Generative AI workloads introduce new challenges in cloud cost management and performance. Unlike traditional cloud services, AI models (especially large language models) run on scarce, specialized hardware (GPUs/TPUs) and often use token-based pricing – meaning you pay by the data processed (tokens) rather than simple compute hours. This has led cloud providers to offer two modes for AI inference capacity: on-demand (shared) capacity and provisioned (dedicated) capacity. In this guide, we explain what provisioned AI capacity means, how to start using it, best practices for managing it, and how it differs from the usual Reserved Instances or Savings Plans for compute.
On-Demand vs. Provisioned AI Capacity
On-Demand (Shared) Capacity is the flexible, pay-as-you-go option. You do not reserve any hardware upfront – you simply send requests to the cloud AI service and pay per use. This model is similar to using cloud VMs on-demand: no commitment, and you are billed only for what you consume. The trade-off is that performance can vary and isn’t guaranteed during peak times because resources are shared. High demand from many users can increase latency or even cause capacity to run out temporarily. In other words, it’s “live fast, die young” – very flexible, but potentially unreliable performance when everyone’s trying to grab a slice of the pie.
Provisioned (Dedicated) Capacity means you reserve a fixed amount of model-processing capacity for your exclusive use, usually by committing to pay for it ahead of time. This is akin to renting your own server in the cloud’s AI service. The cloud provider carves out dedicated GPU-powered resources that only your workloads will use, giving consistent performance and guaranteed throughput for your AI requests. You pay for this capacity whether you fully utilize it or not, typically at a discounted rate compared to pure on-demand usage. Think of it as the “slow and steady wins the race” approach – reliable and predictable, but requiring a commitment and upfront cost.
How Provisioned AI Capacity Works
When you purchase provisioned capacity for an AI service, you are essentially buying a fixed throughput allotment for a model. For example, in AWS Bedrock you purchase Model Units (MUs) as provisioned throughput. Each MU guarantees a certain number of input and output tokens the model can handle per minute at a fixed hourly rate. You can commit to different durations (e.g., no commitment vs. 1-month or 6-month term) – longer commitments give lower hourly pricing. Azure’s OpenAI service similarly offers Provisioned Throughput Units (PTUs), which you deploy for a chosen model to secure a tokens-per-minute rate. These PTUs are billed hourly, with significant discounts if you purchase longer-term Azure Reservations for them. Google Cloud’s Vertex AI calls its offering Provisioned Throughput, available as a fixed-cost subscription (weekly, monthly, etc.) that reserves throughput for a specific generative model. And Oracle Cloud Infrastructure (OCI) provides Dedicated AI Clusters, where you allocate “AI units” to host a model with a guaranteed capacity – for example, OCI requires at least a one-month (744 hour) commitment for a dedicated model hosting cluster.
In all cases, the idea is the same: you’re paying a fixed rate to reserve capacity so that your AI requests don’t have to wait in line behind others. You avoid the unpredictable latency of shared pools during peak usage, at the cost of potentially paying for idle time when your traffic is low. This approach is actually a throwback to old-school capacity planning – something cloud users hadn’t worried about much in the era of “infinite” elasticity, but which is now essential again due to GPU scarcity and high demand. If you under-provision, you risk slowdowns or outages for your AI features; if you over-provision, you could be paying for capacity you don’t fully use.
Key Differences from EC2 Reserved Instances/Savings Plans
At first glance, provisioned AI capacity sounds similar to reserving cloud VMs (like AWS EC2 Reserved Instances or Savings Plans) – both involve committing to pay for resources in exchange for lower rates. However, there are important differences:
- Resource Type: Traditional RIs reserve infrastructure (virtual CPU/RAM or specific VM instances). Provisioned AI capacity reserves a service throughput (e.g., “tokens per minute” of an AI model). It’s a higher-level abstraction. For example, an AWS EC2 Reserved Instance gives you a VM with certain vCPUs, whereas an AWS Bedrock Model Unit gives you the ability to process a certain token rate on a chosen model.
- Commitment Flexibility: AI capacity commitments can be shorter. Some providers allow monthly or even weekly terms. Google’s Vertex AI, for instance, offers commitments as short as 1 week for its generative model throughput subscriptions, or monthly/annual options for bigger discounts. This is more flexible than the typical 1-year or 3-year terms for EC2 RIs. AWS Bedrock offers no-commitment hourly provisioning (cancel anytime) or 1-, 6-, 12-month commitments with increasing discounts. Azure’s new model uses standard Azure Reservations (1-year or 3-year likely) for provisioned throughput. OCI requires at least 1 month for a dedicated AI cluster.
- Cost Measurement: With VM reservations, usage is in hours of instance uptime. For AI capacity, usage is measured in model-specific units like tokens or characters processed. This can be more opaque. Each model and provider has its own token definitions and rates, which complicates cost tracking. (As a FinOps practitioner quipped: “Every model talks about tokens, but each token is a bit different.”) It requires translating those token costs back into something meaningful for your business (e.g., cost per user request or per document processed).
