If you are evaluating LLM providers for your next product, you probably have a shortlist: OpenAI, Anthropic, Mistral, maybe a self-hosted option. But most teams make the decision on vibes, benchmarks, or blog posts — not on the operational reality of running on a single provider for 12+ months.
Here is what most indie hackers and CTOs miss:
- Benchmarks do not match your workload. Public leaderboards test against curated datasets. Your actual prompt mix — long-context, tool-use, structured output — may behave very differently.
- Provider roadmaps shift under you. A model deprecation, API pricing change, or context window cut can blow up your unit economics overnight.
- Support gaps appear late. You discover rate limits, latency spikes, or model behavior changes at 2 a.m. on launch night.
The due diligence most people skip
Before you lock in a provider, do these four things:
- Build a representative eval set. Use your actual prompts and expected input/output lengths, not MMLU or HumanEval.
- Calculate real token costs over 6 months. Project your usage growth, not just your current headcount.
- Check model stability history. How many major API version breaks has the provider shipped in the last year?
- Audit fine-tuning and feature flags. If you need system prompts, tool use, or JSON mode, test them under load before signing up.
What second-source intelligence looks like
Deep, structured provider briefings exist — but most are locked behind analyst reports that cost thousands. For indie hackers and bootstrapped teams, that is out of reach.
The practical middle ground: a compact, single-provider intelligence brief that covers pricing history, release cadence, known failure modes, and head-to-head comparisons against competitors.
Getting started without the guesswork
You do not need an expensive consultant. Start with this:
- Spawn a throwaway project and run 500 real calls through each provider.
- Log latency P50/P95, error rates, and actual token spend.
- Build a one-page decision matrix weighted by your actual priorities (cost, latency, context length, function calling).
If you want a shortcut, structured intelligence packages exist for teams that do not have two weeks to burn on eval.
Bottom line
Provider selection is a bet on the next 12 months of your product. Treat it like an infrastructure decision, not a feature toggle. Know the pricing, the stability track record, and the actual performance on your problems before you ship.
We publish structured intelligence briefs on leading AI providers to save you the eval cycle. The Anthropic Intel Brief covers Anthropic's 65B-class models in depth — pricing history, API behavior, and head-to-head comparisons with GPT-4o and Gemini. £9, no subscription.
Also built: AI Coding Cost Tracker (£5 one-time) and AI Phone Service (from £99/mo) — so you can track AI spend and never miss a lead call.
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