Self-Hosting LLMs vs API: The Real Cost Breakdown (2026)
February 2026 analysis — self-hosting Llama 4 vs using GPT-5/Claude APIs. GPU costs, breakeven at ~6.8M tokens/month, hidden costs of self-hosting. Complete cost comparison with real numbers.
Should you self-host an open-source model like Llama or pay for a hosted API like GPT-5 or Claude? The answer is not philosophical — it is a math problem. Your volume, latency requirements, privacy constraints, and team capabilities determine which option costs less. Most developers get this wrong because they compare GPU rental prices to API prices without accounting for the full picture.
This guide runs the actual numbers as of February 2026. Every price point, every GPU option, and every hidden cost is included so you can make the decision with real data instead of assumptions.
The Cost of API Access (February 2026)
API pricing has dropped significantly over the past year. Here is what the major providers charge today, along with estimated monthly costs assuming a workload of 10 million tokens per day (roughly 5M input + 5M output, 30 days):
Flagship Models
| Model | Provider | Input / 1M | Output / 1M | Monthly Cost (10M tok/day) |
|---|---|---|---|---|
| GPT-5 | OpenAI | $1.25 | $10.00 | ~$168 |
| Claude Sonnet 4.5 | Anthropic | $3.00 | $15.00 | ~$270 |
| Gemini 2.5 Pro | $1.25 | $10.00 | ~$168 | |
| Grok 3 | xAI | $3.00 | $15.00 | ~$270 |
Budget Models
| Model | Provider | Input / 1M | Output / 1M | Monthly Cost (10M tok/day) |
|---|---|---|---|---|
| DeepSeek V3.2 | DeepSeek | $0.27 | $1.10 | ~$21 |
| Gemini 2.5 Flash | $0.15 | $0.60 | ~$11 | |
| GPT-4.1 Nano | OpenAI | $0.10 | $0.40 | ~$8 |
| Mistral Small 3.1 | Mistral | $0.20 | $0.60 | ~$12 |
| Llama 3.3 70B (hosted) | Meta | $0.88 | $0.88 | ~$26 |
Use the AI Model Pricing Calculator to plug in your exact workload.
Why APIs Are Compelling
The API model has several structural advantages that are easy to undervalue:
- Zero infrastructure. No GPU provisioning, no driver updates, no CUDA version conflicts.
- Automatic scaling. Handle 10 requests or 10,000 per second without configuration changes.
- Always the latest model. When OpenAI ships GPT-5.1, you update one string in your code.
- No idle cost. You pay per token. If traffic drops to zero on weekends, your bill drops to zero.
- Built-in reliability. Providers handle failover, redundancy, and uptime SLAs.
For most teams under 50 million tokens per day, APIs are the cheaper option after accounting for total cost of ownership. The rest of this article explains why.
The Cost of Self-Hosting
Self-hosting means running an open-source model (typically Llama, Mistral, or Qwen) on GPU hardware you rent or own. The primary cost is the GPU, but the real cost includes everything around it.
Cloud GPU Pricing (Per Hour, February 2026)
These are representative prices from major cloud GPU providers (AWS, GCP, Lambda Labs, RunPod, Vast.ai). Prices vary by provider and commitment level — the figures below reflect on-demand pricing without long-term contracts.
| GPU | VRAM | Price/hr | Monthly (24/7) | Best For |
|---|---|---|---|---|
| NVIDIA L4 | 24 GB | ~$0.70/hr | ~$504 | Budget inference, 7B models |
| NVIDIA A10G | 24 GB | ~$1.00/hr | ~$720 | Small models (7B-13B) |
| NVIDIA A100 80GB | 80 GB | ~$2.00/hr | ~$1,440 | Llama 70B at good throughput |
| NVIDIA H100 80GB | 80 GB | ~$3.50/hr | ~$2,520 | Maximum performance, large models |
| 2x A100 80GB | 160 GB | ~$4.00/hr | ~$2,880 | 70B+ models with high throughput |
| 4x A100 80GB | 320 GB | ~$8.00/hr | ~$5,760 | 405B parameter models |
| 8x A100 80GB | 640 GB | ~$16.00/hr | ~$11,520 | 405B at production throughput |
Spot instances can reduce these prices by 40-60%, but they come with interruption risk — unsuitable for production workloads that need uptime guarantees.
