reserved GPU capacity options (3–12 month commits) for H100s — who offers discounts and SLAs?

If you’re hunting for reserved H100 capacity on 3–12 month commits, you’re balancing three things: actually getting the GPUs, not overpaying, and not getting paged when a region or provider glitches. That means you’re really buying three attributes at once: capacity, discount, and an SLA you can live with.

This guide walks through the main reserved GPU capacity options for H100s, what kind of discounts and SLAs they attach to 3–12 month terms, and how a multi-cloud control plane like VESSL AI fits into that mix.

Quick takeaway:

• 3–6 month H100 commits usually trade a modest discount for flexibility.
• 12+ month H100 commits unlock the steepest discounts but may lock you to one cloud or provider.
• Multi-cloud GPU platforms like VESSL AI focus on guaranteed capacity + unified failover, then layer discounts on top.

---

What “reserved H100 capacity” actually buys you

Before picking a provider, it helps to be clear on what you’re buying beyond raw TFLOPs:

• Capacity guarantee:
  You want “we will have X H100s for you, on demand, for the entire term,” not “we’ll try our best.” For LLM post-training or long-running AI-for-Science workloads, this is the difference between shipping and slipping.

• Discount vs. flexibility:

  • Short terms (3–6 months) → lower discount, easier to adjust capacity.
  • Longer terms (12+ months) → higher discount, harder to back out of.

• SLA coverage:
  The real question is not “99.9% or 99.99% uptime?” but:

  • Are GPUs covered by the SLA or just the control plane?
  • How are preemptions/outages treated?
  • Do you get credits or just an apology?

• Scope:

  • Single provider, single region (simpler, but riskier).
  • Multi-region or multi-cloud (more resilient, but you need a control plane that doesn’t add operational pain).

---

Typical reserved H100 patterns on 3–12 month commits

Most H100 capacity offers fall into a few patterns:

3–6 month H100 reservations

Best if you’re:

• Running a specific project or grant with defined timelines.
• Unsure how your GPU footprint will evolve.
• Piloting a new model architecture where you may scale up or down sharply.

You can expect:

• Discounts: Often in the 10–20% range vs. pure on-demand, depending on provider and payment terms (upfront vs. monthly).
• SLA: Usually the same or slightly better than on-demand, but still tied tightly to a single provider’s regional health.
• Risk: If a region runs out or your provider de-prioritizes you versus larger tenants, “reserved” may still mean “please open a ticket.”

12 month H100 reservations

Best if you’re:

• Operating a production LLM service with known baseline load.
• Running multi-quarter or multi-year AI-for-Science or Physical AI workloads.
• Comfortable standardizing on the H100 for at least a year.

You can expect:

• Discounts: Often 20–40%+ vs. on-demand, especially with partial or full upfront payment.
• SLA: More formal capacity guarantees, response-time commitments, and sometimes dedicated account management.
• Risk: You’re more locked-in to a specific provider’s capacity and pricing structure, including any operational quirks in their stack.

---

Who offers discounts and SLAs on H100 reservations?

Below is how the major categories of vendors typically structure 3–12 month H100 reservations, and where VESSL AI fits.

1. Major public clouds

Examples: AWS, Google Cloud, Microsoft Azure (H100 availability varies by region and service).

What they offer for H100s:

• Reserved / committed use contracts:

  • You commit to a certain spend or instance family for 1–3 years.
  • In return, you get discounted rates that can be significant for longer terms.
  • For H100-class instances, 3–12 month “true” reservations can sometimes be arranged via sales, but most formal programs start at 1 year.

• Discount range:

  • Commonly 20–40% discount for longer commits (typically 1–3 years) versus on-demand.
  • For 3–12 month H100 blocks, discounts are often negotiated on a case-by-case basis through a sales rep, especially if you’re taking meaningful capacity.

• SLA profile:

  • Strong, documented SLAs for VM uptime and control plane services.
  • Less explicit guarantees that “you will always be able to scale to X H100s in region Y” unless you negotiate a special capacity reservation.
  • Outages or capacity shortages in a specific region are still your problem to route around.

When this path makes sense:

• You’re already heavily invested in one public cloud.
• Your security and procurement story is fully anchored there.
• You have the leverage (spend) to get real capacity guarantees on H100s for 3–12 months via enterprise agreements.

What to watch:

• Commitment granularity: many schemes are “dollars per hour” or instance-family-level, not “these exact H100s reserved for my team.”
• Regional risk: if your primary region has an issue, failover to another region may not be seamless, and your reserved economics may not fully carry over.

---

2. GPU-specialized cloud providers

Examples: Dedicated GPU clouds and bare-metal providers focused on A100/H100-class hardware.

What they offer for H100s:

• Term-based reservations:

  • 3–12 month H100 reservations are standard for these vendors.
  • You often reserve a specific number of GPUs and sometimes even specific nodes.

• Discount range:

  • 10–20%+ for 3–6 month terms.
  • 20–40%+ for 12 month commitments, especially for larger footprints.
  • Discounts can stack further with higher volumes or upfront payment.

• SLA profile:

  • Uptime SLAs on the infrastructure and sometimes explicit language around GPU availability.
  • Typically less mature than the big three clouds in documentation and compliance unless you pick a more enterprise-focused vendor.

When this path makes sense:

• Your primary problem is “I need H100s now, in volume,” not “I need hundreds of managed services.”
• You’re comfortable integrating your own tools for orchestration, monitoring, and failover, or you wrap them under a higher-level platform.

