Hubspot Pricing Plans for Singapore [2025]
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If your team in Singapore is testing or scaling generative AI, chances are Azure OpenAI is on your shortlist. This guide breaks down how Azure OpenAI pricing works today, the currencies you’ll actually be billed in, what to expect from add-ons, and how to keep next month’s bill under control. We’ll also compare Azure OpenAI to its main API competitor on prices so you can choose with confidence.
Pricing is only half the story — payment is the other. If you run subscriptions across multiple currencies, the Wise Business multi-currency account lets you pay globally with a physical or digital Wise Business debit card, always at the mid-market rate with low conversion fees.
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Azure presents three ways to pay: Standard (PAYG), Provisioned throughput (PTUs), and the Batch API¹. There are no monthly or annual fees. Standard is pure usage-based pricing: you deploy a model and pay for tokens consumed. For current model families, Azure OpenAI pricing rates are per 1m tokens for inputs and outputs; when supported, cached input tokens are also billed per 1m tokens at a reduced rate. The exact unit rate depends on the model and deployment type (Global, Data Zone, or Regional).
Provisioned throughput gives you reserved capacity measured in provisioned throughput units and billed per PTU-hour. You size the PTUs you need, bind them to a deployment, and pay hourly whether you fully consume the capacity or not. If your traffic is steady, committing to capacity can lower your effective price compared with pure on-demand.
The Batch API targets large, non-urgent language jobs. You submit work and receive results in ~24 hours. The key draw is cost: Batch charges per 1m tokens for supported language models at 50% of global standard rates, so it’s useful for daily backfills, log analysis, or bulk summarization where latency isn’t critical.
| Option | What you pay for | Billing cadence |
|---|---|---|
| Standard (PAYG) | Input/output tokens and cached input where supported | Per 1m tokens, charged as you generate/consume |
| Provisioned throughput (PTUs) | Reserved capacity measured in PTUs | Per PTU-hour; metered hourly per region |
| Batch API | Input/output tokens for asynchronous language jobs | Per 1m tokens at 50% of Global Standard; target ~24-hour completion; separate batch quota |
Details accurate as of 27th October 2025
At the time of writing, the freshly released GPT-5 family isn’t listed as available in the Southeast Asia (SEA) region — which includes Singapore² — on the official Azure OpenAI pricing page.
For standard usage, GPT-4.1 is shown at 2 USD per 1m input tokens, 0.50 USD per 1m cached input, and 8 USD per 1m output.
GPT-4.1-mini is 0.40 USD per 1m input, 0.10 USD cached input, and 1.60 USD per 1m output. If you switch to the Batch API, language-model token rates are about 50% of the global standard, so GPT-4.1 becomes roughly 1 USD input/4 USD output per 1m. Azure hasn’t listed Data Zone or Regional SKUs for these models in SEA yet.
In Singapore, rates are listed in USD, and Microsoft issues invoices in USD³.
Public pages may show other currencies for guidance, but your bill follows USD. If you pay using a non-USD card/account, your payment provider applies the exchange rate and any fees. In that case, your payment provider sets the SGD to USD rate and may add fees. For budgeting, agree on a one-month-end ledger rate so finance and engineering plan against the same USD to SGD assumptions.
To minimise exchange rate costs, fund and pay in the invoice currency. With the Wise Business account, you can hold USD and SGD, convert at the mid-market rate with low fees, and settle invoices using the Wise Business debit card. The Wise card has no foreign transaction fees, and Wise never adds any hidden exchange rate markups when converting currencies, so you can save more on your foreign currency SaaS spend.
PAYG is usage-only pricing: deploy a model and pay per 1m input/output tokens. For big non-urgent jobs, switch those calls to Batch to cut unit cost.
Access to Azure’s model catalogue, subject to enablement and region, streaming, tool/function calling, image and audio, where available, content filters, Azure RBAC, and private networking options.
Choose PAYG when you’re prototyping, running spiky or seasonal workloads, or optimizing for time-to-value without commitments. It lets you scale down to zero between experiments. If usage becomes steady and latency matters, PTU often lands a lower effective rate than staying fully on-demand.
Provisioned throughput reserves capacity for a deployment using PTUs and billed per PTU-hour in the chosen region⁴. You size the PTUs you need, bind models to that capacity, and pay hourly whether you use every token or not. Because capacity is reserved, you get consistent throughput and more stable latency. Reserve capacity to pay less per hour when your traffic is steady. Scale up PTUs or add deployments for peaks, then scale back down when demand settles.
