GPT Pricing Breakdown: OpenAI vs Azure vs AWS vs GCP

The era of picking an AI model is no longer just about raw performance — it’s about pricing, platform fit, governance, and cost per output value.

You’ve got options:

• OpenAI’s native API (cheapest, fastest to ship)
• Azure’s GPT integration (great for Microsoft-first orgs)
• AWS Bedrock (tightest security and IAM control)
• Google Vertex AI (clean pipelines and AutoML integration)

But with every platform shouting “enterprise ready”, where do you actually start? Should you call the OpenAI API directly? Should you run OSS models on AWS Bedrock or GCP Vertex AI? What does it really cost? What about caching, governance, and hidden infra? This blog gives you a use case, a deep cost simulation, and a platform-by-platform breakdown all wrapped in practical, punchy insights.

⚡ TL;DR — What You’ll Learn

• GPT-5 pricing comparison across OpenAI, Azure, AWS Bedrock, and GCP Vertex AI
• When to use what — based on latency, access model, security, and scale
• Real-world simulation of 50K monthly queries using 20M+ tokens
• How OSS GPT models (like GPT‑OSS‑20B) work inside AWS and GCP infra
• Cost-saving tactics for input token optimization and caching
• Key governance tips to avoid budget blowouts

🧠 What’s GPT‑OSS and Why It Matters

OpenAI recently released two open-weight Mixture of Experts (MoE) models under Apache 2.0:

• gpt-oss-20b: Lightweight, good for classification and utility tasks
• gpt-oss-120b: Performs surprisingly well for reasoning and code

These are not the same as GPT‑5, but can be run on your own infra via AWS EC2, GCP Vertex, or even local GPUs. For many dev and enterprise use cases, OSS is the closest you’ll get to GPT‑5 in a self-hosted form.

When to Use What

💰 Real-World Pricing Simulation

Let’s assume your app handles 50,000 queries/month, and each one processes:

• 400 input tokens
• 150 output tokens
• 20M input tokens (16M regular, 4M cached)
• 7.5M output tokens

OpenAI and Azure apply a 90% caching discount on repeated inputs. OSS models don’t. So here’s how the monthly cost shakes out:

 EC2/Infra Notes:

AWS OSS (Bedrock or SageMaker):

• GPU hosting (EC2): Approx. $2.50–$3.50/hr (e.g. A10G, A100, Inf2)
• SageMaker (serverless): ~$1.01/hr for basic deployment like g5.xlarge
• Caching: Not supported natively
• Note: AWS Bedrock lets you skip EC2 for some OSS variants, but advanced use needs hosting.

GCP OSS (Vertex AI):

• Provisioned Deployments: $0.001–$0.005/sec depending on machine type
• On-Demand (Lite Use): Per-token only, no infra needed
• $300 credits available for new GCP users
• Caching: Not available natively
• Usage: Serverless interface simplifies smaller workloads, but for high concurrency, provisioned instances perform better.

OSS totals exclude potential 50% batch discounts; caching not supported natively for open-weight models on AWS/GCP.

⚠️ Caveat: Running GPT OSS on AWS or GCP

If you choose to run Open Weight (OSS) GPT models on your own infrastructure (like gpt-oss-20b or Meta LLaMA 3), costs look very different. AWS and GCP recently made this easier:

AWS: OSS models now on Bedrock

GCP: GPT OSS on GKE

Let’s model the same workload using gpt-oss-20b self-hosted on EC2 with ml.p4d.24xlarge (A100 GPU).

⚡️These costs do not include storage, egress, Kubernetes overhead, or token usage. OSS models don’t charge per token — but you must fully host and maintain them.

So unless you have massive scale, custom model needs, or regulatory controls, OSS self-hosting may not be cost-effective for chat-style apps.

🧠 What Model Are We Talking About?

• Proprietary (OpenAI & Azure): Using GPT-5 Flagship, the top-tier model with $1.25/M input and $10/M output pricing.
• OSS (AWS & GCP): Based on gpt-oss-120b, the highest-reasoning open model available today, performing on par with GPT-4-turbo for many tasks.

🔧 When to Use What (Real-World Scenarios)

Tip: If you’re caching input or batching queries, OSS can be 80–90% cheaper than GPT-5.

📜 Governance and Control

If you’re running workloads in a regulated industry or need multi-user org-level controls, OpenAI’s API will fall short. Choose Azure, AWS, or GCP.

🧮 Cost Optimization Nuggets

1. Trim Context: Long chat histories add hidden input tokens. Keep them lean.
2. Exploit Caching: OpenAI and Azure cut cached inputs by 90%. For repeated prompts (e.g., product search queries), that’s major.
3. Model Tiers / Fallback Chains: Start with GPT‑5 Nano or Mini. Only fallback to full GPT‑5 when needed.
4. Async + Batch for Non-Chat Use: Use background jobs for summaries, reports, etc. Don’t pay latency premiums.
5. Monitor Input Spike: Rogue system messages or loops can eat millions of tokens. Set alerts.
6. Use Role-Based Access: Limit access to flagship models. Set budgets. Separate dev/test workloads.
7. Track Vendor Lock-In: Avoid building everything on a single model/version. Stay modular.
8. Batching helps OSS: No native caching, but OSS benefits from batching often yielding ~50% lower costs per token.
9. Infra choice = control: OSS gives you model control and pricing leverage (EC2/SageMaker, or Vertex).
10. Governance favors Azure: For enterprises bound by compliance zones or needing finer IAM control, Azure is a solid default.
11. Latency tradeoff: OSS often has higher query latency (~200–500ms), while OpenAI typically hits <100ms.

⚠️ Common Pitfalls

• Token shock: Output is cheap, but long prompts with unnecessary tokens burn your budget.
• Latency blind spots: Cheaper models (OSS/GPT‑Mini) may not support streaming or realtime.
• Model version surprises: Platforms silently replace GPT-4o with GPT-5 or vice versa.
• Caching misunderstandings: Many users assume more is cached than actually is.
• Overpaying for prod + dev: Mix API + ChatGPT plans for smarter savings.

🔬 Use Case Snapshots

Here’s where each model setup shines:

• GPT-5 (OpenAI)

Great for customer-facing tools like chatbots, email assistants, or coding copilots. Fast, accurate, scalable.

• GPT-5 (Azure)

Same model, with compliance benefits. Easy integration into enterprise tools like Power Platform or Teams.

• GPT-OSS (AWS)

Best for internal microservices or agents. EC2 and SageMaker offer full control — and can help meet region-specific compliance too.

• GPT-OSS (GCP)

Cheapest route for startups. Vertex AI lets you pay-per-second for gpt-oss-120b or 20b. Minimal setup, zero lock-in.

🧠 Final Thoughts

OpenAI’s GPT-5 is fast, premium, and best for mission-critical UX. But for devs, startups, and custom stacks, gpt-oss-120b is a legit contender — especially when paired with AWS or GCP infra.

We’re now seeing a shift where the true cost of “AI at scale” isn’t just tokens — it’s infra, caching, and architecture choices. Make sure you simulate for your specific use case before choosing a provider.