Consumer GPU (One-Time Purchase)
If you are running inference on-premise or on a personal workstation, these are the relevant hardware options:
| Hardware | VRAM / Memory | Approx. Price | What It Runs |
|---|---|---|---|
| RTX 3090 24GB | 24 GB | ~$800 (used) | 7B-13B models, 70B with heavy quantization |
| RTX 4090 24GB | 24 GB | ~$1,600 | 7B-13B at full speed, 70B at 4-bit quant |
| 2x RTX 4090 | 48 GB | ~$3,200 | 70B at decent speed with model sharding |
| Mac M4 Max 128GB | 128 GB unified | ~$4,000 | 70B models in unified memory, slower than dedicated GPUs |
| Mac M4 Ultra 192GB | 192 GB unified | ~$6,500 | 70B-405B with quantization, limited throughput |
Consumer hardware has zero recurring cost after purchase (besides electricity, roughly $30-80/month for a GPU workstation), but it also has zero redundancy. If the GPU dies, your service is down.
Use the LLM VRAM Calculator to check exactly how much memory your target model needs.
What Model Sizes Require What Hardware
This is the core mapping every self-hosting decision starts with:
| Model Size | VRAM Required (FP16) | VRAM Required (4-bit) | Minimum Hardware |
|---|---|---|---|
| 7B parameters | ~14 GB | ~4 GB | 1x A10G, 1x RTX 4090, 1x L4 |
| 13B parameters | ~26 GB | ~8 GB | 1x A10G (tight), 1x A100 |
| 70B parameters | ~140 GB | ~35 GB | 1x A100 80GB (4-bit), 2x A100 (FP16) |
| 405B parameters | ~810 GB | ~200 GB | 4-8x A100 80GB |
Quantization (running models at reduced precision like 4-bit or 8-bit) cuts VRAM requirements by 2-4x with modest quality loss on most benchmarks. For production workloads, 4-bit quantized 70B models are the sweet spot — they fit on a single A100 80GB and deliver competitive quality.
Breakeven Analysis: When Does Self-Hosting Win?
This is the critical calculation. Self-hosting has a fixed monthly cost (GPU rental) regardless of usage. APIs have a variable cost that scales linearly with token volume. The breakeven point is where the API cost exceeds the GPU cost.
Setup: Llama 3.3 70B on a Single A100 80GB
- Monthly GPU cost: $1,440 (24/7 on-demand)
- Inference throughput: Using vLLM or TensorRT-LLM, a single A100 can serve a 70B model at roughly 1,000-3,000 tokens/second output throughput (varies by batch size, context length, and quantization).
- Monthly capacity: At a conservative 1,500 tokens/sec average, that is approximately 3.9 billion tokens per month.
Now compare this to API costs at different volume levels. For API cost, we use a 50/50 input-to-output ratio (half your tokens are input, half are output):
Breakeven vs GPT-5 ($1.25 input, $10.00 output per 1M tokens)
Blended rate at 50/50 split: ($1.25 + $10.00) / 2 = $5.625 per 1M tokens.
| Monthly Volume | API Cost (GPT-5) | Self-Host Cost | Winner |
|---|---|---|---|
| 1M tokens | $5.63 | $1,440 | API (256x cheaper) |
| 10M tokens | $56.25 | $1,440 | API (26x cheaper) |
| 50M tokens | $281 | $1,440 | API (5x cheaper) |
| 100M tokens | $563 | $1,440 | API (2.6x cheaper) |
| 256M tokens | $1,440 | $1,440 | Breakeven |
| 500M tokens | $2,813 | $1,440 | Self-host (1.95x cheaper) |
| 1B tokens | $5,625 | $1,440 | Self-host (3.9x cheaper) |
| 3.9B tokens | $21,938 | $1,440 | Self-host (15x cheaper) |
Breakeven: ~256 million tokens/month (~8.5M tokens/day) vs GPT-5.
That sounds achievable for a production workload. But this comparison is misleading, because Llama 3.3 70B and GPT-5 are not the same model. GPT-5 outperforms Llama 70B on most benchmarks, especially complex reasoning, instruction following, and agentic tasks. You are not replacing GPT-5; you are replacing it with a weaker model.
