Lytix

What do they actually do

Lytix provides a drop-in proxy/gateway and web dashboard for teams running LLMs in production. You point your existing model calls at Lytix, which logs each request/response as a session, lets you route traffic across different providers/models, and surfaces latency, cost, and errors in one place. The dashboard/lab and documented SDKs/integrations make it possible to add this without major code changes Lytix docs, YC launch.

Teams can define custom, domain-specific evaluations and alerts, and enforce guardrails that block outputs or trigger fallbacks when checks fail. Lytix also supports caching (including a self-hostable cache), a playground for testing prompts/models, and integrations with telemetry tools. They publish core libraries (optimodel, lytix-js, lytix-py) in their GitHub org, with optimodel focused on provider-agnostic guardrails and routing logic Lytix docs, Lytix GitHub, optimodel.

Who are their target customer(s)

• Product teams shipping LLM features: They struggle to measure per-task quality and get alerted on regressions, so model changes can break user experience before anyone notices.
• ML/ML-ops engineers owning production LLMs: They need to prevent hallucinations, toxic outputs, and data leaks across providers; building/maintaining guardrails and fallbacks in-house is time-consuming and brittle.
• Engineering or finance owners managing inference cost: They need routing and caching to avoid overpaying and the ability to switch providers quickly to save money, but lack centralized visibility and control.
• Small teams/startups with limited bandwidth: They don’t have time to stitch together logging, tests, dashboards, and alerts; maintaining bespoke pipelines slows product work.
• Compliance, trust & safety, and product managers: They must enforce business rules and regulatory checks and need reliable blocking, similarity checks, and audit trails across all model providers.

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

• First 10: Pilot with YC/startup peers and early LLM teams; offer hands-on help to route their model calls through Lytix and measure changes in errors/cost within a week to produce initial case studies.
• First 50: Lean into product-led growth with ready-made templates (guardrails, custom evals, cost routing), sample repos, and community support; enable self-serve trials that upgrade after measurable value is shown.
• First 100: Add partner integrations/marketplace listings and paid pilot contracts, offer a secure self-hosted cache option, and hire 1–2 sales/CS reps to convert mid-market pilots while publishing results and referrals.

What is the rough total addressable market

Top-down context:

Relevant spend sits in AI observability and LLMOps, with reports sizing AI-in-observability at about $1.4B in 2023 and LLMOps in the ~$1–4B range, while GenAI software/platforms are forecast to reach ~$58.5B by 2028 Market.us, Dataintelo, Omdia/AWS reprint.

Bottom-up calculation:

If 10k–20k teams operate production LLM features and spend $5k–$30k ARR on routing/observability/guardrails, that implies a $50M–$600M serviceable bucket; scaling to ~50k teams at $10k–$50k ARR suggests $0.5B–$2.5B over time. These ranges reflect adoption growth and typical mid-market tooling budgets.

Assumptions:

• Number of active LLM-app teams grows to ~10k–50k globally over the next few years.
• Per-team ARR for this layer ranges from ~$5k (startup) to ~$50k (mid-market).
• Spend is primarily software (not including model/provider or compute costs).

Who are some of their notable competitors

• LangSmith (LangChain): Developer-focused observability and evaluation tied to LangChain traces and workflows; SDK-first rather than a proxy/gateway drop-in LangSmith docs.
• Helicone: Open-source AI gateway + observability with a one-line proxy, routing, caching, and built-in logging; functionally close on proxyed observability, with an OSS-first positioning Helicone docs.
• Robust Intelligence (Cisco AI Defense): Focuses on adversarial testing and real-time AI firewall protection; emphasizes security/red-teaming over the per-task evals + cost routing workflow Robust Intelligence docs.
• Arize: Enterprise LLM/ML observability and continuous evaluations with large-scale tracing and dashboards; deeper analytics focus versus a lightweight proxy/guardrails layer Arize docs.
• Fiddler AI: Enterprise observability and explainability with guardrails, risk, and governance tooling; serves compliance-heavy use cases, beyond a simple drop-in gateway Fiddler product pages.