AkashML is live.
After months of production hardening, Akash has launched the first fully managed AI inference service built entirely on decentralized GPUs. This release eliminates the operational friction that demanded developers to manage the complexities of production-grade inference service on Akash, giving the benefit of decentralized cloud without the burden of having to manage it.
AkashML is a high-performance, distributed AI inference platform designed to make globally distributed GPUs feel like a single, unified runtime.
In the words of Greg Osuri, Founder of Akash and CEO of Overclock Labs:
“AkashML makes it super simple for ML developers to infer models on Akash with significantly less effort than deploying and maintaining the model. Users reap the benefit of the Decentralized Cloud without the friction of having to learn the system. The SDK being OpenAI compatible means developers can simply change a single line of code to switch their model provider to Akash in their existing AI apps.”
At launch, AkashML delivers managed inference for Llama 3.3-70B, DeepSeek V3, and Qwen3-30B-A3B that are all available instantly and for scaling across 65+ datacenters worldwide.
This is the foundation for instant global inference, predictable pay-per-token pricing, and an entirely new level of developer velocity.
Hear Greg Osuri’s preliminary statement about AkashML at his keynote at the PyTorch Conference:
https://www.youtube.com/watch?v=mU-0U1h8J7U
Learning from Serving Early AI Innovators
Akash has been at the forefront of serving many early AI developers and startups since OpenAI kicked off the AI era a couple years ago. Over the last couple years, the Akash Core team has worked with several customers including brev.dev (acquired by Nvidia), VeniceAI, Prime Intellect and others in enabling them launch products that served tens of thousands of users. While these early users were deeply technical and able to work directly with infrastructure, many of them gave us feedback that they would rather just deal with APIs and leave the underlying infrastructure to us. All this feedback led to us testing a non-public version of AkashML with some select users as well as building AkashChat and AkashChat API as precursors to launching Akash.
The Problems AkashML Solves
Developers and businesses face five persistent barriers when deploying large language models. AkashML eliminates each one.
1. Sky-High Costs
Reserved instances for a 70B model on major clouds routinely exceed \(50,000 per month. AkashML charges by the token.\)0.13 input and $0.40 output per million tokens for Llama 3.3-70B delivers savings of 70-85% through marketplace competition.
2. Operational Overhead
Packaging models, configuring vLLM or TGI servers, managing shards, and handling failovers consume weeks of engineering time. AkashML offers drop-in OpenAI-compatible APIs, enabling migration in minutes without code changes.
3. Latency Bottlenecks
Centralized regions force requests to travel thousands of miles. AkashML routes traffic to the nearest of 80+ global datacenters, achieving sub-200ms response times for real-time applications.
4. Vendor Lock-In
Proprietary ecosystems restrict model choice and data control. AkashML serves only open models (Llama, DeepSeek, Qwen, and others) placing full versioning, upgrades, and data governance in users’ hands.
5. Scalability Limits
Traffic spikes trigger throttling or sudden price surges on traditional platforms. AkashML auto-scales across decentralized GPU supply with 99% uptime and no capacity caps.
Why AkashML Is the Fastest Path to Deploying AI for Developers
AkashML is engineered for rapid onboarding and measurable ROI from day one.
- New accounts receive $100 in AI token credits, which can be used across all supported models in both the Playground and the API platform.
- A unified API endpoint supports all models, integrating seamlessly with LangChain, Haystack, or custom agents.
- Pricing is fully transparent and published per model, eliminating budget surprises.
- High-impact deployments earn featured placement through Akash Star, providing free community exposure.
- Upcoming network upgrades such as: BME, virtual machines, and confidential computing will drive costs even lower.
Early users report 3-5x cost reductions and consistent global latency under 200ms, creating a powerful flywheel: lower prices drive higher usage, attracting more providers and further reducing costs.
How to Get Started in Under 10 Minutes
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Create a free account at playground.akashml.com, it takes less than two minutes.
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Browse the model library, including Llama 3.3-70B, DeepSeek V3, and Qwen3-30B-A3B, with rates displayed upfront.
- If you don’t see the model you need, you can request additional models directly from the model page using the “Request Model” button (example: https://akashml.com/models/deepseek-v3/request).
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Test instantly in the Playground or curl the API:
curl https://api.akashml.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer AKASHML_API_KEY" \ -d '{ "model": "deepseek-ai/DeepSeek-V3.1", "messages": [ { "role": "user", "content": "Your message here" } ] }'-
Monitor token usage, regional latency, and spend through the built-in dashboard.
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Scale to production with region pinning and auto-scaling, then integrate into applications.
Why AkashML Wins
Centralized inference remains expensive, slow, and restrictive. AkashML provides global, low-latency access to the best open models at marketplace-driven prices that are fully managed, API-first and completely decentralized.
Businesses and developers ready to reduce inference costs by up to 80% can begin immediately.
Start free: https://akashml.com
Instant Playground: https://playground.akashml.com
