How to choose, size, negotiate, and optimise Azure OpenAI Provisioned Throughput Units (PTUs) vs consumption-based pricing. The right answer for most enterprises in 2026 is neither pure PTU nor pure pay-as-you-go, but a deliberate hybrid that provisions PTUs for predictable baseline production workloads and retains pay-as-you-go for variable, experimental, and overflow traffic. Enterprises that optimise their PTU vs pay-as-you-go mix are achieving 25 to 40% lower total Azure OpenAI costs compared to organisations that default entirely to one model.
Azure OpenAI has become the primary GenAI deployment channel for regulated and enterprise-grade workloads. The choice between Microsoft's two pricing models, Provisioned Throughput Units (PTUs) and pay-as-you-go consumption, now carries multi-million-dollar financial implications for organisations with substantial AI workloads.
At the highest level: pay-as-you-go charges per token consumed, with no upfront commitment and no guaranteed throughput. It offers maximum flexibility but zero cost protection. Provisioned Throughput Units (PTUs) reserve dedicated model capacity at a fixed monthly or annual rate, regardless of actual consumption. PTUs guarantee consistent throughput and latency but require accurate capacity planning. Overcapacity is wasted spend, and undercapacity forces fallback to pay-as-you-go at list rates for overflow traffic.
Neither pure PTU nor pure pay-as-you-go. The optimal model is a deliberate hybrid that provisions PTUs for predictable baseline production workloads and retains pay-as-you-go for variable, experimental, and overflow traffic. On annual spend of $1 to 5M, that optimisation is worth $250K to $2M per year.
Azure OpenAI's pricing has evolved considerably from its initial launch. In 2026, the pricing architecture consists of three distinct consumption models, each serving different enterprise needs.
The default model charges per 1,000 tokens processed, with separate rates for input and output tokens. Rates vary by model: GPT-4o is priced significantly lower than the original GPT-4, reasoning models (o1, o3) carry a premium, and older models (GPT-3.5 Turbo) remain the cheapest option. There is no upfront commitment, no minimum spend, and no guaranteed throughput. You pay only for what you consume, but you are subject to quota limits (tokens per minute / requests per minute) that can throttle production applications during high-demand periods.
PTUs reserve dedicated model processing capacity for your exclusive use. Each PTU provides a defined throughput level (measured in tokens per minute) for a specific model deployment. Key characteristics: fixed monthly cost regardless of actual usage, guaranteed throughput up to the provisioned capacity (no throttling), consistent low-latency responses, and available on monthly or annual commitment terms. Annual commitments carry a significant discount (typically 30 to 50%) over monthly PTU pricing, but require accurate forward planning.
Microsoft has introduced data zone deployments that route requests across multiple regions within a geographic boundary (e.g., within the EU or within the US) for optimised availability and capacity. These deployments offer slightly lower pay-as-you-go rates than standard regional deployments. Global deployments route across all Azure regions worldwide for maximum availability at the lowest rates, but sacrifice data residency control. The deployment type affects both pricing and compliance posture.
| Pricing Model | How You Pay | Throughput Guarantee | Commitment | Best For |
|---|---|---|---|---|
| Pay-as-you-go (standard) | Per 1K tokens (input + output) | None, subject to quota limits | None | Variable, experimental, low-volume workloads |
| Pay-as-you-go (data zone) | Per 1K tokens (reduced rate) | None, subject to quota limits | None | Cost-sensitive workloads, multi-region acceptable |
| PTU (monthly) | Fixed monthly per PTU | Guaranteed up to provisioned capacity | Monthly (auto-renew) | Production workloads needing evaluation period |
| PTU (annual) | Fixed annual per PTU (30 to 50% discount) | Guaranteed up to provisioned capacity | 12-month commitment | Predictable, high-volume production workloads |
| Global deployment | Per 1K tokens (lowest rate) | None, best-effort routing | None | Non-sensitive, cost-optimised batch processing |
The central question in the PTU vs pay-as-you-go decision is utilisation: at what consumption level does the fixed PTU cost become cheaper than the equivalent pay-as-you-go charges?
For each model, calculate: monthly PTU cost divided by monthly pay-as-you-go cost at 100% PTU utilisation = break-even utilisation percentage. If your actual utilisation exceeds this percentage, PTUs are cheaper. If it falls below, pay-as-you-go is cheaper. For most models and current pricing, the break-even point for annual PTU commitments falls between 55% and 70% average utilisation. For monthly PTUs (which carry a higher per-unit cost), the break-even is typically 70 to 85%.
