tokens and $1.25 per million output tokens (OpenAI↗). DeepSeek V4-Flash lists $0.14 per million input and $0.28 per million output, with cache hits cutting cached input to about $0.0028 per million (DeepSeek↗). The rates look tiny per token, but they are unpredictable in aggregate: a busy month of support tickets or document processing can double last month’s invoice.

Flat-rate managed inference inverts that. You reserve dedicated capacity for a fixed monthly fee, and your cost stays the same whether traffic is light or spiking. For production workloads with steady request patterns, that predictability is usually worth more than shaving fractions of a cent off each token.

A Worked Example: How a Per-Token Bill Swings

Say a customer-support workflow processes 50 million input tokens and 10 million output tokens in a month. On GPT-5.4 Nano at $0.20 and $1.25 per million (OpenAI↗), that is $10.00 for input plus $12.50 for output, or $22.50 for the month. Modest, until a product launch doubles your ticket volume and the same workflow costs $45.00. Switch to a reasoning-heavy model for harder tickets and the per-token rate climbs again.

That is the core problem with metering: your bill tracks demand, and demand is exactly the thing you cannot predict. Providers offer levers to soften it. OpenAI’s Batch API discounts asynchronous jobs by 50% and cached input tokens cost up to 90% less than fresh ones (OpenAI↗); DeepSeek’s cache hits drop cached input to roughly one-fiftieth of the miss rate (DeepSeek↗). Those discounts are real, but they require engineering work to capture and they still leave your headline cost moving with traffic.

A flat monthly rate removes that variance entirely. You size capacity once, and a launch-week spike costs the same as a quiet week.

The Hidden Costs of Per-Token Pricing

The token rate is only the visible part of a metered bill. Rate limits and throttling during peak hours push you toward higher-priced tiers. Failed requests and retries still consume tokens. Long system prompts and tool definitions inflate every call, so context bloat quietly raises the input count on workloads you thought were small.

Capturing the published discounts adds its own cost: prompt caching, batching, and routing logic are engineering projects, not configuration toggles. Teams that lack the time to build them pay the full metered rate. With a flat plan, that optimization work is the provider’s problem, and the savings are already priced in.

What You Pay For with Managed Inference

A flat managed-inference fee bundles the things a per-token rate hides: dedicated GPU compute, networking, API routing, capacity planning, and support. You are not hiring ML engineers or forecasting GPU demand, and you are not absorbing the cost of idle reserved hardware on a quiet month.

This is why managed inference has become accessible to lean teams. Instead of a capital outlay for GPUs and the staff to run them, you pay a single predictable fee for production-grade infrastructure from day one. The provider amortizes expensive hardware across many tenants, so you reach enterprise-grade GPUs at a fraction of the cost of buying and maintaining them yourself.

What Private AI Actually Costs: A Real Price Ladder

Concrete numbers beat abstractions, so here is Tessera’s own flat-rate ladder as of July 2026. Every tier includes the open-model catalog behind an OpenAI-compatible API, EU and LATAM data residency, and no per-token metering.

Plan	Monthly price	Built for
Founder	€55	Solo founders: a managed Hermes assistant on Telegram plus flat-rate AI, 20 RPM sustained
Async	€200	Queue-based batch jobs, no sustained RPM
Lite	€450	50 RPM sustained, burst to 100
Pro	€650	200 RPM sustained, burst to 400
Pro+	€1,200	500 RPM sustained, burst to 700, priority scheduling
Scale	from €5,000	High-volume dedicated capacity
Enterprise	from €15,000	Custom deployments and SLAs

Annual billing takes roughly 15% off the self-serve tiers. The point of publishing the ladder is its shape as much as the figures: entry starts at €55 per month, and the price grows with sustained throughput, never with how many tokens a busy month burns.

What Drives Private AI Cost

Three factors set your bill:

• Compute. Dedicated GPUs cost more than shared serverless capacity, but they remove noisy-neighbor latency spikes and give you consistent throughput.
• Model size. Larger models need more GPU memory, which raises cost. Open-weight models like Qwen3.6-35B-A3B run efficiently on mid-tier GPUs, so you get strong quality without paying for oversized hardware.
• Data residency. Keeping inference inside the EU or LATAM avoids cross-border transfer risk and the compliance overhead that comes with it.

