TL;DR — Token pricing wins at variable workloads (pay only what you use). Flat-rate / provisioned wins when you process a predictable high volume daily. Break-even formula: flat_monthly_cost ÷ token_rate = tokens_needed/month. Below that threshold, pay-as-you-go is cheaper every time.
Key Takeaways
- Token pricing: pay per use, zero waste — best for variable or early-stage workloads
- Flat-rate: predictable monthly cost — best for high-volume, consistent usage patterns
- Break-even formula: flat_monthly_cost ÷ token_rate = tokens/month needed to justify flat rate
- Worldwide AI infrastructure spending hit $154B in 2024, with inference costs outpacing training for the first time (IDC, 2024)
According to IDC (2024), worldwide spending on AI infrastructure reached $154 billion in 2024, with inference costs outpacing training costs for the first time. Most LLM APIs charge per token, meaning you pay for exactly what you use. But a growing number of providers offer flat-rate or provisioned pricing: one monthly fee for a fixed amount of capacity, regardless of how many tokens you process.
The answer to "which saves money" isn't universal. It depends on your usage volume, how predictable that volume is, and how much you value cost certainty over cost efficiency. This article compares both models with real math so you can pick the one that fits your workload.
How does token-based pricing work?
Token pricing is the default for OpenAI, Anthropic, DeepSeek, Mistral, and most LLM providers. You pay per million tokens processed, with separate rates for input and output:
Cost = (input_tokens × input_rate) + (output_tokens × output_rate)
Current rates for popular models (June 2026):
| Model | Input (per 1M) | Output (per 1M) |
|---|---|---|
| GPT-4o | $2.50 | $10.00 |
| GPT-4o-mini | $0.15 | $0.60 |
| Claude Sonnet 4 | $3.00 | $15.00 |
| DeepSeek V3 | $0.27 | $1.10 |
For the complete pricing table, see our LLM API pricing guide.
Advantages:
- Pay only for what you use — zero waste
- Scale down without penalty
- Switch models or providers instantly
- No minimum commitment
Disadvantages:
- Costs are unpredictable if usage varies
- No volume discount (at standard tiers)
- A bug can burn through your budget in hours
- Hard to budget when you don't know next month's volume
How does flat-rate pricing work?
Flat-rate pricing takes several forms in the LLM market:
Provisioned throughput (AWS Bedrock, Google Vertex AI)
You buy a fixed amount of processing capacity at a monthly rate. AWS Bedrock pricing documentation (2026) details provisioned throughput options that guarantee tokens-per-second capacity. You get guaranteed throughput (tokens per second) regardless of demand, and you don't pay per token.
Example (AWS Bedrock Provisioned Throughput):
- Claude Sonnet via Bedrock: ~$30-50/hour for a provisioned model unit
- That's ~$21,600-$36,000/month for dedicated capacity
- Process unlimited tokens within that capacity
When this makes sense: High-volume, latency-sensitive workloads. If you're processing 500M+ tokens/month on a single model, provisioned throughput can be 30-50% cheaper than per-token pricing — and you get guaranteed latency with no rate limiting.
Subscription APIs (emerging model)
Some newer providers and aggregators offer monthly subscriptions:
- Fixed monthly fee for a set number of requests or tokens
- Overages charged at per-token rates
- Simpler budgeting but potentially wasteful if you don't use your allocation
Self-hosted inference (the "ultimate flat rate")
Running open-source models (Llama 3.3, Mistral, Gemma 2) on your own GPU infrastructure is effectively flat-rate pricing: you pay for the server, not the tokens.
- GPU server cost: $2-8/hour for an A100 or H100 instance
- Monthly cost: $1,500-$6,000 for a single GPU server
- Tokens processed: Unlimited (bounded only by throughput)
Break-even vs API pricing (GPT-4o-mini equivalent):
- Llama 3.3 70B on a single A100: ~$3,000/month
- Equivalent API cost at $0.15/1M input: break-even at ~20 billion tokens/month
- That's roughly 700,000 requests/day with 1,000-token prompts
Most teams don't process enough tokens to justify self-hosting. But for teams running millions of daily requests with consistent patterns, the math works.
Side-by-side cost comparison
Let's compare token pricing vs provisioned throughput at different usage levels:
Low volume: 1M tokens/day (30M/month)
| Pricing model | Monthly cost |
|---|---|
| GPT-4o per-token | ~$125 |
| GPT-4o-mini per-token | ~$7 |
| Provisioned throughput (Bedrock) | ~$22,000+ |
Winner: Per-token. At low volume, flat-rate/provisioned pricing is wildly more expensive. You're paying for capacity you don't use.
Medium volume: 50M tokens/day (1.5B/month)
| Pricing model | Monthly cost |
|---|---|
| GPT-4o per-token | ~$6,250 |
| GPT-4o-mini per-token | ~$338 |
| Provisioned throughput (approximate) | ~$22,000-$30,000 |
| Self-hosted Llama 3.3 | ~$3,000-$6,000 |
Winner: Depends on model. If you're using GPT-4o at $6,250/month, provisioned throughput isn't cheaper yet. But if you can switch to an open-source model, self-hosted wins at this volume. Per-token with GPT-4o-mini at $338/month beats everything if quality is acceptable.