Predictable capacity, consistent speed, and enterprise controls (RBAC, VNET, logging). PTUs are built to absorb traffic spikes without throttling.
Choose PTUs for customer-facing apps, regulated workloads that need steady performance, or teams with predictable token volumes. If your usage graph is a flat line rather than a skyline, PTUs usually model better over a 3–6 month horizon than staying on pure PAYG.
Batch handles large, asynchronous language jobs. You submit work and get results in ~24 hours. Pricing is per 1m tokens at 50% of the global standard for supported language models, so you trade latency for lower unit cost⁵. Great for overnight runs where no one’s waiting: bulk summaries, backfills, log analysis, dataset labeling. Keep real-time flows on PAYG or PTU, and move heavy internal jobs to Batch to lower your average cost.
Job submission and status tracking, scaled processing without manual sharding, and compatibility with common language workloads — model availability varies by region.
Use Batch when time isn’t critical and cost per output matters most. Many teams in Singapore run daytime interactive calls on PAYG/PTU and push high-volume internal tasks to Batch overnight, improving cost predictability without touching end-user latency.
Azure OpenAI pricing is by tokens (Standard/Batch) or PTU hours (Provisioned). Common add-ons are billed separately and should be in your TCO model.
Azure AI Content Safety: a free tier includes 5,000 text records and 5,000 images each month; beyond that, usage is billed per 1,000 items on the Standard tier.
Azure AI Search (for RAG): priced by SKU and search units (replicas × partitions). There’s a free sandbox; production SKUs are fixed hourly, and you scale throughput by adjusting replicas/partitions.
Fine-tuning: rates are model-specific; Microsoft directs you to the Azure OpenAI pricing page for current per-token figures, and fine-tuned deployments incur an hourly hosting charge when deployed.
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OpenAI’s own API is the main competitor in Singapore because it offers the same core models with a simple, usage-only pricing model and fast onboarding — no Azure tenant setup. It’s a developer-first service from OpenAI with mature docs, SDKs, and wide ecosystem support, which makes it popular for startups and product teams aiming to ship quickly. Azure OpenAI suits organisations already invested in Azure that need reserved capacity, regional options, and enterprise governance alongside model access.
| Aspect | Azure OpenAI | OpenAI API (direct) |
|---|---|---|
| Unit pricing | Per 1m tokens by model, Batch API −50% (language), PTU billed hourly | Per 1m tokens by model (usage-only) |
| Commit options | PTU reservations (monthly/annual) to lower effective rate | No PTU; volume discounts may be available via sales |
| Capacity | Reserved capacity with PTUs for consistent speed | Elastic shared capacity; no reservations |
| Regions & data | Global, Data Zone (EU/US), and Regional deployments; Azure networking/RBAC/SLAs | Global API; organisation controls; enterprise SLAs via sales |
| Add-ons | Azure AI Search, Content Safety, others billed separately | Separate APIs and partner tools billed separately |
Details accurate as of 27th October 2025
For Singapore businesses looking to save on their Azure OpenAI bill every month, they should focus on three levers:
Also, remember that Azure OpenAI pricing is in USD, and invoices for Singapore should also be issued in USD; any other currencies you see are indicative. Month-end exchange rate assumptions still matter for budgeting and reporting.
If you want to reduce your company’s exposure to exchange-rate uncertainty, try out Wise Business. You can hold USD and SGD, convert at the mid-market rate with low conversion fees, and pay your foreign currency SaaS bills with the Wise Business Card without worrying about any foreign transaction fees.


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There isn’t a standing free plan for Azure OpenAI. New Azure accounts may get general Azure credits, but Azure OpenAI access is gated, and Azure meters your usage as soon as you enable access.
You use Azure AI Studio in the browser — via the Azure portal — to create deployments, test prompts, and manage keys, then integrate your product via REST or SDKs.
Yes. Endpoints cover chat/completions, embeddings, and other modalities where supported. Azure OpenAI pricing follows per-1m tokens on Standard/Batch or per PTU-hour on Provisioned.
Microsoft lists usage (tokens) and capacity (PTU hours). There’s no mandatory onboarding fee. Enterprises often add a support plan or partner services — budget those separately.
Under the Microsoft Customer Agreement, invoices for Singapore are issued in USD. Public pages may display other currencies for guidance, but your bill follows USD.
Sources
Sources checked on 27th October 2025
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