Because Qwen3.6-35B-A3B ships as open weights, there is no per-token model license layered on top of compute, unlike proprietary APIs such as GPT-5.4 Nano at $0.20 input and $1.25 output per million tokens (OpenAI↗). You pay for the GPU time you reserve, not for access to the model, and you are never locked into a black box you cannot inspect.

Self-Hosting vs Managed: Capex vs Opex

Running your own GPUs turns inference into a capital project: hardware purchase, facility cooling, driver maintenance, security hardening, and at least one engineer who understands the stack. It can win for workloads that run flat-out 24/7 at large scale, where owning the hardware beats renting it.

For most teams, managed inference is the better economic fit. You convert that capital expense into a predictable operating expense, and the provider absorbs the risk of a failed GPU or a botched driver update. The trade is straightforward: slightly higher unit cost in exchange for zero infrastructure overhead and a bill you can forecast. For the broader buyer view, see private AI for small business.

Where Flat-Rate Wins

Flat-rate pricing pays off once usage is steady and high-volume. The fixed fee absorbs the traffic spikes that would otherwise blow up a per-token bill, so the number you budget is the number you pay. There are no overage line items to reconcile and no surprise invoice after a busy quarter. For a small team, that predictability often matters more than the headline per-token rate: a stable line item you can plan around beats a cheaper rate that swings with demand.

Per-token still makes sense for spiky, low-volume experiments where you may go days without a request. The crossover point depends on your own traffic, which is why modeling your real volume matters more than any rule of thumb.

Pricing and Data Residency

For regulated teams, price and compliance are linked. A flat-rate plan on dedicated EU or LATAM GPUs keeps customer data inside your jurisdiction, backed by a data processing agreement. GDPR Article 28↗ requires that agreement and binds a processor to act only on your documented instructions, so a compliant provider cannot reuse your prompts, including for training, without explicit authorization. Tessera runs all inference on EU and LATAM nodes and trains on none of your data, so the compliance work you would otherwise pay for in audit effort is built into the price.

How to Estimate Your Cost

Start from your real token volume. Pull a month of usage from your current provider, split it into input and output tokens, and compare the per-token total against a flat monthly rate for the same workload. Then add the variance: look at your busiest month, not your average one, because that is the bill a flat plan protects you from.

Model both with our pricing calculator, and if you are moving off a public API, the OpenAI API alternatives and migration guide covers the drop-in swap so you can switch base URL and keys without re-engineering your application.

FAQ

How much does a private AI system cost?

Managed flat-rate plans run from €55 per month (Tessera Founder, a solo tier with a managed assistant) to €450-1,200 for production tiers and €5,000+ for dedicated scale. Annual billing cuts roughly 15%. Self-hosting adds hardware and staff on top.

Is private AI cheaper than public cloud APIs?

For steady, high-volume workloads, usually yes: flat-rate caps your monthly spend, while per-token bills spike with traffic.

How is private AI priced?

Either per-token metering or a flat monthly fee. Tessera uses flat-rate pricing on dedicated EU and LATAM GPUs running open-weight models behind an OpenAI-compatible API.

What is the difference between per-token and flat-rate pricing?

Per-token scales with usage and is unpredictable month to month. Flat-rate caps spend regardless of token consumption.

Does model choice change the price?

Yes. Larger models need more GPU memory and cost more to serve. Open-weight models like Qwen3.6-35B-A3B run on mid-tier GPUs and carry no model license fee.

Do batching and caching discounts make per-token cheaper?

They help: OpenAI discounts batch jobs 50% and cached input up to 90% (OpenAI↗), but capturing them takes engineering work and your cost still moves with traffic.

Can I run private AI without managing hardware?

Yes. Managed inference handles GPU provisioning and scaling, so you reach an OpenAI-compatible endpoint without owning hardware.