For the last two years, the AI conversation has been dominated by model training: bigger models, more parameters, record-breaking benchmarks.
But according to a January 2026 analysis from Forbes Technology Council, the real commercial transformation of software isn’t happening in training at all.
It’s happening in inference.
And that shift has major consequences for anyone buying, building, or betting on SaaS products.
Training Gets Headlines. Inference Creates Businesses.
The article makes a critical distinction that many non-technical leaders miss:
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Training is where models learn
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Inference is where models work
Inference is the moment when AI:
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Serves users
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Responds to queries
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Powers workflows
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Incurs real, recurring costs
As the author puts it:
Training drives innovation. Inference determines how broadly and efficiently that innovation can be deployed.
In other words: models don’t earn money — inference does.
This is why the current generation of SaaS products looks fundamentally different from those of even three years ago.
Why This Moment Is Different for SaaS
Recent advances in foundation models (GPT-5, Llama 4, DeepSeek V3) have crossed a threshold where AI-native products are no longer experimental.
Enterprises are now:
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Scaling AI across real workflows
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Embedding AI into CRM, customer support, engineering, and operations
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Moving beyond pilots into production
The article strongly warns against scattered “proofs of concept.” Instead, it argues that companies should prioritize use cases with proven ROI, such as:
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Coding assistants
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Customer support automation
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CRM enrichment
This mirrors the early SaaS era: the winners weren’t the most novel tools — they were the ones that scaled reliably and economically.
The New Economics of SaaS: Inference Is the Cost Center
Here’s the shock factor most SaaS buyers don’t yet appreciate:
Training is a one-time cost. Inference is a variable cost that scales forever.
Every user action, every AI-assisted workflow, every automated decision triggers inference.
That means:
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Unit economics now depend on inference efficiency
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Margins can collapse if inference isn’t optimized
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Two products with similar features can have wildly different cost structures
This is where the article delivers a subtle but powerful insight: The “best” model is often the wrong model.
A smaller, fine-tuned, task-specific model can:
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Perform better on a targeted workflow
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Cost dramatically less to run
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Scale more predictably
For SaaS buyers, this introduces a new risk: You can’t judge AI products by model branding alone anymore.
Infrastructure Choices Now Shape Product Viability
Inference isn’t uniform. The article explains that different workloads have radically different requirements:
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Real-time chat or fraud detection → milliseconds matter
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Legal analysis or reporting → minutes or hours are acceptable
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Hybrid workflows → multiple performance tiers in one product
Modern SaaS platforms increasingly run multiple inference strategies simultaneously, mixing:
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Auto-scaling GPU clusters
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Batch processing
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Cached responses
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Older or spot hardware
This creates huge efficiency opportunities — but also complexity.
From a buyer’s perspective, this means:
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Two vendors can promise “AI-powered” features
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One may be economically sustainable
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The other may quietly bleed cash as usage grows
AI Is No Longer Just a Feature — It’s a Stack
One of the most important takeaways from the article is that inference is now a full-stack problem, not a single technical decision.
A production-grade AI SaaS product spans three layers:
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Token Generation (raw compute, hardware, inference libraries)
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Orchestration (caching, load balancing, autoscaling, throughput optimization)
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Developer & Operational Experience (observability, reliability, evaluation, deployment workflows)
SaaS companies that coordinate all three layers win on:
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Cost
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Latency
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Reliability
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Long-term scalability
Those that don’t… eventually hit a wall.
What This Means for SaaS Buyers (Not Builders)
Here’s the uncomfortable truth for customers of SaaS products:
You are now indirectly exposed to your vendors’ inference decisions.
If a SaaS provider:
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Picks the wrong model
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Over-relies on closed ecosystems
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Fails to optimize inference pipelines
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Doesn’t understand their own unit economics
You may experience:
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Sudden price increases
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Usage caps or throttling
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Feature rollbacks
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Slower performance at scale
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Vendor instability or acquisition risk
These are not theoretical risks — they are already emerging in AI-heavy products.
Why Decision Speed Now Matters More Than Ever
The article makes it clear: We are not just watching a new market emerge — we are watching how software gets built being rewired in real time.
For SaaS customers, this creates a new challenge:
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The market is moving faster than traditional evaluation cycles
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Architecture choices today lock in economics for years
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Waiting for “clarity” often means inheriting someone else’s mistakes
This is where connection, intelligence, and context become strategic advantages.
Where G2 Connections Fits — Without the Hype
If inference economics define whether AI-powered SaaS succeeds or fails, then customers need better visibility before committing.
Not better demos. Not bigger claims.
But:
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Clear insight into how AI features are delivered
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Understanding of scalability tradeoffs
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Awareness of which vendors are built for production vs experimentation
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Faster access to real signals, not marketing narratives
If we can’t slow the pace of AI innovation, we have to shorten the decision loop.
G2 Connections exists in that gap — helping customers:
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Navigate an increasingly complex SaaS landscape
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Avoid costly misalignment between promise and reality
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Make informed decisions before inference costs, performance constraints, or vendor instability surface downstream
Because in the AI era, the biggest risk isn’t choosing the wrong tool.
It’s choosing too late — or choosing without seeing the full picture.
Source
Roman Chernin, “How AI Inference Can Unlock The Next Generation Of SaaS,” Forbes Technology Council, January 20, 2026.
