AI Developer Tooling & Middleware
The picks and shovels for everyone building on top of foundation models.
What this layer does
Between the raw model API and a shippable application sits a stack of glue: ways to store and search embeddings, orchestrate multi-step agents, evaluate model output, observe production behavior, fine-tune on custom data, and label or synthesize training data. This is the AI equivalent of the data infrastructure boom of the 2010s — lots of competing private startups, frequent commoditization, but a few will own real markets.
Almost all private. Public exposure is mainly via Databricks/Snowflake-adjacent tooling and the few platform incumbents (MongoDB, Elastic) that have credible vector offerings.
Sub-categories
Storing and searching embeddings. The RAG backbone.
Libraries for chaining model calls, tool use, memory, multi-agent coordination.
Measuring whether an AI system is actually working in prod. The Datadog of LLMs.
Hosted fine-tuning of open or proprietary models on customer data.
Curating and generating the training data that frontier and post-training runs depend on.
Prompt-injection defense, PII scrubbing, content moderation, jailbreak testing, red-teaming.
Smaller models sold as utilities — embeddings, rerankers, classifiers, OCR, speech.
Where open-weight models live and get pulled from.