OpenAI API cost: ~$2/million tokens
OpenAI API pricing is approximately $2 per million tokens, while open-source alternatives like Llama can reduce this to fractions of a cent.
Snapshot · The MongoDB Podcast
Don't Build Your Own AI (Unless You Have To)
Explore episode
Mar 6, 2026
Where this was said
Off-the-Shelf AI Maturity and the Commoditisation of LLMs
At 22:27 · chapter starts 22:20
Akshaya paints a landscape where the case for building your own model is eroding almost by the day. The gap between GPT-4 and Llama's 70-billion-parameter open-source model is narrowing to the point where a self-hosted inference node can produce comparable results for many use cases. Security frameworks that once took years to mature are now shipped as standard in enterprise-grade off-the-shelf products — GDPR compliance, privacy controls, role-based access — because these are competitive table stakes. Model refreshes have gone from annual to weekly on commercial platforms, and daily on Hugging Face for open-source variants. And the per-token cost is compressing: OpenAI charges roughly $2 per million tokens, while running Llama yourself can bring that to fractions of a cent. [1] — Akshaya Murthy "OpenAI API cost: ~$2/million tokens: OpenAI API pricing is approximately $2 per million tokens, while open-source alternatives like Llama c…" 22:27 Most strikingly, the expertise required to work with these systems has inverted: in 2023 you needed PhDs with prompt engineering experience; today a product manager can achieve the same with a drag-and-drop interface. The only genuine exceptions to the 'integrate-first' rule are hyperscale operations where token costs make self-hosting cheaper, and mission-critical verticals like defence, banking, or healthcare where general-purpose training data is simply inadequate.
The Commoditisation of AI Is Already Happening
The gap between commercial LLMs and open-source alternatives is closing fast. OpenAI charges ~$2 per million tokens; Llama gets you there for fractions of a cent. The endgame looks like internet pricing — you'll pay for a utility, not per megabyte.
When Building Your Own AI Actually Makes Sense
At hyperscale — billions of tokens per hour — the per-token cost math flips and self-hosted models win. Mission-critical verticals like defence, banking, and healthcare also need bespoke models because general-purpose LLMs simply weren't trained on the right data.
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