VAST Data's 2026 Inference Predictions: Agents, Cost Fluidity, and the End of GPU-Only Thinking

VAST Data, the storage and data-platform company, published a blog post on December 30, 2025, making a case that 2026 is the year AI inference moves from prototype to production. The post, written by technology storyteller Derrick Harris, lays out five trends the company expects to define the year. VAST Data has a commercial interest in being right — it sells infrastructure for exactly this shift — but the trends it identifies are worth examining because they reflect what a vendor working with real enterprise and government customers is hearing on the ground.

The core argument is simple. 2025 was the year of massive infrastructure buildout for AI training. 2026 is the year that infrastructure starts earning its keep through inference workloads. Not just chatbot-style prompts and image generation, but what VAST calls “mission-critical workloads” where generative AI becomes a core component of enterprise and scientific application stacks.

The agent moment, for real this time

VAST’s first trend is the emergence of AI agents operating at scale. The company points to evidence in ecommerce, where agentic protocols are enabling direct purchases from chat sessions, and to the Linux Foundation’s Agentic AI Foundation, which houses Anthropic’s MCP and OpenAI’s AGENTS.md. But the more concrete example comes from VAST’s own customers. The company describes a large city government using AI agents for video analysis: each new video file triggers a pipeline that chunks, vectorizes, and analyzes surveillance footage in real time, then suggests or even executes actions like alerting emergency services.

VAST also cites work with partners Leidos and NVIDIA on cybersecurity agents that analyze network traffic in real time. The pitch is familiar — humans cannot keep up with the volume of suspicious activity, so agents triage threats and free up human experts for deeper work. What is different this time is the infrastructure claim. VAST argues its unique data architecture makes agentic deployments at this scale possible, a claim that is both self-serving and worth watching. If agents require a fundamentally different data plumbing than traditional applications, the winners in infrastructure will be those who build for that reality.

The fluid cost of AI operations

VAST’s second trend addresses the economics of running inference. Per-token costs are falling, but larger context windows and reasoning models mean more tokens per task. The net effect on total cost is uncertain. VAST’s solution is to offload work from expensive GPU memory to cheaper SSD storage, specifically the KV cache mechanism that fills up fast during token generation. The company claims this frees GPU resources while maintaining performance at a much lower price point.

The broader point is that inference cost is not a fixed number. It depends on model architecture, hardware choice, and workload characteristics. VAST argues its platform lets users switch cloud GPU providers or use multiple providers without data egress fees or performance degradation, effectively creating a commodity market for inference compute. Whether that vision holds depends on how sticky the major cloud providers can make their GPU ecosystems.

The end of GPU monoculture

The third trend is the most interesting. VAST argues that two ground truths of AI are fading: that transformer models are king and that the highest-tier GPUs are always the best. The company cites Hugging Face CEO Clem Delangue’s suggestion that the industry might be experiencing an LLM bubble, not an AI bubble. The implication is that other model architectures — video-language models, multimodal models, hybrid approaches — will gain ground.

VAST points to NVIDIA’s own Nemotron 3 models as evidence. These are hybrid Mamba-transformer LLMs that combine the strengths of each architecture. The Nemotron 3 Nano model can run on a wide range of NVIDIA GPU architectures, not just the high-end gear typically required for training. This is a subtle but important signal. NVIDIA itself is building models that do not require its most expensive hardware, and VAST is working with a wide range of hardware partners to ensure compatibility. The era of “buy the biggest GPU or nothing” is ending.

The data stack crystallizes

VAST’s fourth trend is about data architecture. The company argues that existing data infrastructure — Kafka, Spark, data warehouses, multiple databases — was built for batch processing and web applications, not real-time AI. Production inference workloads, especially agentic ones, need fast access to historical data, freshly vectorized data for RAG, and the ability to process massive logs in real time.

VAST’s answer is its own platform, built around DataEngine, SyncEngine, and InsightEngine. The company’s argument is that generative AI should not be “weighed down by data infrastructure from the Hadoop era.” This is a fair critique. Many organizations are stitching together legacy systems to support AI workloads, and the complexity is a real bottleneck. Whether VAST’s unified platform is the right answer or just another vendor pitch is something the market will decide in 2026.

Boring work takes center stage

The fifth trend is the least glamorous and potentially the most important. VAST argues that production AI workloads must be secure, highly available, and observable — the same requirements as any production application, but harder to achieve with agents and LLMs in the mix. A fleet of agents communicating with each other, with data stores, and with external tools creates a mountain of events to log and analyze. Different agents may need different access levels to different data, and enforcing those rules is harder when the users are autonomous software.

VAST’s solution is consolidation onto a single data platform designed for multitenancy and data security at AI scale. The company argues that adding more shards and tools introduces more failure points and higher latency. The implication is that the winners in AI infrastructure will be those who reduce complexity, not add to it.

What this means for builders

VAST Data’s predictions are self-serving, but they are not wrong. The company is describing a world where inference becomes the dominant compute workload, agents become real operational tools, and data infrastructure becomes the critical bottleneck. For builders, the key takeaway is that 2026 will not be about who has the biggest training cluster. It will be about who can run inference reliably, cost-effectively, and at scale.

The shift from training to inference changes the hardware calculus. Training favors the largest GPUs and the most exotic interconnects. Inference favors availability, latency, and cost per token. That opens the door for a wider range of hardware — older GPU generations, specialized inference chips, and architectures that trade raw FLOPS for efficiency. It also opens the door for infrastructure vendors like VAST that claim to make that hardware work better together.

The question VAST does not answer is whether its unified platform is the right approach or just another proprietary stack in a market that will eventually standardize. But the company is asking the right questions about cost, architecture, and operations. For builders planning their 2026 inference strategy, those questions are worth answering, whether or not VAST’s platform is the answer.