Culpa vs Literal AI

Literal AI got the architecture right and handed you the maintenance. Its cost tracking asked you to configure, per model, a Pattern as a regular expression such as gpt-4o followed by a wildcard, an Input Price in USD per million tokens, an Output Price in the same unit, and optionally a Period with start and end dates that its own docs describe as useful for handling price changes. That's an effective-dated price book, which is the correct shape and the same one Culpa uses. Two differences decide how it behaves in practice. The rates were yours to enter and yours to keep current, so a page of settings quietly became the thing your cost reporting depended on. And matching by regular expression means a pattern written for one family silently catches the next model that shares its prefix, at last year's rate. The dashboard then showed total cost, cost per conversation and a breakdown by model. The company discontinued the product with service available until 2025-10-31, saying it couldn't differentiate enough in a competitive market to build sustainable revenue.

• Your model rates live in a settings page and nobody remembers who last updated them.
• A new model in a familiar family started billing at an older model's rate.
• Cost reporting was right last quarter and nobody can say whether it still is.
• You have cost per conversation and no way to set it against what that customer pays.
• A price change landed mid-month and your totals reflect one rate for the whole period.

1. Find where your model rates are stored and who last changed them.
2. Add a new model in an existing family and check which rate it picked up.
3. Recompute a month from six months ago and see whether it matches what you reported then.
4. Ask your tool for cost per customer against what that customer pays you.
5. Check what happens to pricing on the day a provider changes a rate mid-month.

Literal AI matched models by regular expression, its own docs giving gpt-4o with a wildcard as the example for all GPT-4o variants. Model families gain members and the new member rarely shares the old rate. Take 10M input tokens billed under a pattern written for a $2.50 per million tier when the call actually ran on a $5.00 per million model that the pattern also matched. Rates are real points from the price book effective 2026-07-02, and the volume is modelled.

A wrong rate is worse than a missing one, because a missing rate is visible. Culpa matches exact model ids and records cost_complete as false when a model is absent from the book, so an unpriced call shows up as unpriced instead of silently taking a neighbour's rate.

Literal AI is the closest thing in this cluster to Culpa's own design, which makes the comparison narrow and specific rather than sweeping. Both price calls from a rate table with effective dates. Both report cost broken down by model and by conversation. Three things differ. Culpa ships the price book versioned and maintained, extracted from the ledger's own migrations, so nobody's settings page is the source of truth. Culpa matches exact model identifiers and marks a call incomplete when the model is unknown, rather than matching a prefix and quietly pricing at whatever the pattern caught. And Culpa carries the revenue each customer pays you, so cost per conversation becomes margin per customer, which is the question Literal AI's dashboard couldn't reach. The product was discontinued with service until 2025-10-31, so this compares a design rather than a purchase.

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