Across our advisory engagements, the median PTU utilisation rate for enterprise Azure OpenAI deployments is 58%, meaning a significant number of organisations are paying for capacity they do not use. The distribution is bimodal: production customer-facing applications typically achieve 70 to 90% utilisation (well above break-even), while internal productivity and batch processing workloads often run at 30 to 50% utilisation (below break-even). This pattern highlights why a blanket PTU commitment is often suboptimal.
Every percentage point of PTU utilisation below 100% represents lost value. On a $100,000/month PTU commitment at 60% utilisation, $40,000 per month ($480,000 annually) is effectively wasted. This waste is invisible in standard Azure billing because PTUs appear as a flat line item. There is no alert that says "you are only using 60% of what you are paying for." Implementing PTU utilisation monitoring is essential to avoid this silent cost drain.
Conversely, organisations running high-volume production workloads entirely on pay-as-you-go face two risks: cost unpredictability (a 3x usage spike translates to a 3x cost spike) and throttling (when Azure's shared capacity is constrained, your requests may be delayed or rejected). For customer-facing applications where latency and reliability matter, throttling is a service quality issue that can directly affect business outcomes.
| Scenario | Monthly PAYG Spend | Monthly PTU Cost (Annual) | PTU Utilisation | Monthly Savings / (Waste) | Verdict |
|---|---|---|---|---|---|
| High-volume customer bot | $85,000 | $60,000 | 82% | +$25,000 saving | PTU wins decisively |
| Internal knowledge assistant | $22,000 | $30,000 | 45% | ($8,000) waste | Pay-as-you-go wins |
| Document processing pipeline | $55,000 | $50,000 | 68% | +$5,000 saving | PTU marginal advantage |
| Developer coding assistant | $15,000 | $20,000 | 38% | ($5,000) waste | Pay-as-you-go wins |
| Agentic workflow engine | $120,000 (volatile) | $80,000 | 88% | +$40,000 saving | PTU wins decisively |
Calculate break-even for each workload independently, not a single aggregate number. Run production workloads on pay-as-you-go for at least 60 to 90 days and track tokens-per-minute utilisation at hourly granularity. This data, not projections, should drive PTU sizing decisions. Set a minimum utilisation threshold: only provision PTUs for workloads where you have high confidence of sustaining 65%+ average utilisation.
Correct PTU sizing is the difference between a cost-optimised deployment and an expensive overcommitment. The sizing exercise must account for peak-hour demand, model-specific capacity per PTU, growth projections, and the availability of overflow to pay-as-you-go.
Before sizing PTUs, profile each workload's demand pattern across three dimensions: average tokens per minute (the baseline), peak tokens per minute (the maximum sustained demand during business hours), and off-peak tokens per minute (evenings, weekends, holidays). Production customer-facing workloads typically show a 2.5 to 4x ratio between peak and off-peak. Internal productivity tools show a 3 to 6x ratio. Batch processing may show an inverted pattern (highest overnight when resources are cheapest).
The most common PTU sizing mistake is provisioning for peak demand. Since peak demand occurs for only a fraction of the day, provisioning PTUs to cover it means paying for idle capacity during all other hours. Provision PTUs to cover approximately 70% of average business-hours demand and allow the remaining 30% to spill over to pay-as-you-go. For workloads with very spiky demand (e.g., agentic workflows that trigger in bursts), the PTU allocation should be even more conservative, perhaps 50 to 60% of average, with a larger pay-as-you-go buffer.
Each model type delivers different throughput per PTU. A PTU allocated to GPT-4o delivers substantially more tokens per minute than the same PTU allocated to the o1 reasoning model (which requires more compute per token). PTU sizing must be model-specific. You cannot simply add up total token demand across all models and buy PTUs generically. Each model deployment requires its own PTU calculation.
