Alternatives

Products that do what ModelOps does

Unified AI SDK with built-in observability

1
OpenObserve▲305
AI-native, open-source Datadog alternative
Mar 2026
2
Foglamp▲101
Ship AI agents you can actually see
Jun 2026 · foglamp.dev
3
OpenLIT's Zero-code LLM Observability▲136
Trace LLM requests + costs with OpenTelemetry monitoring
Oct 2025
4
Oxlo.ai▲388
Scale across AI models without scaling your bill
Jun 2026 · oxcode.ai
5
LLMonitor▲128
Open source monitoring and production toolkit for AI apps
2023
6
Openlit▲152
One click observability & evals for LLMs & GPUs
2024
7
AskCodi▲230
Custom LLMs, without training. Use via openai compatible api
Nov 2025
8
Langtrace AI▲119
Improve your LLM apps with open-source observability tool
2024
9
Label Studio 1.8.0 Release▲185
Open source data labelling platform for AI model tuning
2023
10
LLM Ops Toolkit by Lamatic.ai▲96
Aggregate uptime monitoring across OpenAI, Claude, and more
Apr 2026
11
Progress AI Observability▲168
Trace, evaluate, and improve AI agents in production
Aug 2026 · telerik.com
12
Siloam AI (alpha)▲83
LLM-usage observability and monitoring tool
2025
13
Temporal + OpenAI Agents SDK▲130
Build production-ready agents, fast.
2025
14
Clevrr Computer▲195
Computer use but with OpenAI and Gemini models
2024
15
Unify - Interfaces▲157
Notion for AI Observability 📊
2025
16
TraceLLM▲100
OpenTelemetry for production AI applications
Jul 2026 · tracellm.in
17
OpenAI Agents SDK▲122
Build production agents with harness and sandbox
Apr 2026
18
Foglamp HUD▲5
See your AI SDK agents brains at localhost!
Jun 2026 · hud.foglamp.dev
19
Token Harbor▲85
The easiest way to access frontier AI models.
Aug 2026 · tokenharbor.ai
20
ModelRiver▲37
One AI API for production - streaming, failover, logs
Jan 2026
21
Dimies▲4
Observability tool for product teams
Jul 2026 · dimies.com
22IM
I'm building a simple way to package, collaborate, and deploy ML models▲9
Heya HN, after spending +1 year building an ML-driven analytics product (that didn't pan out unfortunately), I've pivoted to solving a problem my team and I found while building the previous product … why the hell is it so hard to move a model from a Jupyter notebook, to a development server, then to a production pipeline!? To solve this my team and I started the open source KitOps project under the Apache 2 license. KitOps includes the Kit CLI that uses a Kitfile manifest to create ModelKits: 1. The kit CLI packages your model, datasets, code, and configuration into an OCI compliant…
2024 · kitops.ml
23OA
Oodle.ai – $10 per million agent traces▲31
Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai
24MY
Monitoring your OpenAI usage without a third party▲9
LLM observability is an absolute must-have for anyone running something in prod (or prod-like). While all the observability startups are great, you're essentially sending all your OpenAI usage history - prompts, generations, chats - to a random third party. So this script deploys a basic proxy in your Azure account, catches all incoming OpenAI requests, stores logs in your own resource group, and comes with visualizations premade (charts, timelines, chat history, cost estimation, etc). Thanks for any thoughts and feedback!
2023 · github.com

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