Choosing an Open LLM for Enterprise in 2024

Updated: 2026-07-12

A year ago, "open LLM for enterprise" basically meant Llama 2. Today the field is far richer: Mistral 7B and Mixtral 8x7B, Qwen 1.5, Yi 34B, DeepSeek, Phi-2, and more. The option fan makes choosing harder, not easier. This article gives enterprise criteria for deciding, beyond the trendy benchmarks.

Key Takeaways

• Licence comes first, not benchmark: choosing a model and discovering the licence does not allow commercial use is mistake number one.

• Mistral 7B and Mixtral 8x7B are sweet spots for most mid-size enterprises given their performance, size, and Apache 2.0 licence.

• For 80% of enterprise cases, prompt engineering + RAG is sufficient without fine-tuning.

• Self-hosting is only justified with high constant volume, data that cannot leave, or strict compliance.

• Academic benchmarks are useful but insufficient: always complement with your own evaluation on real use cases.

The Candidates

Open models worth serious evaluation at the time of writing:

• Llama 2^[1] (Meta): 7B/13B/70B. Solid base, licence restrictive for large commercial use.

• Mistral 7B^[2] and Mixtral 8x7B^[3] (Mistral AI): Apache 2.0, excellent performance/size ratio.

• Qwen 1.5^[4] (Alibaba): 0.5B up to 72B, strong multilingual support.

• Yi^[5] (01.AI): 6B/34B, good in Chinese and English, permissive commercial licence.

• DeepSeek^[6]: various sizes, very strong in code and maths.

• Phi-2^[7] (Microsoft): 2.7B: small but competitive in reasoning.

• CodeLlama^[8] (Meta): Llama fine-tuned for code, various sizes.

The frontier moves fast: these are the serious contenders at the time of writing.

Licence: first, not last

Mistake number one: choose by benchmark then discover the licence does not allow commercial use. Key situations:

• Apache 2.0 (Mistral, Mixtral, Yi): free for commercial use without size restrictions.

• MIT (Phi-2): free, research-oriented.

• Llama 2 License: allows commercial use with clauses: restrictions above 700M MAU, and it must display "Built with Llama 2".

• Tongyi Qianwen License (Qwen): allows commercial use under thresholds, similar to Llama 2.

• "Community" licences: sometimes yes, sometimes no. Read the full text before committing.

For large enterprise, Apache 2.0 is the safest bet. For mid-size enterprise, Meta’s or Alibaba’s licences are acceptable once you meet the thresholds.

Benchmarks: useful but limited

MMLU, HellaSwag, and GSM8K are the most widely known publicly. Their known limitations:

• Contamination: many models may have seen the tests during training.

• Gaming: some models are explicitly optimised for specific benchmarks.

• Distance from real use: "reasoning about trivia" is not equivalent to "solving my business problem".

Academic benchmarks are useful but insufficient. Always complement with your own evaluations on real use cases: a model that performs well on MMLU can perform poorly on your domain documents.

Required Hardware

Hardware filters options fast. Rough figures for inference (not training):

Practical implications: a quantised 7B (via llama.cpp) fits on a consumer RTX 4090 (24 GB) or a 16 GB M2/M3 laptop; Mixtral 8x7B needs an A100 40 GB; Llama 70B needs an A100 80 GB or two A100 40 GB cards. For real self-hosting, the 7B and 34B models are the most practical. Anything from 70B up needs expensive infrastructure.

Quality by Use Category

Without being exhaustive, impressions from real cases:

For code: DeepSeek Coder and CodeLlama dominate; Mixtral 8x7B is competitive at a smaller scale.

For summarisation and text analysis: Mistral 7B punches above its size; Mixtral 8x7B gets close to GPT-3.5; Yi 34B is competitive with long contexts.

For Spanish and Portuguese: Qwen 1.5 and Mixtral are the strongest; Llama 2 works, but its lack of multilingual training shows.

For maths and reasoning: DeepSeek Math (specialised) is exceptional; Yi 34B and Mixtral are acceptable.

5-10% differences on academic benchmarks are irrelevant if the model does not perform well in your specific domain.

Inference Serving

The mature options for serving models:

• vLLM^[9]: maximum GPU throughput (see also serving vLLM in production).

• TGI^[10] (Hugging Face): tight integration with the HF ecosystem.

• llama.cpp^[11]: CPU and Apple Silicon.

• Ollama^[12]: built on llama.cpp, a polished developer experience.

• LMDeploy^[13]: a solid-performing alternative.

For enterprise: vLLM for throughput, Ollama for individual developers.

Fine-tuning: when yes, when no

In most enterprise cases, you don’t need fine-tuning. Prompt engineering + RAG covers 80%. Fine-tuning makes sense when you have very domain-specific data (medicine, technical legal, internal company knowledge), when latency is critical and a long prompt does not fit, or when you need a specific tone or voice that is impossible to achieve with prompting.

The most useful techniques are LoRA / QLoRA (efficient fine-tuning with little GPU) and DPO / RLHF (for aligning the model with preferences). Start with prompting and reach for fine-tuning only once prompting demonstrably falls short.

This flow combines well with the patterns from LLM proxies for managing several models without duplicating logic.

Self-hosting vs Open API

Three deployment models:

• Self-hosting: you download the weights and run them on your own infrastructure. Full control, fixed hardware cost.

• Provider API: Mistral AI, Together.ai, Anyscale, Replicate serve open models pay-per-token. No hardware, variable cost.

• Hybrid: sensitive data on-prem, the rest through an API.

Self-hosting is only justified if volume is high and constant, if sensitive data cannot leave, if local latency is critical, or if there is strict compliance. For 80% of cases, an open-provider API (Mistral, Anyscale) is more efficient.

Decision Checklist

Seven questions that filter the options when choosing an open LLM for enterprise:

• Licence: compatible with your use and scale?

• Size / hardware: fits your infrastructure budget?

• Languages: covers your users’ languages?

• Own benchmark: performs on your real use cases?

• Provider: self-host, open API, or hybrid?

• Security: alignment sufficient or additional layers needed?

• Roadmap: does the project have active continuity?

With that filter, options reduce to 2-3, and your own testing decides from there.

Conclusion

The open-LLM ecosystem is mature enough that companies of almost any size find a viable model. Mistral 7B and Mixtral 8x7B are sweet spots for most. Llama 2 remains relevant, especially at 70B. For specialised domains (code, multilingual), DeepSeek, Qwen, and Yi provide valid alternatives. The decision should not be "which has the best MMLU" but "which fits my licence, hardware, language, and real cases".

This article is also available in Spanish: Elegir un LLM abierto para empresa en 2024.

Sources

1. Llama 2
2. Mistral 7B
3. Mixtral 8x7B
4. Qwen 1.5
5. Yi
6. DeepSeek
7. Phi-2
8. CodeLlama
9. vLLM
10. TGI
11. llama.cpp
12. Ollama
13. LMDeploy