The question "run the model yourself or use an API" became sharper in 2026, not simpler. On one hand, open models have caught up with closed ones in quality, and local deployment no longer means a compromise. On the other hand, API prices have fallen: Claude Haiku 5.5 costs 10 times less than its predecessors, and Gemini 4 Argon is announced at 2 dollars per million input tokens. We've updated the comparison and recalculated the economics based on current prices.
Cloud Models via API
You send a request to the provider's server and pay for tokens. This is how the APIs of OpenAI, Anthropic, Google work, as well as cloud versions of open models from DeepSeek, Alibaba, and aggregators.
Pros
- Access to the strongest models: closed flagships — GPT-6.1 Sol, Claude Opus 5.5, Gemini 3 Pro — exist only in the cloud.
- Zero upfront investment and launch within an hour.
- Scaling: a thousand requests per second is the provider's problem, not yours.
- Model updates without your involvement.
- No maintenance, electricity, or infrastructure engineer costs.
Cons
- Data goes to someone else's servers. For many industries this is unacceptable, and for Russian companies there's also the question of jurisdiction.
- Cost grows linearly with load and never reaches a plateau.
- Provider dependency: limits, price changes, region shutdowns, model deprecation.
- Network latency and unavailability without internet.
- For users from Russia — payment and access issues with Western providers.
Local Models
You download an open model and run it on your computer, server, or rented GPU. We covered how to do this technically in detail in the local deployment guide, and the model overview is in the article on open-source neural networks.
Pros
- Full privacy: data never leaves your perimeter.
- Fixed costs: after buying hardware, the marginal cost per request is close to zero.
- No limits, blocks, or dependency on external conditions.
- Control: the model version won't change without your knowledge, and you can fine-tune it for your task.
- Works without internet and with minimal latency.
Cons
- Quality is limited by open models and your hardware. Open-world flagships require servers with multiple GPUs; on a home graphics card you can run compact versions at the level of closed models from a year ago.
- Upfront investment: from a few hundred dollars for a consumer card to tens of thousands for a server.
- Maintenance: updates, monitoring, fault tolerance — your responsibility.
- Scaling requires buying hardware in advance.
Comparison Table
| Criterion | Cloud (API) | Local |
|---|---|---|
| Maximum quality | ★★★★★ | ★★★★☆ |
| Privacy | ★★☆☆☆ | ★★★★★ |
| Upfront costs | ★★★★★ | ★★☆☆☆ |
| Costs at high load | ★★☆☆☆ | ★★★★★ |
| Project launch speed | ★★★★★ | ★★★☆☆ |
| Scaling | ★★★★★ | ★★☆☆☆ |
| Independence | ★★☆☆☆ | ★★★★★ |
| Maintenance | ★★★★★ | ★★☆☆☆ |
| Access from Russia | ★★☆☆☆ | ★★★★★ |
Economics: Three Scenarios
The figures below are order-of-magnitude estimates as of October 2026; for an exact calculation for your workload, it's more convenient to use the cost calculator, and for hardware requirements — the VRAM calculator.
Scenario 1. Personal Use or a Small Team
Workload: up to 1 million tokens per day, tasks — texts, code, document parsing.
- Cloud. On a fast tier (Haiku 5.5, Gemini 3.5 Flash) this is a few dollars per month, on a mid tier — tens. A chat subscription for 20 dollars covers most needs.
- Local. A graphics card with 16–24 GB costs 500–2000 dollars, plus electricity. Payback relative to API — years.
Conclusion: cloud, unless there are privacy requirements. The exception is an existing gaming PC with a good card: then a local model is free.
Scenario 2. A Product with Medium Load
Workload: 50–100 million tokens per day — a support chatbot, document processing, content generation.
- Cloud. On fast models — hundreds of dollars per month, on mid-tier — thousands. The bill grows along with the product.
- Local. A server with one or two professional cards (rental 1–3 thousand dollars per month or purchase for 15–40 thousand) handles a medium-sized open model with such a load. Payback of the purchase relative to a mid-tier API — 6–18 months.
Conclusion: borderline zone. If the tasks are simple and suit fast models, cloud is cheaper. If you need a mid tier and the load is stable, a local or rented GPU wins after a year.
Scenario 3. High Load or Sensitive Data
Workload: billions of tokens per day, or any volume but with personal, medical, or financial data.
- Cloud. Tens of thousands of dollars per month and above; with sensitive data — legal risks.
- Local. A GPU cluster, a team of engineers, but the marginal cost per token is several times lower than API, and data is under control.
Conclusion: local or hybrid. At these volumes the savings are obvious, and privacy becomes a requirement rather than an option.
Hybrid Approach
Most mature systems in 2026 use both options simultaneously:
- By data sensitivity. Personal data is processed by a local model, anonymized requests go to the cloud to a stronger model.
- By complexity. A cheap local or fast cloud model does the grunt work (classification, extraction, simple answers), and the flagship is connected only on complex steps. Modern AI agents are built the same way.
- By availability. The main traffic goes to the cloud, the local model is a backup in case of failure or limits.
- Develop locally, production in the cloud — or vice versa: experiment with a free local model, and put a proven API into the product.
Technically, hybrid has become simple: Ollama and other local servers are compatible with the OpenAI API format, so switching between a local and cloud model is just changing the server address in the configuration.
Selection Checklist
Answer five questions:
- Can data be sent outside? If not — only local or a hybrid with local processing of sensitive data.
- Do you need flagship quality? If the task can't be solved by medium-sized open models — cloud.
- What will the load be in a year? Less than 10 million tokens per day — cloud is almost certainly cheaper. More than 100 million — calculate the local option.
- Is there someone to maintain it? Local infrastructure without an engineer turns into a source of problems.
- Is independence from the provider critical? For businesses in Russia, this point often outweighs everything else.
If the answer to the first and fifth questions is "yes," the choice is made. In other cases, start with the cloud: it's faster, and switching to a local model later is technically easy.
Summary
Cloud — for speed of launch, maximum quality, and unpredictable load. Local deployment — for privacy, independence, and savings on large volumes. The boundary between them has shifted over the year in favor of local solutions: open models have become stronger, and deployment tools — simpler. But API prices have also fallen, so for small projects the cloud is still more reasonable. Calculate for your own workload, not by general advice.
API prices and open model capabilities change every month. We track both in our channel: price cuts, new open releases, and what hardware they run on.