Breakeven vs DeepSeek V3.2 ($0.27 input, $1.10 output per 1M tokens)
Blended rate at 50/50 split: ($0.27 + $1.10) / 2 = $0.685 per 1M tokens.
| Monthly Volume | API Cost (DeepSeek) | Self-Host Cost | Winner |
|---|---|---|---|
| 100M tokens | $68.50 | $1,440 | API (21x cheaper) |
| 500M tokens | $342.50 | $1,440 | API (4.2x cheaper) |
| 1B tokens | $685 | $1,440 | API (2.1x cheaper) |
| 2.1B tokens | $1,440 | $1,440 | Breakeven |
| 3.9B tokens | $2,672 | $1,440 | Self-host (1.85x cheaper) |
Breakeven: ~2.1 billion tokens/month (~70M tokens/day) vs DeepSeek V3.2.
That is an enormous volume. Most startups never reach 70 million tokens per day. And DeepSeek V3.2 arguably matches Llama 70B in quality while requiring zero infrastructure.
Breakeven vs Gemini 2.5 Flash ($0.15 input, $0.60 output per 1M tokens)
Blended rate: ($0.15 + $0.60) / 2 = $0.375 per 1M tokens.
Breakeven: ~3.84 billion tokens/month (~128M tokens/day).
This is essentially the entire capacity of your A100. You would need to be running the GPU at maximum utilization 24/7 just to break even against Gemini Flash pricing. The economics almost never favor self-hosting over ultra-cheap API models.
The Breakeven Summary
| Compare Self-Host Against | Breakeven Volume | Tokens/Day |
|---|---|---|
| GPT-5 | ~256M tokens/month | ~8.5M/day |
| Claude Sonnet 4.5 | ~160M tokens/month | ~5.3M/day |
| DeepSeek V3.2 | ~2.1B tokens/month | ~70M/day |
| Gemini 2.5 Flash | ~3.84B tokens/month | ~128M/day |
The takeaway: self-hosting only beats APIs on cost when you are comparing against expensive flagship models and running at high volume. Against budget APIs, the breakeven is often physically impossible to reach on a single GPU.
Hidden Costs of Self-Hosting
The GPU rental price is the floor, not the ceiling. Every self-hosting deployment has additional costs that teams consistently underestimate.
1. DevOps Engineering Time
Someone has to set up the inference server, configure model loading, tune batch sizes, manage GPU drivers, handle CUDA version compatibility, and keep everything running. This is not a one-time setup — it is ongoing maintenance.
Conservatively, a self-hosted LLM deployment requires 10-20 hours per month of engineering time for maintenance, monitoring, and troubleshooting. At $75-150/hour for a senior DevOps or ML engineer, that is $750-$3,000/month in labor cost alone.
2. Inference Server Software
You need inference software to serve the model efficiently. The main options in 2026:
- vLLM — Open source, excellent throughput with PagedAttention, the default choice for most deployments.
- TensorRT-LLM — NVIDIA’s optimized runtime, best performance on NVIDIA GPUs, more complex setup.
- Text Generation Inference (TGI) — Hugging Face’s solution, easy to deploy, slightly lower throughput.
- Ollama — Great for local development, not optimized for production-scale serving.
The software itself is free, but tuning it for your workload — batch sizes, KV cache allocation, tensor parallelism configuration — takes expertise and time.
3. Scaling and Reliability
A single GPU is a single point of failure. For production, you need:
- Load balancing across multiple GPU instances
- Health checks and automatic restart on crashes
- Autoscaling for traffic spikes (or accepting degraded performance during peaks)
- Monitoring — GPU utilization, inference latency p50/p95/p99, queue depth, error rates
This infrastructure adds complexity and cost. A basic Kubernetes setup with GPU node pools, monitoring (Prometheus + Grafana), and load balancing adds $200-500/month in infrastructure and significantly more in engineering time.
4. Model Quality Gap
This is the cost people avoid quantifying. In February 2026, the benchmark standings are roughly:
| Model | MMLU | HumanEval | Reasoning Tasks |
|---|---|---|---|
| GPT-5 | ~92% | ~95% | Excellent |
| Claude Sonnet 4.5 | ~90% | ~93% | Excellent |
| Llama 3.3 70B | ~82% | ~81% | Good |
| Llama 3.3 70B (4-bit) | ~79% | ~77% | Good (slight degradation) |
The quality gap matters. If your self-hosted model produces lower quality outputs, you may need more tokens per task (retries, longer prompts with more examples, post-processing steps) — which erodes the cost advantage.
