Akamai Inference Cloud
Turn trained models into real-time intelligence that performs securely at global scale.
Book an AI consultation, create a cloud account, or join the Blackwell GPU waitlist.
Inference is the future of AI
Training teaches AI to think; inference puts it to work. It’s how models become applications that reason, respond, and act in real time. With Akamai Inference Cloud, AI runs closer to users for lower latency, predictable performance, and global reach.
- Reduce latency by up to 80% versus centralized clouds by running inference near users on a distributed platform. See how.
- Optimize time to first token (TTFT) and tokens per second (TPS) with NVIDIA Blackwell GPUs running on a network built for low-latency delivery. Read the Blackwell benchmarks.
- Secure the entire AI interaction layer with model-aware protections at the edge.
Why Akamai Inference Cloud?
Akamai offers a hardened, globally distributed cloud built for the AI era, combining GPU inference, edge traffic control, and AI-aware security.
- Run AI faster and closer to users: Requests are routed to optimal GPU regions across a global edge footprint for consistent, low-latency responses.
- Secure AI workloads at the edge: Layered defenses block prompt injection, scraping, model abuse, and DDoS before they reach your endpoints.
- Build and scale without lock-in: Open APIs, full Kubernetes control, no-cost egress, and clear pricing help you scale on your terms.
Read why we built for the agentic web.
How it works
Build a unified AI stack — from models and data to execution and security — with edge-native routing and observability.
- Edge intake and policy
- AI-aware traffic management routes each request at the edge, applying LLM-specific rate limits, quotas, and semantic caching where appropriate.
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Optional model-aware protections (Firewall for AI) evaluate prompts to mitigate injection, jailbreak attempts, and abusive patterns before hitting your model.
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Secure, low-latency routing
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Traffic is directed to the closest suitable GPU region using Akamai’s distributed edge network and global traffic management for predictable performance.
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High-performance inference
- Inference runs on NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs with NVIDIA BlueField DPUs, optimized for TTFT and TPS.
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Choose your runtime: vLLM, KServe, NVIDIA NIMs, NVIDIA NeMo, or your preferred framework.
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Data and memory services at the edge
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Access vector databases for RAG, tiered memory (GDDR7/DRAM/NVMe), and low-latency object/block storage to serve context and tools in real time.
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Streamed responses and acceleration
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Stream tokens to clients with CDN acceleration and optional semantic caching for repeat queries.
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Observability and controls
- Unified logs and metrics feed into your stack via low-latency data streams for real-time insight, cost control, and SLO tracking.
What you can build
- Agentic AI and assistants: Multi-tool, multi-agent workflows with edge inference for faster, more accurate responses.
- Customer experience and chatbots: Global conversational apps with streaming responses and predictable latency.
- Personalization and recommendations: Real-time recommendations with vector search and secure data access at the edge.
- Automation and decision engines: High-frequency inference for fintech, healthcare, and commerce that must act in real time.
Platform capabilities
Build
- GPU compute
- NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs; scale from a single GPU to clusters.
- BlueField-3 DPUs, 128 vCPUs, up to 1,472 GB DRAM, and 8,192 GB NVMe per node for demanding inference.
- Kubernetes platform
- Managed Kubernetes (LKE) with KServe and Kubeflow Pipelines; open and portable to any CNCF-conformant Kubernetes.
- App Platform: a pre-engineered cloud-native stack to deploy LLMs, agents, and knowledge bases quickly. Explore the docs.
- Data and memory
- Managed vector databases, object and block storage, backups and snapshots, private networking/VPC.
- Serverless and edge
- EdgeWorkers for ultra-low-latency functions; integrate with your services and models at the edge.
Protect
- Model- and app-aware security
- Firewall for AI, App & API Protector, API Security, and Bot & Abuse Protection work together to defend endpoints and agents.
- Network and lateral movement protections
- Guardicore Segmentation and DDoS protections harden the environment around your AI stack.
- Identity and access
- Granular controls at the edge to protect data, credentials, and tools accessed by agents.
Optimize
- Edge-native control plane for humans and agents: discovery, auth, identity, trust.
- Semantic caching, LLM rate limits and quotas, and MCP server support to reduce cost and boost responsiveness.
- CDN acceleration and unified observability to track TTFT/TPS, errors, and spend in real time.
Integrations and compatibility
- NVIDIA AI Enterprise: built to accommodate NIM microservices and NeMo for accelerated inference and model operations.
- Open AI stack support: vLLM, KServe, Kubernetes, vector databases, and OpenAI-compatible APIs.
- Portable by design: run on LKE or your preferred conformant Kubernetes cluster without lock-in.
Performance highlights
- Engineered for TTFT and TPS: architecture and runtimes focus on first-token speed and sustained throughput.
- Blackwell advantage: on Akamai Cloud, NVIDIA RTX PRO 6000 Blackwell has demonstrated up to 1.63x higher inference throughput versus H100 in our tests. See the benchmarks.
- Global reach: inference routes across a massive edge footprint to keep experiences responsive everywhere.
Use cases
- Agentic AI and assistants
- Multi-agent orchestration with tool use, RAG, and guardrails enforced at the edge.
- Customer experience and chatbots
- Global, low-latency conversations with streaming responses and policy controls per region.
- Personalization and recommendation
- Real-time recommendations with edge vector search and secure customer data access.
- Automation and decision engines
- High-frequency inference for fraud detection, claims routing, inventory and pricing decisions.
Frequently asked questions
How is Akamai Inference Cloud different from traditional GPU hosting?
It’s purpose-built for inference at the edge. Compute, networking, and security run together on a distributed platform so you can operationalize AI globally with predictable latency, integrated defenses, and controls designed for LLMs and agents — not just raw GPUs.
Who is it for?
- MLOps: automate retraining, deployment, and production monitoring.
- AI engineers: build end-to-end agentic apps using pre-trained or fine-tuned models.
- Agentic system architects: design autonomous systems that reason, plan, and act toward business goals.
How does the edge reduce latency?
Requests are processed closer to users. Akamai’s edge routes each session to the best GPU region and applies AI-aware traffic controls, delivering faster, more consistent responses than centralized inference.
What GPUs and specs are available?
Clusters with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, BlueField-3 DPUs, up to 128 vCPUs, 1,472 GB DRAM, and 8,192 GB NVMe per node. Additional storage tiers and vector databases are available for context-heavy workloads.
What tools and integrations are supported?
Deploy with App Platform and LKE using vLLM, KServe, NVIDIA NIMs, and NeMo. Bring your own models or use OpenAI-compatible APIs, vector databases, and your preferred observability stack.
How do I secure models, data, and APIs?
Enforce model-aware policies (Firewall for AI), WAAP and API protections (App & API Protector, API Security), bot mitigation, and network segmentation. Apply identity, access, and data controls at the edge to protect sensitive information.
How do I get started?
- Talk with us about your use case, model mix, and performance goals. We’ll map workloads to the right GPUs and deployment method and help you launch quickly.
- Prefer to self-serve? Create a cloud account and start building.
Resources
Get started
Book your AI consultation today
AI is moving from the lab to production. Whether you’re optimizing inference, scaling models, or reducing latency, we’ll help you bring AI to life at the edge.
- See how to deploy and scale inference closer to users.
- Learn how edge-native infrastructure improves TTFT/TPS.
- Explore how to cut costs while maintaining enterprise-grade security.
We’ll follow up shortly after you submit the form to schedule time with our team.