Keywords AI

What do they actually do

Keywords AI is an API gateway for large language model (LLM) calls with built-in monitoring. Teams route their model requests through Keywords AI’s API to access 250+ models via one interface (with options like fallbacks and cost-aware routing), while capturing per-request logs, costs, latency, and traces for agent workflows (integration overview, platform overview).

It provides a UI for prompt editing and versioning, plus automated evaluations that score outputs against datasets or rules using LLM or human-in-the-loop judges. It also includes user analytics for production usage and behavior (platform overview, users analytics).

Access is offered via free and paid tiers, with higher retention, seats, and enterprise options like SOC 2/HIPAA readiness and on‑prem deployment available at the enterprise level (pricing). The company positions itself for developer and product teams building LLM features and cites usage by over 40 YC AI startups (cookbook overview).

Who are their target customer(s)

• Early-stage AI startup engineers building LLM features: They need a quick way to connect multiple models, use their own provider keys, see per-request costs, and iterate on prompts without building custom tooling (integration overview).
• Product and engineering teams at growth-stage apps adding chat/agents: They worry about quality regressions and user-facing errors when changing models or scaling; they need prompt versioning, experiments, and detailed logs to catch breakages (platform overview).
• ML/Infrastructure engineers owning reliability and cost: They must route traffic across providers, manage fallbacks and cost routing, and get traces for complex agents to diagnose latency and failures (integration overview, platform overview).
• Security/compliance/IT teams in regulated orgs: They need controls for data privacy, retention, attestations, and deployment options (SOC 2/HIPAA, on‑prem) plus clear export paths for audit needs (pricing).
• PMs/QA/Ops running ongoing model quality checks: They lack a repeatable way to evaluate outputs across model versions and need testsets, evaluators, and experiments to measure regressions without manual review (platform overview).

How would they acquire their first 10, 50, and 100 customers

• First 10: Leverage YC and founder networks for warm intros and run hands‑on pilots that route existing model calls through the gateway so teams immediately see logs, costs, and prompt/eval tooling (cookbook overview, integration overview).
• First 50: Publish concise tutorials and case studies showing “two lines of code” setup, engage developer communities, and co-market new framework integrations (e.g., Vercel AI SDK) to pull in users where they already build (integration overview, changelog).
• First 100: Instrument trials to quantify ROI (cost visibility, fewer regressions), hire a growth/sales lead to target growth/infra teams, and offer short enterprise pilots with retention/SSO/on‑prem upsells to convert larger accounts (pricing).

What is the rough total addressable market

Top-down context:

Targets software teams building and operating LLM features across SaaS and consumer apps—an expanding segment of dev tooling that sits between model providers and application observability.

Bottom-up calculation:

If 20,000 teams adopt LLM features in the next few years and an average team spends $5k–$12k/year on gateway, observability, and evals, the practical near-term TAM is roughly $100M–$240M, with additional upside from enterprise contracts.

Assumptions:

• 20,000 suitable teams globally over the next 3–5 years (startups to mid-market)
• Average annual spend $5k–$12k/team across seats, retention, and eval usage; enterprises higher
• Expansion from enterprise features (SSO, SOC2/HIPAA, on‑prem) increases ARPU over time

Who are some of their notable competitors

• LangSmith: LangChain’s observability and evals tool captures runs and agent steps with integrated prompt tooling; strongest fit for teams already on LangChain/LangGraph (docs).
• PromptLayer: Prompt management and request logging with versioning and lightweight evals; less focused on multi‑provider routing or enterprise governance (product, docs).
• OpenAI Evals: Open-source framework for automated evaluations and benchmarks; useful alongside a stack but not a unified API gateway or multi‑provider router (guide, GitHub).
• Arize AI: Enterprise ML observability and evaluation with strong analytics and monitoring; less focused on being a routing gateway across many LLM providers (capabilities).
• Robust Intelligence: Automated robustness testing, continuous risk monitoring, and model governance; competes on safety/eval/governance rather than prompt versioning or multi‑provider routing (docs).