Scalable, secure LLMinfrastructure built forreal production workloads
Integrate large language models into your applications in minutes with Bulutistan LLMaaS. Token-based, flexible, and secure — running on low-latency, production-ready infrastructure hosted in Türkiye.
Start with zero setup overhead and scale effortlessly as your usage grows.
- Data stays in Türkiye
- Production-ready infrastructure
- Low latency
- Token-based usage
$ Models, compliance, and stability — in a single layer.
Enterprise AI projects need more than a good model — they need infrastructure you can trust, regulatory compliance, and stable performance. Bulutistan LLMaaS brings all three together in a single service layer.
- Türkiye-compliant data residencyData processing and storage stay on local infrastructure.
- High-performance inferenceLow p95 latency through an optimized serving layer.
- The right model for the jobChat, agent, document — different models from a single endpoint.
- Flexible token-based scaleThe same infrastructure from PoC to high volume.
Buy a production pipeline, not just a model.
LLMaaS (Large Language Model as a Service) is a service model that lets you consume large language models over an API.
Instead of standing up your own GPU infrastructure, running a model serving layer, and designing scaling and access operations, you get model output directly from a ready-to-use service.
Teams focus on product development, integration, and business value — not on infrastructure complexity.
— Less operational overhead, faster integration, more predictable cost.
Designed layer by layer. Delivered from a single endpoint.
Built around four core principles: local compliance, stable performance, the right model choice, and predictable economic growth.
Türkiye-compliant infrastructure
An infrastructure approach that keeps your data in Türkiye, giving you a foundation aligned with enterprise security and regulatory expectations.
High-performance inference
An optimized serving layer delivering low-latency, stable model access built for heavy usage.
Flexible model selection
Choose from open-source model options suited to different business scenarios, balancing accuracy, performance, and cost.
Scalable cost structure
A token-based usage model that supports controlled growth — from small experiments to high-volume production scenarios.
Built for teams turning AI ideas into products.
Designed to help engineering, product, IT, and innovation teams ship LLM-based projects faster.
- Engineering teamsFast API-based integration and a short development cycle.
- Product teamsRoll out chat, summarization, classification, and automation flows quickly.
- Enterprise ITMeets security, control, and scalability expectations all at once.
- Innovation teamsAccelerates the move from PoC to production.
OpenAI-compatible SDK
Your existing client libraries and integrations keep working with a single endpoint change.
Smart routing
Traffic can be routed to different models per request, so the best service is selected for every call.
Streaming output
Token-by-token streaming for chat and agent scenarios dramatically reduces time to first response.
Rate limits & quotas
Quotas at the organization, project, and API key level keep usage predictable.
Observability
Visibility into token spend, latency, and error metrics — usage reported in an engineering-friendly way.
Dedicated capacity
As volume and SLA requirements grow, dedicated capacity can be provisioned behind the same API.
One service layer, many AI products.
Power a range of AI products and intelligent automation flows on the same infrastructure.
AI Chatbots
Conversational experiences for customer communication, support workflows, and internal knowledge access.
AI Agents
Agent-based workflows that autonomously carry out specific tasks.
Document Processing
Analyze, summarize, classify, and make sense of your documents.
Code Assistants
Code assistance and explanation flows that boost developer productivity.
Internal Knowledge
Internal assistants that give faster access to enterprise knowledge.
Workflow Automation
Speed up repetitive work across support, operations, and content processes with AI.
Live in minutes.
The same service structure for PoC, pilot, and production scenarios. No setup complexity.
- 01
Create your API access
Create an organization and API key from the console, and start access in seconds.
export BULUT_KEY="blt_live_********" - 02
Pick the model that fits your needs
Chat, agent, document, or code. Choose the right model for every scenario.
model: "qwen3-next-80b-instruct" - 03
Send a request, get your output
Complete the integration over an OpenAI-compatible endpoint; get responses via streaming or all at once.
POST /v1/chat/completions - 04
Scale as usage grows
Grow in a controlled way on the same infrastructure — from PoC to high volume — with quotas and capacity tuned to your needs.
autoscale: true
A foundation designed for enterprise expectations.
In LLM projects, it's not just model quality that matters — where data is processed, how access is governed, and how predictable the infrastructure is are just as critical.
Bulutistan LLMaaS answers this need with Türkiye compliance, an enterprise-usage focus, and a production-ready service approach.
- Local data residencyRequests and log streams are processed within Türkiye's borders.
- Organization-based isolationAccess, permission, and quota management at the project and key level.
- Auditable usageConsumption and error metrics that can be reported independently.
- Stable service levelPredictable performance through capacity planning and a scaling approach.
The right model for your needs — transparent, token-based pricing.
With models optimized for different use cases, tune the balance of accuracy, performance, and cost to fit your needs. Pay only for the tokens you use.
qwen3-next-80b-instruct
Qwen3-Next 80B MoE · hybrid attentionQwen3-Next 80B MoE (3B active) — with a Gated DeltaNet + Gated Attention hybrid attention architecture. Delivers output approaching Qwen3-235B quality at roughly 1/3 of the cost. Suited to chat, agent, and long-context scenarios.
- Context
- 131K
- Profile
- chat · completion
- Pricing
- $0.61 / $3.20
gpt-oss-120b
OpenAI gpt-oss MoE · 5.1B activeOpenAI gpt-oss-120b MoE (117B total, 5.1B active). MXFP4 quantized, with harmony response format and configurable reasoning levels. A balanced profile for general-purpose enterprise scenarios.
- Context
- 131K
- Profile
- chat · completion
- Pricing
- $0.61 / $3.20
gemma-4-26b
Gemma 4 26B MoE · multimodal · tool-useGemma 4 26B MoE (26B total, 3.8B active) — hybrid sliding + global attention. Processes text and image input in a single model and supports tool-use. Suited to multimodal chat, vision-aware agents, and mixed-content scenarios.
- Context
- 131K
- Profile
- text · vision · tools
- Pricing
- $0.40 / $2.20
Token-based, predictable pricing.
The same pricing logic from PoC to high-volume production traffic. Move to a dedicated plan as your capacity and compliance needs grow.
| Model | Input · 1M tk | Output · 1M tk | Context |
|---|---|---|---|
| qwen3-next-80b-instruct | $0.61 | $3.20 | 131K |
| gpt-oss-120b | $0.61 | $3.20 | 131K |
| gemma-4-26b | $0.40 | $2.20 | 131K |
Prices are per 1M tokens. VAT excluded.
Need high volume, dedicated capacity, or a custom SLA?
Talk to our solution engineers about enterprise needs like dedicated capacity, a written SLA, a KVKK compliance addendum, and VPN / IP whitelisting. We'll prepare a proposal tailored to your use case.
Prices and quotas are confirmed in writing in the service agreement. The figures shown in the list are for illustration only.
Start building your
LLM-based products today.
Move your team to production fast with secure, scalable, enterprise-ready LLM infrastructure.
Start in minutes and grow on the same foundation as your needs increase.
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