- Primary Motivation – Performance vs. Cost: A key distinction is that many teams purchase dedicated AI capacity primarily to ensure performance and reliability, not just to save money. With EC2 RIs, you typically reserve to get a cost discount on steady workloads (capacity availability is seldom an issue for standard VMs). But with generative AI, capacity itself can be constrained (due to limited GPUs). Thus, provisioned capacity is often needed to guarantee your app can even function at peak load. The cost savings come as a bonus if you utilize it fully, but if you only care about cost and not performance, you could just stick to on-demand usage. In practice, you should aim for high utilization of what you reserve – one analysis found you might need around ~70% or more steady use of a provisioned instance for it to break even versus pay-as-you-go rates. If your utilization is lower, you’re paying a premium for peace of mind (which might be fine for critical user-facing features).
Best Practices for Using Provisioned AI Capacity
Adopting provisioned capacity for AI requires a more hands-on approach to capacity planning than many cloud teams are used to. Here are some best practices to ensure you get the benefits (consistent performance and lower unit costs) without runaway waste:
- Evaluate Latency and Reliability Requirements: Start by determining which AI workloads truly need the guaranteed performance of dedicated capacity. If an AI feature is user-facing, real-time, and critical (e.g., an assistant in your app that users expect instant responses from), it’s a good candidate for provisioned capacity. If instead it’s an internal or batch job that can tolerate variable slowness, on-demand might suffice. In short, use provisioned capacity for high-throughput, low-latency production workloads and stick with on-demand for sporadic or non-critical jobs.
- Right-Size Your Commitment (Start Small): Especially early on, teams often overestimate or misjudge how much throughput they need from an LLM service. It’s wise to start with a smaller capacity commitment than your absolute worst-case estimate. Because these services can be scaled (and most providers let you increase provisioned units if needed), you can add more later. This incremental approach prevents paying for a large chunk of capacity that sits idle if adoption or traffic is lower than expected. Begin with a conservative number of units/PTUs and scale up as you gather real usage data.
- Implement Failover Logic: A clever strategy is to combine both capacity types for a safety net. Failover logic means if your dedicated capacity is ever fully saturated or unavailable, your system automatically retries the request on the provider’s on-demand/shared pool. This requires engineering effort (your developers must handle the retry to a secondary endpoint), but it mitigates the risk of dropped requests. For example, you might route traffic to your provisioned endpoint normally, but if it returns a “capacity exceeded” error or high latency, you failover that request to the on-demand API. This way, the user still gets a response (perhaps slightly slower), and you don’t strictly need to over-provision for the absolute peak. Throttling less-critical requests is another complement to this – e.g., if capacity is maxed out, you might queue or reject non-essential jobs to keep quality high for primary users. Together, these techniques ensure resilience and cost-efficiency.
- Continuously Load Test and Monitor Utilization: Treat your reserved AI endpoint like a system you need to regularly tune and validate. It’s recommended to perform periodic load tests on your provisioned capacity – not only before go-live, but as an ongoing practice. Load testing reveals the true throughput (tokens per second/minute) your cluster can handle, which can shift over time as providers update models or infrastructure. In fact, throughput can improve as models or hardware are optimized, meaning you might handle more load than initially – if you don’t re-test, you wouldn’t know you have headroom to consolidate or serve more traffic without buying more units. Conversely, if there’s a degradation or an issue in the dedicated cluster, a load test can catch it early before it impacts customers. Set utilization KPIs such as target 70% peak usage on provisioned units, and track your token consumption metrics (all providers supply metrics for tokens processed). If you consistently see much lower utilization than expected, consider scaling down units or see if more of that traffic could run on-demand more cheaply.
- Collaboration Between FinOps and Engineering: Managing AI capacity isn’t solely an engineering task or a finance task – it’s a joint effort. Engineers must design efficient prompt usage and handle failovers, while FinOps practitioners provide visibility into costs per model, utilization rates, and when it’s time to adjust commitments. Set up tagging or a tracking system for your AI usage by service or feature, so you can attribute the costs of provisioned capacity to the teams/products using it. Close collaboration ensures that when an AI feature’s usage spikes or dips, both cost and performance implications are addressed proactively. For example, if an engineering team plans a big new generative feature launch, FinOps should be involved early to forecast the needed capacity and avoid last-minute scrambles (or worse, outages).
- Beware of Token “Exchange Rates”: Because each model may count tokens differently (e.g., 1 output token might count as 2 or 4 input tokens in various services), be careful when comparing costs or planning capacity across different AI platforms. A use case that consumes N tokens on one model might use 2× N on a more complex model for the same input. FinOps should help translate these token metrics into apples-to-apples cost per query or cost per user metrics. Don’t let the abstraction of tokens obscure real money – educate your team that tokens cost money, and optimize prompts to use fewer tokens where possible (shorter prompts, smaller output if feasible). This is analogous to optimizing data payloads in a network-based pricing model.