AI usage in most enterprises is growing 15 to 30% quarterly. PTU commitments should include a plan for scaling up (can you add PTUs mid-term?) and reallocation (if a workload migrates from GPT-4 to GPT-4o, can you reassign PTUs between models?). Negotiate these operational flexibilities into your Azure agreement. They are not always available by default.
| Sizing Factor | Recommendation | Common Mistake | Impact of Mistake |
|---|---|---|---|
| Base PTU allocation | ~70% of average business-hours demand | Sizing for peak demand | 30 to 50% wasted capacity during off-peak |
| Overflow strategy | Pay-as-you-go for demand above PTU | No overflow plan; PTU must cover 100% | Either over-provisioned or users throttled |
| Model specificity | Separate PTU calculation per model | Single aggregate across models | Wrong model allocated; throughput mismatch |
| Measurement period | 60 to 90 days of production data | Sizing from projected estimates | Commitments based on assumptions, not evidence |
| Growth buffer | 10 to 15% headroom for quarterly growth | No growth consideration | PTU undersized within 3 to 6 months |
The optimal cost structure for most enterprises in 2026 is a hybrid that combines PTUs for baseline production demand with pay-as-you-go for everything else.
Tier 1: PTU (annual commitment) for high-volume, predictable, customer-facing production workloads where throughput guarantees and latency consistency are essential. These workloads justify the commitment because they run at high utilisation during business hours and directly affect customer experience. Tier 2: Pay-as-you-go (standard or data zone) for internal productivity tools, moderate-volume batch processing, development and testing environments, and any workload with variable or unpredictable demand. Tier 3: Pay-as-you-go (global deployment) for non-sensitive batch processing, model evaluation, and workloads where cost minimisation trumps data residency and latency requirements.
Architect your application layer to automatically route requests to PTU capacity first and fall back to pay-as-you-go when PTU capacity is fully utilised. This ensures that every PTU token-per-minute is consumed before any pay-as-you-go charges are incurred, maximising the return on your PTU investment. Azure's built-in routing capabilities (or a custom API gateway) can handle this automatically.
If your business has seasonal demand patterns (e.g., retail companies with holiday peaks, financial services with quarter-end processing spikes), consider PTU commitments that align with your low season and use pay-as-you-go to absorb seasonal surges. Negotiate with Microsoft for the ability to add temporary PTUs for peak periods without long-term commitment. Some enterprises have secured 30 to 90 day PTU add-ons for known seasonal events.
| Workload Category | Recommended Tier | Rationale | Expected Utilisation |
|---|---|---|---|
| Customer-facing chatbot / copilot | Tier 1: PTU (annual) | High volume, latency-sensitive, business-critical | 75 to 90% |
| Internal document processing | Tier 1 or Tier 2 | Depends on volume consistency; evaluate on data | 55 to 75% |
| Employee productivity assistant | Tier 2: PAYG (standard) | Variable demand; concentrated in business hours | 30 to 50% |
| Agentic workflows (burst) | Tier 2: PAYG + PTU overflow | High per-task cost but unpredictable timing | Variable |
| Development and testing | Tier 2: PAYG (data zone) | Cost-sensitive; no throughput guarantee needed | 20 to 40% |
| Batch data enrichment | Tier 3: PAYG (global) | Non-sensitive; lowest cost priority; runs overnight | N/A (batch) |
Implement automatic PTU-first routing. Configure your API gateway to route all eligible requests to PTU deployments first, with automatic fallback to pay-as-you-go when PTU capacity is saturated. This is the single most impactful optimisation for PTU economics. Classify every workload into Tier 1/2/3. Create a workload register with each application's demand profile, data sensitivity, latency requirement, and recommended pricing tier. Update quarterly as workloads mature.
Both PTU and pay-as-you-go pricing are negotiable within the context of your Microsoft relationship. Your commitment to Azure consumption is leverage, and Microsoft's GenAI team is motivated to win your AI workloads.
Annual PTU pricing is typically listed at a 30 to 50% discount over monthly PTU pricing. However, this listed annual rate is itself negotiable. Enterprises committing to 50+ PTUs annually should expect to negotiate an additional 10 to 20% beyond the published annual discount. Leverage points include total Azure spend (existing MACC commitment), multi-year commitment (2 to 3 year PTU agreements), strategic value (reference customer, co-development, case study participation), and competitive alternatives (OpenAI direct pricing comparison, Anthropic/Google quotes). A well-negotiated annual PTU deal can achieve 45 to 60% below the monthly PTU list rate.
Pay-as-you-go rates are harder to negotiate individually, but Microsoft has flexibility through several mechanisms: volume-based tiered pricing (lower rate per token above a monthly threshold), MACC credit inclusion (Azure OpenAI consumption counts toward your committed Azure spend), Azure credits or consumption incentives (promotional credits for new AI workloads), and guaranteed rate locks (fixed pay-as-you-go rates for 12 to 24 months, protecting against price increases). Even if the per-token rate remains at list price, MACC inclusion alone can reduce the effective cost to zero incremental dollars for organisations with unused Azure commitment capacity.
