Vector databases: pgvector vs dedicated
Storing embeddings at scale. This post digs into the topic, drawing on recent discussion from Hacker News, Reddit, and the wider community.
The conversation that prompted this post is linked in the sources. I have tried to capture the substance of the discussion rather than just summarize it, because the comments are usually where the real insight lives.
What happened
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Why it matters
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Practical takeaways
- Prompt engineering is real engineering.
- The hardest part of building with LLMs is not the model call.
- Retrieval quality dominates RAG quality.
- Model routing sends simple queries to cheap models and hard ones to expensive models.
- Large language models are best understood as very good pattern matchers with a large but finite context.
- Prompt engineering is real engineering.
- Large language models are best understood as very good pattern matchers with a large but finite context.
- Caching is the single biggest cost lever for LLM applications.
- Structured output via JSON schema constraints makes LLMs usable as components in larger systems.
- Structured output via JSON schema constraints makes LLMs usable as components in larger systems.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Sources
This post is informed by discussion on github.com and related threads on Hacker News and Reddit. Primary source: https://github.com/pgvector/pgvector.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.