5. Idle Cost
APIs cost nothing when idle. A rented GPU costs the same whether you process zero tokens or four billion. If your workload is variable — high during business hours, near-zero at night and on weekends — you are paying for idle GPU time roughly 60-70% of the month.
Even with spot instances or autoscaling (spinning down GPUs during low demand), the operational complexity of managing this further increases costs.
6. No Automatic Updates
When Meta releases Llama 4 (or 3.4, or whatever comes next), you have to download the weights, test compatibility, benchmark performance, update your inference configuration, and redeploy. API providers ship model updates with zero effort on your part.
Total Cost Adjustment
Adding these hidden costs together, a reasonable rule of thumb is: multiply the raw GPU cost by 1.3x to 2.0x to get the true total cost of self-hosting.
| Cost Component | Monthly Estimate |
|---|---|
| GPU rental (A100 80GB) | $1,440 |
| DevOps time (15 hrs x $100/hr) | $1,500 |
| Infrastructure overhead | $300 |
| True monthly cost | $3,240 |
At the adjusted cost of $3,240/month, the breakeven against GPT-5 jumps from 256M tokens/month to ~576M tokens/month (~19M tokens/day). Against DeepSeek V3.2, it jumps to ~4.7 billion tokens/month — which exceeds the throughput capacity of a single A100.
When Self-Hosting Makes Sense
Despite the cost disadvantage at most volumes, there are legitimate reasons to self-host:
Data Privacy and Compliance
This is the strongest argument for self-hosting. If you work in healthcare (HIPAA), finance (SOC 2, PCI-DSS), government (FedRAMP), or handle EU personal data under strict GDPR interpretations, sending data to third-party API providers may not be an option. Self-hosting keeps all data within your infrastructure boundary.
Note: Both OpenAI and Anthropic now offer zero-data-retention API plans and SOC 2 compliance, which addresses many privacy concerns without self-hosting. But some regulatory environments still require full on-premise control.
Massive Volume (100M+ Tokens/Day)
If you consistently process over 100 million tokens per day and your target quality level is comparable to what open-source models deliver, self-hosting starts to win on pure economics — even after accounting for hidden costs. At 500M+ tokens/day, the savings become significant.
Companies at this scale typically have dedicated ML infrastructure teams already, which reduces the marginal cost of adding LLM serving to existing GPU clusters.
Custom Fine-Tuned Models
If you have fine-tuned a model on proprietary data and it outperforms general-purpose APIs for your specific use case, self-hosting is the only option. You cannot deploy a custom fine-tune to someone else’s API infrastructure (with the exception of OpenAI and Mistral fine-tuning APIs, which support custom models but at higher per-token costs than self-hosting at scale).
Latency-Sensitive On-Premise Deployments
Some applications require single-digit millisecond latency that network round-trips to cloud APIs cannot achieve. Industrial automation, real-time trading systems, and edge deployments fall into this category. Running inference on a local GPU eliminates network latency entirely.
Research and Experimentation
If you are an ML researcher modifying model architectures, testing quantization strategies, or benchmarking inference optimizations, self-hosting is a necessity — not a cost decision.
When API Makes More Sense
For the majority of production use cases, APIs win:
Under 50M Tokens/Day
At this volume, even the most expensive APIs (Claude Opus 4.5 at $5/$25 per 1M) cost less than renting GPU infrastructure after accounting for hidden costs. With budget models like DeepSeek or Gemini Flash, the crossover point is so high it is effectively unreachable.
Need Flagship-Quality Models
GPT-5, Claude Opus 4.5, and Gemini 2.5 Pro are not available for self-hosting. If your application requires frontier-model capabilities — strong reasoning, nuanced instruction following, advanced agentic behavior — APIs are the only path. Open-source models are improving rapidly but still trail the best proprietary models on the hardest tasks.
Small Team Without GPU Expertise
Running GPU infrastructure well requires specialized knowledge: CUDA debugging, memory optimization, tensor parallelism, inference server tuning. If your team does not have this expertise, the learning curve and the mistakes made along the way will cost more than the API premium.
Variable or Unpredictable Traffic
If your traffic patterns are bursty — spikes during product launches, seasonal fluctuations, or growth-phase unpredictability — APIs absorb the variance automatically. Self-hosted infrastructure either over-provisions (wasting money during low traffic) or under-provisions (degrading performance during peaks).