The most valuable negotiation outcomes often involve flexibility rather than price. Key flexibility terms to pursue: the ability to reallocate PTUs between model deployments (e.g., move capacity from GPT-4 to GPT-4o), the right to scale up PTU commitments mid-term at the same negotiated rate, a 90-day evaluation period for new PTU commitments before the annual lock-in begins, rollover of unused MACC or committed spend credit to the next period, and the ability to add temporary PTUs (30 to 90 days) for seasonal peaks without annual commitment.
Microsoft's fiscal year ends June 30. The highest discount authority and deal flexibility occur in Q4 (April to June), when account teams are motivated to close commitments against annual targets. EA renewals provide another high-leverage moment. Bundling Azure OpenAI into your EA renewal gives Microsoft incentive to offer concessions across the entire relationship.
| Negotiation Lever | Applies To | Expected Outcome | Difficulty |
|---|---|---|---|
| Annual vs monthly PTU discount | PTU | 30 to 50% below monthly rate (standard) | Low, published benefit |
| Additional volume discount on annual PTU | PTU | +10 to 20% beyond published annual rate | Medium, requires 50+ PTUs |
| Multi-year PTU commitment discount | PTU | +5 to 15% for 2 to 3 year term | Medium, lock-in risk for buyer |
| MACC credit inclusion | Both | $0 incremental if MACC capacity available | Low, standard for EA customers |
| Rate lock for pay-as-you-go | PAYG | Fixed rates for 12 to 24 months | Medium |
| PTU model reallocation flexibility | PTU | Move PTUs between model deployments | Medium to High |
| Seasonal PTU add-ons (30 to 90 day) | PTU | Temporary capacity without annual lock-in | High, requires escalation |
| EA renewal bundling | Both | Best overall terms when combined with EA | Low, Microsoft incentivised |
Time your negotiation to Microsoft's fiscal calendar. Aim to negotiate during Q4 (April to June) or align with your EA renewal for maximum leverage. Present a competitive comparison: obtain written quotes from OpenAI direct and at least one alternative (Anthropic Claude via AWS Bedrock, Google Gemini via GCP). Negotiate flexibility first, price second. The ability to reallocate PTUs, scale up at locked rates, and add seasonal capacity can save more money over a 3-year term than an incremental per-token discount.
For enterprises with existing Microsoft Azure Consumption Commitments (MACCs), the integration of Azure OpenAI with MACC is often the single most important factor in the pricing model decision.
MACC is a commitment to consume a specified dollar amount of Azure services over a defined period (typically 1 to 3 years). Azure OpenAI consumption, both pay-as-you-go and PTU, is eligible to count toward MACC spend. Every dollar spent on Azure OpenAI reduces your remaining MACC obligation. If your organisation has committed to a $10M annual MACC and is currently consuming $8M across other Azure services, $2M of Azure OpenAI consumption would be absorbed within the existing commitment at no incremental cost.
If your MACC has headroom (committed spend exceeding current consumption), Azure OpenAI is effectively free up to that headroom. This fundamentally changes the PTU vs pay-as-you-go calculus: if both models count equally toward MACC, the financial comparison shifts to which model provides better operational value (throughput guarantees, latency consistency) rather than which is cheaper in raw dollars. In this scenario, PTUs become attractive even at lower utilisation rates because the wasted capacity costs nothing incremental.
Not all Azure OpenAI consumption models count toward MACC equally in all agreement structures. Verify the following: that both pay-as-you-go and PTU consumption count toward MACC at 1:1 face value (not at a reduced credit rate), that all model types (GPT-4, o1, GPT-4o, etc.) are MACC-eligible, that data zone and global deployments are MACC-eligible, and that fine-tuning training compute is MACC-eligible. Any gap in MACC eligibility changes the economics and should be negotiated into your agreement.
| MACC Scenario | MACC Size | Current Azure Consumption | Headroom | Azure OpenAI Impact |
|---|---|---|---|---|
| Large headroom, AI is free | $10M | $7M | $3M | Up to $3M of Azure OpenAI at $0 incremental |
| Moderate headroom, partially free | $10M | $9M | $1M | First $1M free; above that is incremental cost |
| No headroom, all incremental | $10M | $10.5M | $0 | All Azure OpenAI is incremental cost |
| MACC increase to absorb AI | $12M (increased) | $10M | $2M | Negotiate MACC increase to cover planned AI spend |
PTU commitments create financial lock-in that must be managed through contractual protections. Unlike pay-as-you-go (which can be reduced or stopped at any time), an annual PTU commitment is a fixed financial obligation regardless of actual consumption or changes in business requirements.