When to Use On-Demand vs. Provisioned Capacity
To decide which capacity mode to use for a given AI workload, consider both technical requirements and cost factors:
- Use On-Demand (Shared) when: your usage is low-volume or sporadic; you’re still prototyping or unsure of traffic patterns; latency/throughput needs are not strict; or when avoiding any upfront commitment is a priority. This is ideal for experiments, occasional batch jobs, or services that can tolerate a queue. On-demand ensures you only pay for actual usage, making it cost-efficient at smaller scale or unpredictable workloads. Just keep in mind that if many users hit the same service (for example, a sudden surge in a popular API), you might see slower responses because you’re in a “crowded restaurant.”
- Use Provisioned (Dedicated) when: you have a steady, high-volume workload or a production app where consistent low latency is crucial. Committing capacity makes sense if the feature is core to your product (e.g., a paid feature for customers) and you expect continuous traffic. Economically, if you find that your on-demand bills are growing quickly, it’s a signal to analyze if a reserved capacity would lower your effective rate. For instance, if you’re running millions of tokens daily through an LLM, check the provider’s pricing – at some point, the fixed monthly price of a dedicated unit (plus its higher throughput) may be cheaper than the equivalent per-token charges on demand. As a rough rule, if you can utilize a dedicated capacity most of the time (e.g., >50–70% utilization), it will likely be more cost-effective. Additionally, if missing or slowing down this service would severely hurt user experience or revenue, the guaranteed capacity is like an insurance policy.
- Hybrid Approach: In practice, many organizations use a mix. For example, you might reserve enough capacity to cover baseline traffic or business-hours load, and let on-demand handle any spiky overflow beyond that. This can optimize cost while still protecting the majority of users’ experience. The key is to monitor and adjust where that boundary lies over time.
By thoughtfully leveraging these two modes, companies can lower AI costs while maintaining performance. One FinOps case study showed that blending dedicated and on-demand capacity (with ~10–15% of requests overflowing to on-demand during peaks) actually reduced the overall cost per token by double-digit percentages compared to all on-demand. In other words, a bit of flexibility in non-critical traffic can yield significant savings, while critical interactions remain fast.
Getting Started with Provisioned AI Capacity
If you’ve decided to try provisioned capacity, here’s how to get started in a nutshell:
- Identify Candidate Workloads: Pinpoint the AI inference workloads in your cloud usage that would benefit from dedicated capacity (using the criteria above). Gather stats on their current token usage, QPS (queries per second), and latency sensitivity.
- Consult Provider Documentation: Each cloud provider has specific SKUs and processes for purchasing dedicated AI capacity. For example, AWS Bedrock users would look at how to purchase Provisioned Throughput (MUs) for a given model. Azure users would explore the Azure OpenAI Provisioned Throughput docs and perhaps contact Microsoft to enable it (it may require certain approvals). GCP users can use the Vertex AI API or console to set up a Provisioned Throughput subscription for the model and region they need. OCI users would navigate to the Generative AI service and configure a Dedicated AI Cluster with the desired model and number of AI units.
- Estimate Required Capacity: Work with your engineers to estimate how many units or throughput you need. Many providers give reference metrics – e.g., tokens per second per unit. AWS’s MUs specify a certain number of input and output tokens per minute they can handle. Google provides a calculator for throughput needs based on model and token lengths. If possible, run a small-scale test: use on-demand to simulate expected load and measure tokens per request and latency. This will inform how many units you should reserve. Remember to include some buffer for unexpected spikes (without going overboard).
- Purchase a Commitment: Through the provider’s console or sales team, purchase the provisioned capacity. This often involves selecting a term (1 month, 3 months, 1 year, etc.) and quantity. For instance, you might reserve X throughput units in region Y for model Z. Ensure the commitment aligns with your budget approvals (a longer term may need finance’s sign-off). The billing will typically switch to a fixed hourly or monthly fee for that capacity.
- Deploy and Test: After provisioning, direct your application to use the new dedicated endpoint or deployment. Run load tests or a pilot to verify that it’s delivering the expected performance (e.g., latency improvements, capacity headroom). Monitor for any errors – for example, hitting a limit will often throw a specific error code (like HTTP 429 Too Many Requests). Verify your failover logic works by temporarily simulating an overload and ensuring traffic spills over gracefully.
- Monitor and Optimize: Treat the first few weeks as a learning period. Check how high the utilization gets at peak and how low at trough. Calculate the effective cost per token or per request under this model and compare to prior on-demand costs. If utilization is very low, you might phase some capacity back to on-demand or consolidate use cases onto fewer units. If utilization is consistently maxed out, plan to add more units before it starts throttling users – remember that acquiring more capacity might take time, especially with popular AI models (capacity isn’t infinite). Keep an eye on any provider announcements too – model upgrades or pricing changes could affect your plan (the AI field is evolving quickly, with new models and price adjustments happening frequently).
By following these steps and best practices, you can confidently leverage provisioned AI capacity to get the best of both worlds: cost efficiency and reliable performance for your AI-driven applications.