High volume: 500M tokens/day (15B/month)
| Pricing model | Monthly cost |
|---|---|
| GPT-4o per-token | ~$62,500 |
| GPT-4o-mini per-token | ~$3,375 |
| Provisioned throughput | ~$25,000-$36,000 |
| Self-hosted Llama 3.3 (3 GPUs) | ~$9,000-$18,000 |
Winner: Flat-rate. At this scale, provisioned throughput saves 40-60% vs GPT-4o per-token pricing. Self-hosted saves even more if you have the engineering team to manage infrastructure.
What is the break-even formula?
To calculate when flat-rate becomes cheaper than per-token:
Break-even tokens/month = flat_rate_monthly_cost / cost_per_token
Example:
- Provisioned throughput: $25,000/month
- GPT-4o per-token: $2.50/1M input (ignoring output for simplicity)
- Break-even: $25,000 / $2.50 × 1M = 10 billion tokens/month
If you process more than 10B tokens/month on GPT-4o, provisioned throughput is cheaper. Below that, per-token wins.
For GPT-4o-mini at $0.15/1M:
- Break-even: $25,000 / $0.15 × 1M = 167 billion tokens/month
- You'd need to process 167B tokens/month to make provisioned throughput cheaper than GPT-4o-mini per-token
- Almost nobody hits this, which is why cheap per-token models kill the flat-rate argument for most teams
How does cost predictability affect your choice?
Flexera's 2025 State of the Cloud Report found that 82% of organizations rank cost management as their top cloud challenge, and unpredictable AI inference bills compound the problem. The math above only considers raw cost. Many teams choose flat-rate pricing for a different reason: budget predictability.
Token-based pricing means your bill varies month to month. If user engagement spikes, your bill spikes. If a developer deploys a verbose prompt without review, your bill spikes. If a retry bug fires, your bill spikes.
Flat-rate pricing means your bill is the same every month. No surprises. Your CFO can budget exactly, your finance team doesn't need to investigate variance, and a runaway feature can't blow your budget.
You can get predictability with per-token pricing too — just use different tools:
- Budget alerts fire before you hit your cap
- Hard spending caps block requests when budget is exhausted
- Monthly audits catch trends before they become problems
- A cost monitoring proxy gives real-time visibility across all providers
These controls give you per-token efficiency with flat-rate predictability. You pay only for what you use, but you never pay more than you planned.
Decision framework
| Your situation | Best pricing model | Why |
|---|---|---|
| Startup, pre-product-market-fit | Per-token | Usage is unpredictable, need flexibility |
| Growing SaaS, <$5K/month LLM spend | Per-token | Not enough volume for flat-rate savings |
| Stable workload, $10K+/month on one model | Evaluate provisioned | Potential 20-40% savings |
| High-volume batch processing | Self-hosted or provisioned | Predictable, high-throughput workload |
| Multi-model architecture | Per-token per provider | Need flexibility to route across models |
| Enterprise with compliance needs | Provisioned/self-hosted | Data residency, guaranteed capacity |
The most common mistake is switching to flat-rate too early. Teams see their $5,000/month OpenAI bill and think provisioned throughput will save money. But provisioned throughput starts at $20,000+/month — the savings only kick in at much higher volumes.
Frequently Asked Questions
Which is cheaper, per-token or flat-rate LLM pricing?
Per-token is cheaper at low and variable volumes. Flat-rate (provisioned throughput) becomes cheaper above roughly 50 million tokens per day on a single model (OpenAI, 2026). Most startups spending under $5,000/month won't hit that threshold, making per-token the better choice for the majority of teams.
How can I get budget predictability with per-token pricing?
Use budget alerts at 80% of your monthly limit and hard spending caps at 100%. These give you flat-rate-style cost control while paying only for actual usage. You get the economic flexibility of per-token pricing without the surprise bills that make finance teams nervous.
What is provisioned throughput for LLM APIs?
Provisioned throughput is a committed capacity purchase where you pay a fixed monthly fee for guaranteed tokens-per-minute regardless of actual usage. OpenAI and Azure both offer it. Minimum commitments typically start at $20,000+/month, so it's only practical for high-volume enterprise workloads.
Should I switch to flat-rate pricing to save money?
Probably not yet. Most teams save more by optimizing model selection first. Switching 60% of calls from GPT-4o to GPT-4o-mini often saves more than any pricing tier change. Flat-rate makes sense only when you've already optimized and still have consistent high-volume usage on a single model.
What should most teams actually do?
For the majority of teams spending $500-$10,000/month on LLM APIs:
- Stay on per-token pricing. The flexibility is worth more than marginal savings.
- Optimize model selection first. Switching 60% of calls from GPT-4o to GPT-4o-mini saves more than any pricing tier change. See our cheapest LLM per use case guide.
- Use prompt caching. Reduces cost of repeated system prompts by 50-90%. This is effectively a per-token discount.
- Track everything. You can't optimize what you don't measure. Use Tokonomics or build your own cost dashboard.
- Set budget guardrails. Alerts and caps give you flat-rate predictability with per-token economics.
The pricing model matters less than most people think. a16z (2024) found that model selection and prompt optimization together account for a larger cost reduction than any billing structure change. Model selection, prompt efficiency, and cost visibility drive 80% of LLM cost savings. Get those right on per-token pricing, and flat-rate becomes a rounding error.
For a broader overview of LLM pricing strategies including committed use and PAYG tiers, see our dedicated comparison guide.
Last updated June 2026. All sources retrieved June 2026.