Solutions
Production-grade solutions that turn our open-source packages into deployable AI systems for specific business problems. Pick one, follow the DIY tutorial to see how it's done, download the examples and deploy them on your own infrastructure — for free — or tell us which ones you want customized and deployed.
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10 solutions
vllm-observability-suite-for-smb-ai-operations
Small businesses running vLLM for AI inference struggle to monitor token usage, latency, and cost across multiple agents, leading to overspend and undetected performance regressions.Prebuilt observability stack with OpenTelemetry traces and dashboards for any AI agent using vLLM as the inference backend.
ollama-ai-observability-with-cost-allocation-for-smbs
On-prem LLM deployments lack visibility: IT teams can't tell which departments are consuming tokens, how much each call costs in terms of compute or proxy fees, or where bottlenecks occur. Without observability, they can't optimize or perform internal chargebacks.Gain OpenTelemetry tracing and per-department cost attribution for your Ollama LLM deployments running on-prem or at the edge.
databricks-llm-observability-for-smb-production-ai
Small businesses deploying Databricks-hosted LLMs lack visibility into latency, token usage, and spend across their applications, making it hard to debug slowdowns or control costs.Drop-in OpenTelemetry tracing and cost attribution for every Databricks model call, visualized in Langfuse, so small teams can monitor LLM performance without building custom instrumentation.
vercel-ai-gateway-observability-for-smb-ai-agent-operations
Small businesses deploying AI agents on multiple models through Vercel AI Gateway lack visibility into token consumption, latency, and failure rates across providers. Without centralized monitoring, they cannot pinpoint cost spikes, detect degradation, or enforce budgets, leading to runaway bills and unreliable customer experiences.Unified OpenTelemetry tracing, cost tracking, and performance alerts for every LLM call routed through Vercel AI Gateway.
databricks-llm-observability-suite-for-smb-ai-operations
SMBs using Databricks for AI workloads have no easy way to monitor spending, latency, or error rates across multiple models, leading to bill shock and debugging blind spots.Gain end-to-end visibility into every LLM call on Databricks, from token usage to cost, with ready-made dashboards and alerts.
anthropic-llm-observability-for-smb-ai-operations
Small businesses using Anthropic's Claude models for customer support or content generation lack visibility into token spend, latency patterns, and sudden error rate changes. Without integrated observability, they overspend and cannot diagnose issues before customers complain.Drop-in OpenTelemetry instrumentation that gives SMBs real-time cost, latency, and error insights across all Anthropic API calls, plus pre-built dashboards for Langfuse and Phoenix.
xai-grok-observability-for-smb-ai-workflow-monitoring
SMBs integrating Grok into their workflows lack visibility into how often the model is called, what it costs, and when it fails. Without monitoring, they risk overspending and missing performance degradations.Single-pane monitoring for token usage, latency, errors, and cost across all Grok-powered features in your SMB app.
agnostic-per-tenant-llm-cost-chargeback
The VP of Engineering at a B2B vertical SaaS company needs to bill each SMB customer for the AI features they use. Without per-tenant cost tracking, the company absorbs all LLM expenses, eroding margins. Existing observability tools don't tie token usage to tenant IDs, making chargeback impossible. The team manually approximates costs, leading to billing disputes and lost revenue.Attribute every LLM call to the right tenant and export cost data to your billing system.
langchain-observability-for-smb-ai-workflow-monitoring
SMBs adopting LangChain for multi‑step LLM workflows have no built‑in way to see where latency piles up, which chain step costs the most, or why a particular prompt is bleeding tokens. They either fly blind or pay for a separate SaaS with complex setup.Plug‑and‑play tracing and cost observability for LangChain‑based pipelines, built on REAA’s open‑source instrumentation stack.
multi-agent-observability-for-small-business-support
SMBs adopt several AI agents (support bot, lead qualifier, appointment setter) but have no real visibility into their behavior, leading to silent failures, cost overruns, and distrust in the automation.Get a unified dashboard of every AI agent your business runs, tracking latency, cost, and failure patterns so you never lose a customer conversation.