What to watch:

• Fragmentation: if you split across multiple GPU clouds to hedge risk, you now own multi-cloud orchestration and monitoring.
• Operational overhead: more bare metal often means more job wrangling unless you bring your own control plane.

---

3. Multi-cloud GPU platforms (like VESSL AI)

This is where VESSL AI sits: not just selling GPUs, but acting as a GPU liquidity and orchestration layer across multiple providers and regions.

How VESSL AI handles reserved H100 capacity:

• Guaranteed capacity for scarce GPUs:

  • Reserved plans explicitly target scarce classes like A100, H100, H200, B200, GB200, B300.
  • With a reserved plan, VESSL AI secures H100 capacity for your team so you’re not fighting waitlists or region-level shortages every time you launch a run.

• Commitment terms:

  • Reserved plans start at 3 months, fitting neatly into the 3–12 month commit window.
  • You can plan a 3–6 month research project or a 12+ month production phase without daily capacity roulette.

• Discounts:

  • Reserved plans come with volume discounts over standard on-demand rates.
  • For substantial H100 usage and longer terms, discounts can reach up to ~40% compared with paying purely on-demand, depending on your footprint and term length.
  • Pricing is transparent: on-demand H100 SXM 80GB is publicly listed (e.g., $2.39/hr in current published prices), and reserved pricing is negotiated with clear terms.

• SLA and support posture:

  • SOC 2 Type II and ISO 27001 for security and compliance.
  • Enterprise buyers can negotiate SLAs, onboarding, and custom integrations, plus dedicated support tied to reserved plans.
  • The reliability story goes beyond one provider:
    • Auto Failover: if a provider or region has issues, workloads can shift to another provider with minimal disruption.
    • Multi-Cluster: unified view and control across regions and clusters, so you don’t build your own multi-cloud scheduler.

• Operational modes for H100s:

  • Spot: cheapest, preemptible capacity for experiments. Not for critical runs.
  • On-Demand: reliable H100 capacity with automatic failover. Good default for most training/inference.
  • Reserved: your long-term H100 base load with guaranteed capacity + discounts + dedicated support.

This structure lets you separate “how reliable does this workload need to be?” from “how long am I willing to commit?”:

• Use Reserved H100s for your baseline throughput over 3–12+ months.
• Use On-Demand H100s to burst above that, without negotiation.
• Use Spot H100s when you’re iterating architectures and can tolerate preemptions.

When VESSL AI makes the most sense:

• You’re hitting quota ceilings and waitlists on your current cloud.
• You want H100 reservations for 3–12 months but don’t want to be pinned to one underlying provider’s reliability.
• Your teams are losing time to job wrangling, managing environment quirks, and babysitting long runs.

What to watch:

• You’ll still want to talk to sales for custom reserved pricing on H100s, especially at higher volumes.
• As with any multi-cloud approach, you need a clear view of where data lives; VESSL helps via Cluster Storage and Object Storage, but you should design your data layout with this in mind.

---

How to choose among H100 reserved options (3–12 months)

Use a simple decision framework:

1. Start from your workload profile

• LLM post-training / long-running fine-tuning:

  • Needs: continuous H100 access, minimal interruptions.
  • Match: Reserved + On-Demand H100 capacity, with failover if possible.

• Physical AI / robotics / simulation:

  • Needs: large bursts, sometimes tied to hardware-in-the-loop schedules.
  • Match: Reserved H100s for baseline, burst via On-Demand or Spot.

• AI-for-Science (multi-week runs, sweeps):

  • Needs: predictable cost, high resiliency; interruptions can waste weeks.
  • Match: Reserved H100s with automatic failover and strong monitoring.

2. Decide how much lock-in you can tolerate

• Already all-in on a single hyperscaler and happy there?

  • Negotiate 3–12 month H100 capacity directly with that cloud.
  • Target range: 20–40% discounts for longer terms, but expect some rigidity.

• Want to hedge against outages and regional shortages?

  • Use a multi-cloud GPU platform that:
    • Guarantees H100 capacity via reserved plans.
    • Provides Auto Failover and Multi-Cluster as first-class features.

3. Match term length to reality, not optimism

• 3–6 months:

  • Great for new projects and grants.
  • Favors flexibility and smaller discounts.
  • VESSL’s 3-month+ reserved options are designed for this window.

• 12 months:

  • Best for stable production LLMs and large, ongoing research programs.
  • Unlocks the strongest discounts across most providers.
  • Use this for your core H100 footprint, not every experiment.

---

Where VESSL AI fits in your H100 capacity strategy

If you’re evaluating reserved H100 capacity options (3–12 month commits) and you care about both discounts and SLAs, VESSL AI essentially gives you:

• Guaranteed H100 capacity across multiple providers, from as short as 3 months.
• Volume discounts on top of transparent on-demand pricing (e.g., published H100 SXM rates), with potential up to ~40% savings for longer, larger reserved contracts.
• Operational reliability via:
  • Auto Failover between providers and regions.
  • Multi-Cluster for a unified operational view.
  • 24/7 support and enterprise-ready security (SOC 2 Type II, ISO 27001).
• Reduced job wrangling:
  • Web Console for visual cluster management.
  • CLI (vessl run) for native workflows.
  • Monitoring built in so you can “fire-and-forget” more runs.

Instead of stitching together one cloud’s reserved H100s, another’s spot H100s, and your own ad-hoc failover logic, you treat H100s as a pooled resource behind a single control surface.

---

Next Step

If you’re planning a 3–12 month H100-heavy roadmap and want both discounts and SLAs without giving up flexibility, the simplest path is to benchmark VESSL AI’s reserved capacity against whatever your primary cloud or GPU provider is offering.

Get Started