A 12-month PTU commitment cannot be cancelled or reduced mid-term under standard Azure terms. If your workload decreases, migrates to a different model, or is decommissioned, you continue paying for the provisioned capacity. For a $1M annual PTU commitment, this represents $1M of exposure to business change, material enough to require the same risk assessment you would apply to any seven-figure technology contract.
OpenAI regularly deprecates and replaces models. If you have PTUs provisioned for GPT-4 and Microsoft announces GPT-4's deprecation, what happens to your commitment? Under standard terms, the PTU commitment remains, but the underlying model may be replaced, potentially with a model that has different throughput-per-PTU characteristics. Negotiate explicit protections: if a model is deprecated during your PTU term, you should be entitled to equivalent capacity on the successor model at no additional cost, or the ability to terminate the affected PTU commitment without penalty.
Pursue these contractual protections for any PTU commitment exceeding $250K annually: a 60 to 90 day initial evaluation period before the annual commitment lock-in takes effect, quarterly reallocation rights to move PTUs between model deployments, annual scale-down rights of 15 to 25% at the commitment anniversary, model deprecation protection as described above, and a co-termination clause aligning PTU commitments with your EA renewal date.
| Lock-In Risk | Impact | Mitigation | Contract Clause Required |
|---|---|---|---|
| Annual commitment, workload decreases | Pay for idle capacity for remainder of term | Size conservatively (70% of demand) | 15 to 25% annual scale-down right |
| Model deprecation mid-term | PTU may lose value if model retired | Monitor OpenAI model roadmap | Successor model equivalence guarantee |
| Better pricing becomes available | Locked at negotiated rate | Include most-favoured-customer clause | MFC clause (difficult to secure) |
| Switching to different provider | PTU cost continues while migrating | Maintain PAYG for portable workloads | Early termination for convenience (with penalty) |
The pricing model decision is not a one-time event. It requires continuous governance to maintain optimisation as workloads evolve, usage patterns shift, and pricing changes.
For PTU deployments, track utilisation (tokens per minute consumed vs provisioned capacity) at hourly granularity, and alert when average daily utilisation falls below 60% or exceeds 90%. For pay-as-you-go deployments, track daily and monthly spend by model, application, and cost centre, with alerts at 70%, 85%, and 95% of monthly budget. Azure Cost Management provides the native tooling, supplemented by custom dashboards for GenAI-specific metrics.
Conduct monthly reviews examining PTU utilisation by deployment, pay-as-you-go spend by workload, overflow traffic from PTU to pay-as-you-go (if this exceeds 20% of total traffic, consider adding PTUs), model tier optimisation (are workloads running on expensive models that could be served by cheaper alternatives?), and inactive or underutilised ChatGPT Enterprise seats. Assign a designated FinOps owner for GenAI costs. Without accountability, optimisation does not happen.
Every quarter, evaluate whether the PTU vs pay-as-you-go allocation remains optimal based on actual data from the preceding 90 days. Rebalance by adding PTUs for workloads that have grown above the break-even utilisation threshold, reducing or not renewing PTUs for workloads that have fallen below break-even, migrating workloads between model tiers as new cheaper models become available, and adjusting overflow routing to minimise cost while maintaining performance.
| Governance Activity | Frequency | Owner | Key Metric | Action Trigger |
|---|---|---|---|---|
| PTU utilisation monitoring | Daily (automated) | IT / FinOps | Average daily utilisation % | Below 60%: investigate; below 50%: escalate |
| PAYG budget tracking | Daily (automated) | Finance / FinOps | Monthly spend vs budget | Above 85%: review; above 95%: escalate |
| Model tier optimisation | Monthly | IT / Data Science | % spend on premium vs standard | Above 60% on premium: test cheaper alternatives |
| PTU vs PAYG rebalancing | Quarterly | Procurement / IT | Break-even vs actual utilisation | Workloads crossing break-even in either direction |
| Vendor pricing review | Quarterly | Procurement | Rates vs market / competitors | Significant price decline: renegotiate or shift |
Yes. You can add PTUs at any time for workloads that have demonstrated sustained high utilisation. Reducing or eliminating PTUs is constrained by your commitment term. Annual PTUs cannot be cancelled mid-term without negotiating an early termination provision. The recommended approach is to start with pay-as-you-go, measure actual utilisation for 60 to 90 days, and then migrate workloads to PTUs only where the break-even analysis is clearly favourable.
