YandexGPT
Cloud platform as a service (PaaS) software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is YandexGPT
YandexGPT is a large language model service provided by Yandex and delivered through Yandex Cloud for building and integrating generative AI features into applications. It targets developers and product teams that need text generation, summarization, Q&A, and conversational interfaces via API. The service is typically used alongside other cloud components (identity, networking, logging, and application hosting) to deploy AI-enabled workloads. It is positioned as a managed model offering with regional availability aligned to Yandex Cloud infrastructure.
Managed LLM via API
YandexGPT provides a managed interface for text generation and chat-style interactions without requiring teams to host model infrastructure. This reduces operational work compared with self-managed model serving on general compute. It fits common application patterns such as assistants, content drafting, classification, and summarization. Integration is typically done through standard HTTPS APIs and cloud IAM controls.
Integrated with cloud services
The service is designed to work within the broader Yandex Cloud environment, which can simplify deploying end-to-end applications that combine compute, storage, networking, and observability. Teams can align access control, auditing, and resource management with existing cloud accounts. This can be useful when building production systems that need consistent governance across components. It also supports building AI features as part of a broader PaaS-style architecture.
Regional hosting alignment
YandexGPT is hosted within Yandex Cloud regions, which can help organizations that need data residency or latency characteristics tied to those regions. For teams operating primarily in markets where Yandex Cloud has strong presence, this can reduce cross-border data transfer requirements. It can also simplify procurement and support when the rest of the stack already runs on the same cloud. Regional alignment may be a deciding factor versus using function-only or frontend-focused platforms.
Ecosystem and portability constraints
Using YandexGPT typically ties application architecture to Yandex Cloud APIs, IAM, and service conventions. Migrating prompts, integrations, and operational tooling to another cloud may require rework. Organizations standardizing on other major cloud platforms may face additional integration overhead. This can be a limitation compared with more cloud-agnostic deployment patterns.
Model transparency and controls vary
As a managed model service, details about training data, evaluation methodology, and certain safety controls may be less transparent than what some enterprises require for governance. Available controls for content filtering, auditability, and policy enforcement depend on the current feature set of the service. Teams with strict compliance requirements may need additional layers (logging, redaction, human review) around the API. This can increase implementation complexity for regulated use cases.
Feature parity not guaranteed
Capabilities such as advanced tool/function calling, fine-tuning options, context window sizes, and enterprise administration features may differ from other LLM platforms and can change over time. If an application depends on specific generative AI features, teams may need to validate support and roadmap fit before committing. Performance and cost characteristics can also vary by region and workload profile. This may require benchmarking and ongoing monitoring in production.
Plan & Pricing
Pricing model: Pay-as-you-go Free tier/trial: Trial credits / trial period available for new Yandex Cloud customers; no evidence of a permanent free tier specifically for YandexGPT (see notes).
Example costs (selected, all without VAT, from Yandex AI Studio pricing policy):
- YandexGPT Lite — $0.001667 per 1,000 input tokens (synchronous); $0.001667 per 1,000 output tokens (synchronous); $0.000834 per 1,000 input/output tokens (asynchronous).
- YandexGPT Pro 5.1 — $0.0067 per 1,000 input tokens (synchronous); $0.0067 per 1,000 output tokens (synchronous); $0.003361 per 1,000 input/output tokens (asynchronous).
- YandexGPT Pro 5 — $0.0100 per 1,000 input tokens (synchronous); $0.0100 per 1,000 output tokens (synchronous); $0.0050 per 1,000 input/output tokens (asynchronous).
- Embeddings (text vectorization) — $0.000083 per 1,000 tokens (one unit = one token).
- Image generation (YandexART) — $0.018333 per generation request.
- Text classification (examples): 1 request (1,000 tokens) with YandexGPT Lite — $0.001250; 1 request (250 tokens) with YandexGPT Pro or tuned classifier — $0.001250.
- Voice agents (example unit rates): incoming audio $0.000217 per second; outgoing audio $0.000167 per second; text generation for voice agents: $0.006668 per 1,000 tokens (example from Agent Atelier section).
Batch mode and dedicated instances:
- Batch mode minimum cost per run: 200,000 tokens (i.e., batch runs are charged with a required minimum of 200k tokens).
- Dedicated instances are charged per second; price examples shown on the official page (per hour rates by model/configuration) — see official docs for specifics.
Notes & important details from the official site:
- Token accounting: input + output tokens determine cost; Yandex Cloud Billing breaks usage into billing units (rounded up).
- Some components are free: TokenizerService calls and Tokenizer methods are free of charge; fine-tuning was free at Preview (per official docs).
- Currency and applicable prices depend on contracting legal entity and region; prices shown above are USD (without VAT) as listed on the Yandex AI Studio pricing policy (updated Feb 16, 2026).
Seller details
Yandex LLC
Moscow, Russia
1997
Private
https://translate.yandex.com/
https://x.com/yandex
https://www.linkedin.com/company/yandex/
