Code · Backend

Open-weight LLM stack

RecommendedLlama for the everyday, Mistral for European data, Claude as the eval anchor

Closed-weight APIs are the right default for most teams. They're not the right default for a regulated industry, a privacy-led product, or anyone whose customers ask 'where does this data go'. Llama and Mistral are open-weight families that ship near-frontier quality with the receipts. Use Claude on a small eval set to confirm the open-weight model holds up on YOUR task.

· 5 months ago
CODEADVANCEDAdvancedFrom $15/mo
MetaAIMeta AIMistralMistralClaude
The stack
MetaAI
Meta AI
Default open-weight model

Llama 3.x and 4 cover most general tasks. Hosted on Groq for fast inference, on Together for breadth, or self-hosted on your own GPUs via Ollama / vLLM. Open weights mean you keep the option to leave any provider.

Free chat · Meta One Core $7.99/mo · Premium $19.99/moAlts: Mistral, Qwen
Mistral
Mistral
EU data residency + code

Mistral Large for general, Codestral for code. EU-headquartered, EU data centers available. The right pick when the legal team is in the room.

Free chat · $14.99/mo Pro · API meteredAlts: Meta AI
Claude
Eval anchor + spot-check

Use Claude on a 30 to 50-row eval to confirm your open-weight pick holds quality on your specific task. Re-run quarterly or whenever you swap providers.

Free · $20/mo Pro · $100/mo Max 5x · $200/mo Ultra · Sonnet API $2/$10 per M tokens (input/output)Alts: ChatGPT
Real monthly cost
small
$15/mo
Hosted inference, low volume
  • meta-ai$10 (Groq / Together API)
  • mistralFree Le Chat
  • claude$5 (eval)
medium
$170/mo
Hosted inference, real volume
  • meta-ai$100 (Groq scale tier)
  • mistral$50 API
  • claude$20 (eval Pro)
heavy
$1,180/mo
Self-hosted on rented GPUs
  • meta-ai$1,100 (rented GPU box for Llama)
  • mistral$0 (also on the same box)
  • claude$80 (regular eval + drift checks)
Recent changes to this stack
featureOCT 29

Usage and Cost Analytics API launched in beta to track token consumption and costs

Anthropic released the Usage and Cost Analytics API in beta, enabling organizations to monitor token usage and costs across Claude API consumption with filtering and grouping by dimensions like model, workspace, and API key. The API provides /v1/organizations/analytics/usage and /v1/organizations/analytics/cost endpoints. Legacy usage reporting endpoints remain supported.

docs.claude.comagentaffects Full-stack with Lovable, Cloud IDE with Replit, UI-first with v0 +31
featureOCT 23

Claude Tag API released in beta for Claude Enterprise organizations on Slack

The Claude Tag API is now available in beta for Claude Enterprise organizations, providing endpoints to read and configure Claude Tag settings on Slack channels and workspaces with Access bundle support. The API requires Admin API keys with read:claude_tag or write:claude_tag scope. This feature is exclusive to Claude Enterprise.

docs.claude.comagentaffects Full-stack with Lovable, Cloud IDE with Replit, UI-first with v0 +31
Workflow
  1. 1
    Pick the model for the use caseMeta AI

    Llama 3.x for general; Llama 4 for the heavy reasoning tasks; Mistral Large for EU + nuanced French/Spanish; Codestral when the task is code generation specifically.

  2. 2
    Pick the hostMeta AI

    Don't self-host until you have to. Groq is fastest for Llama; Together is broadest. Mistral La Plateforme for hosted Mistral. Self-host only when data-residency or cost-at-scale forces you.

  3. 3
    Build a 50-row eval with ClaudeClaude

    Use Claude (or GPT) to draft inputs + expected outputs. Hand-edit to remove ambiguity. This eval is the only way to know your open-weight pick is truly good enough.

    Prompt · Eval scaffold for an open-weight LLM swap
    I'm evaluating whether to use {{Llama 3.3 70B / Mistral Large / etc.}} in production for {{task description}}. Help me build the eval set.

Task:
"""
{{task: input shape, expected output shape, definition of correct}}
"""

Output:
1. **Eval rows** (50) — table of {input, expected output, why this case matters}. Cover the easy cases, the long-tail edge cases, and 5 deliberately adversarial inputs.
2. **Scoring rubric** — exactly how I score actual outputs against expected. Define partial credit if useful.
3. **Pass bar** — what % score against the rubric should I require before swapping production traffic to the open-weight model?
4. **What I should re-run quarterly** — the 5 to 10 most-important rows that catch regression.

Be ruthless about the adversarial cases. The whole point of evals is the model failing on things YOUR users will throw at it.
  4. 4
    Smoke-test on the evalMistral

    Run the eval on Claude AND your open-weight pick. If the open-weight model scores within 5% (or your domain's tolerance), ship it. If not, narrow scope or pick a different open-weight model.

  5. 5
    Production + monthly drift checkClaude

    Ship to prod. Re-run the eval monthly to catch silent quality drift when the host updates the model.

What it produced
Healthcare-adjacent SaaS, EU customers

Could not use closed-weight US-hosted APIs for the customer-data path. Built on Mistral La Plateforme (EU region) for the prod calls, kept Claude for the eval rig. Internal compliance team signed off in week 2 because the audit trail (model card + EU hosting) was clean.

Common pitfalls
Self-hosting before you have to

Renting a GPU box for one app is rarely a good trade. Hosted Llama (Groq, Together) covers most cases at lower cost. Self-host only when audit / latency / cost-at-scale forces it.

Treating 'open weights' as a feature without a use case

Open weights only matter if you'd actually self-host or audit. If you'll never inspect them, the closed-weight APIs are usually still the right call. Be honest about which camp you're in.

Forgetting the eval drift

Hosted open-weight models update quietly. The monthly eval is the cheapest way to catch a regression before a customer does.

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