You pay the full PTU cost regardless of utilisation. Unused capacity is wasted spend. The median PTU utilisation across our advisory clients is 58%, meaning many organisations are paying for significant idle capacity. Mitigate this by sizing PTUs to approximately 70% of average demand (not peak demand), implementing PTU-first routing so all available capacity is consumed before pay-as-you-go charges, and monitoring utilisation daily with alerts when utilisation falls below 60%.
If your MACC has headroom (committed spend exceeding current non-AI Azure consumption), both PTU and pay-as-you-go Azure OpenAI costs are absorbed within the existing commitment at zero incremental cost. In this scenario, the decision shifts from "which is cheaper" to "which provides better operational value," and PTUs win for production workloads because they guarantee throughput and consistent latency at no additional financial cost.
Directly discounting per-token pay-as-you-go rates is possible but requires substantial volume and Microsoft relationship leverage. More commonly, enterprises achieve effective discounts through MACC inclusion (consumption counts toward committed spend), volume-based tiered pricing at negotiated thresholds, rate locks that protect against future increases, and promotional Azure credits for new AI workloads. Combined, these mechanisms can reduce effective pay-as-you-go costs by 15 to 30%.
PTUs are model-specific. Each PTU provides a defined throughput for the model it is assigned to. A PTU allocated to GPT-4o delivers different token-per-minute throughput than the same PTU allocated to o1. You must calculate PTU requirements separately for each model deployment and cannot simply pool PTU capacity across models. Negotiate reallocation rights so you can move PTU capacity between models as your workload mix evolves.
Under standard terms, model deprecation does not automatically terminate your PTU commitment. Negotiate explicit protections: if a model is deprecated during your PTU term, you should receive equivalent throughput on the successor model at no additional cost, or the right to terminate the affected PTU commitment without penalty. This is a critical contract clause that many enterprises overlook.
No. Bundle them together for maximum leverage. Your existing Microsoft relationship (Azure spend, M365, Dynamics, etc.) provides context and leverage that makes PTU negotiation more effective. Time the discussion to coincide with your EA renewal if possible. Microsoft's account teams have maximum flexibility during EA renewals and fiscal Q4 (April to June).
Implement three layers of cost control: budget alerts at 70%, 85%, and 95% of monthly targets (via Azure Cost Management), hard spending limits where Azure supports them for specific subscription types, and application-level rate limiting that caps the tokens per minute your applications can consume. Without these controls, a viral internal adoption or a coding error can generate five-figure surprise charges within days.
For annual PTU commitments at currently published pricing, the break-even utilisation rate is typically 55 to 70% depending on the model. For monthly PTUs (which carry a higher per-unit cost), break-even is approximately 70 to 85%. These thresholds assume standard pay-as-you-go rates as the alternative. If you have negotiated PAYG discounts or MACC offsets, the break-even calculation changes accordingly.
Quarterly at minimum. AI usage patterns evolve rapidly. Workloads that justified PTUs three months ago may have shifted to different models or reduced in volume. The quarterly review should examine actual PTU utilisation against break-even thresholds, pay-as-you-go overflow volumes, new workloads that may benefit from PTUs, and changes in Microsoft's pricing that affect the economics. Assign a designated FinOps owner for GenAI costs.
Redress Compliance provides independent advisory for enterprises evaluating PTU vs pay-as-you-go decisions, negotiating Azure OpenAI pricing within their Microsoft EA, or implementing GenAI FinOps governance. Current PTU benchmarking data, Microsoft negotiation expertise, and cost optimisation frameworks proven across multiple enterprise Azure OpenAI deployments. Complete vendor independence. No Microsoft partnerships, no resale commissions.
GenAI Negotiation ServicesIndependent GenAI advisory helping enterprises optimise Azure OpenAI PTU vs pay-as-you-go pricing, negotiate Microsoft terms, and implement FinOps governance. Fixed-fee engagement models.