Want the Latest Models Immediately
The pace of model releases is accelerating. In the past 12 months, we have seen GPT-5, Claude 4.5, Gemini 2.5, Grok 3, DeepSeek V3.2, and Mistral Large 3 — all accessible via API within days of launch. Self-hosting ties you to a specific model version that you must manually upgrade.
The Hybrid Approach: Best of Both Worlds
The most cost-effective architecture for many teams is a hybrid setup:
Architecture
- Self-host a small, fast model (Llama 7B or Mistral 7B) for high-volume, simple tasks: classification, entity extraction, content filtering, embeddings.
- Use API calls for complex tasks that require flagship-quality reasoning: content generation, code review, agentic workflows, customer-facing chat.
Example: E-Commerce Product Pipeline
Customer review comes in
|
v
[Self-hosted Llama 7B] -- Classify sentiment (positive/negative/neutral)
| Extract product mentions
| Flag for moderation if needed
| Cost: ~$0 marginal (GPU already running)
v
[API: GPT-5] -- Generate personalized response
Complex cases only (~10% of volume)
Cost: ~$56/month at 10M tokens/monthIn this setup, the self-hosted model handles 90% of the volume at near-zero marginal cost (the GPU is a fixed expense), while the API handles the 10% that actually needs flagship intelligence. Total cost is a fraction of routing everything through an API, and a fraction of self-hosting a model large enough to handle the complex tasks.
When Hybrid Works
- You have a clear separation between simple and complex tasks
- The simple tasks represent 70%+ of your token volume
- You have the infrastructure to run a small model (even a single RTX 4090 is sufficient for 7B-13B)
- Your complex tasks genuinely need flagship-model quality
When Hybrid Is Overkill
- Your total volume is under 10M tokens/day (just use a cheap API for everything)
- All your tasks require similar quality levels
- You do not have anyone who can maintain a self-hosted model
Quick Decision Framework
Use this flowchart to guide your decision:
Step 1: What is your monthly token volume?
- Under 10M tokens/day: Use an API. Full stop. DeepSeek V3.2 or Gemini Flash will cost you under $30/month.
- 10M-100M tokens/day: Continue to Step 2.
- Over 100M tokens/day: Continue to Step 3.
Step 2: Do you have privacy/compliance requirements that prohibit third-party APIs?
- No: Use an API. At 10-100M tokens/day, even flagship APIs cost $170-$2,700/month. Self-hosting would cost more after hidden costs.
- Yes: Consider self-hosting. Budget $3,000-5,000/month total cost for a 70B model on A100 infrastructure.
Step 3: Do you need GPT-5/Claude-level quality?
- Yes: Use an API for those tasks. Consider hybrid for the rest.
- No, open-source quality is sufficient: Self-hosting likely makes economic sense at this volume. Budget for proper infrastructure and a dedicated engineer.
Step 4: Do you have ML infrastructure expertise on your team?
- Yes: Self-hosting is viable. Start with vLLM on a single A100, benchmark throughput, and scale from there.
- No: Use an API or invest in hiring before committing to self-hosting. The first three months of a self-hosted deployment without experienced engineers will cost more in wasted GPU hours and debugging time than a year of API costs.
The Bottom Line
For the vast majority of teams in 2026, API access is cheaper than self-hosting after accounting for the full cost picture. The math only flips at very high volumes (100M+ tokens/day) or when privacy requirements force on-premise deployment.
The budget API tier has changed the calculus dramatically. When DeepSeek V3.2 charges $0.27 per million input tokens and Gemini Flash charges $0.15, the breakeven for self-hosting a comparable model is billions of tokens per month. At those volumes, you are not a startup — you are running infrastructure at the scale of a major tech company.
Start with APIs. Measure your actual usage. If and when you cross 50-100 million tokens per day with a workload where open-source model quality is sufficient, revisit the self-hosting analysis with real numbers from your production traffic.
Related tools and guides:
- LLM VRAM Calculator — Calculate exactly how much GPU memory your target model needs
- AI Model Pricing Calculator — Compare API costs across 40+ models with your usage patterns
- How to Cut AI API Costs by 80% — 8 strategies to reduce API spend before considering self-hosting
- AI API Pricing Comparison 2026 — Full pricing table for all major providers