LLM observability in Gemini model API answers

As of

All 16 companies by gemini recommendation prompt presence

Braintrust leads at 62.5%; the middle of the board sits at 12.5%.

Ahead of second place: 12.5%
Held by the top three: 54%
Middle of the board: 12.5%
Below half the leader: 12 of 16

Rank, name and gemini recommendation prompt presenceMiddle 12.5%

1Braintrust62.5%
2Langfuse50.0%
3LangSmith50.0%
4Arize AI / Phoenix37.5%
5Maxim AI25.0%
6Traceloop25.0%
7Galileo12.5%
8Datadog LLM Observability12.5%
9Fiddler12.5%
10Helicone12.5%
11HoneyHive0.0%
12Humanloop0.0%
13New Relic AI Observability0.0%
14Opik by Comet0.0%
15Patronus AI0.0%
16W&B Weave0.0%

The prompt boards are re-run in rotation, about one category a day, so each board refreshes every few weeks and shows the date of its snapshot.

engines: 2 on 8 of 16 rows · 8 checks behind each row · 100% confidence across rows.

LLM Observability brands in Gemini model API answers current rankings
Rank	Name	Gemini recommendation prompt presence	Mention coverage	Share of names	Cited as source	Change	Why
01	Braintrust	62.5%	30.56% · 11 / 36	18.33% · 11 / 60	23.91% · 11 / 46		This rank My rank
02	Langfuse	50.0%	30.56% · 11 / 36	18.33% · 11 / 60	17.39% · 8 / 46	1	This rank My rank
03	LangSmith	50.0%	16.67% · 6 / 36	10% · 6 / 60	10.87% · 5 / 46	2	This rank My rank
04	Arize AI / Phoenix	37.5%	25% · 9 / 36	15% · 9 / 60	10.87% · 5 / 46	2	Showing My rank
05	Maxim AI	25.0%	19.44% · 7 / 36	11.67% · 7 / 60	8.7% · 4 / 46	1	This rank My rank
06	Traceloop	25.0%	5.56% · 2 / 36	3.33% · 2 / 60	2.17% · 1 / 46	5	This rank My rank
07	Galileo	12.5%	8.33% · 3 / 36	5% · 3 / 60	4.35% · 2 / 46		This rank My rank
08	Datadog LLM Observability	12.5%	13.89% · 5 / 36	8.33% · 5 / 60	6.52% · 3 / 46	4	This rank My rank
09	Fiddler	12.5%	11.11% · 4 / 36	6.67% · 4 / 60	15.22% · 7 / 46	1	This rank My rank
10	Helicone	12.5%	5.56% · 2 / 36	3.33% · 2 / 60	0% · 0 / 46	4	This rank My rank
11	HoneyHive	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	3	This rank My rank
12	Humanloop	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	3	This rank My rank
13	New Relic AI Observability	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46		This rank My rank
14	Opik by Comet	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	5	This rank My rank
15	Patronus AI	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	1	This rank My rank
16	W&B Weave	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	6	This rank My rank

engines: 2 on 8 of 16 rows · 8 checks behind each row · 100% confidence across rows.

LLM Observability brands in Gemini model API answers current rankings
Rank	Name	Gemini recommendation prompt presence	Mention coverage	Share of names	Cited as source	Change	Why
01	Braintrust	62.5%	30.56% · 11 / 36	18.33% · 11 / 60	23.91% · 11 / 46		This rank My rank
02	Langfuse	50.0%	30.56% · 11 / 36	18.33% · 11 / 60	17.39% · 8 / 46	1	This rank My rank
03	LangSmith	50.0%	16.67% · 6 / 36	10% · 6 / 60	10.87% · 5 / 46	2	This rank My rank
04	Arize AI / Phoenix	37.5%	25% · 9 / 36	15% · 9 / 60	10.87% · 5 / 46	2	Showing My rank
05	Maxim AI	25.0%	19.44% · 7 / 36	11.67% · 7 / 60	8.7% · 4 / 46	1	This rank My rank
06	Traceloop	25.0%	5.56% · 2 / 36	3.33% · 2 / 60	2.17% · 1 / 46	5	This rank My rank
07	Galileo	12.5%	8.33% · 3 / 36	5% · 3 / 60	4.35% · 2 / 46		This rank My rank
08	Datadog LLM Observability	12.5%	13.89% · 5 / 36	8.33% · 5 / 60	6.52% · 3 / 46	4	This rank My rank
09	Fiddler	12.5%	11.11% · 4 / 36	6.67% · 4 / 60	15.22% · 7 / 46	1	This rank My rank
10	Helicone	12.5%	5.56% · 2 / 36	3.33% · 2 / 60	0% · 0 / 46	4	This rank My rank
11	HoneyHive	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	3	This rank My rank
12	Humanloop	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	3	This rank My rank
13	New Relic AI Observability	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46		This rank My rank
14	Opik by Comet	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	5	This rank My rank
15	Patronus AI	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	1	This rank My rank
16	W&B Weave	0.0%	0% · 0 / 36	0% · 0 / 60	0% · 0 / 46	6	This rank My rank

Primary brandQuestion typeModel APIQuestion contains

Run a question live

16.7%

of these answers name or cite LangSmith

6 of 36 answers

16.2%

Its share of the compared brands' appearances

5

Answers citing its own site

5 links to its domains

2 of 3

Model APIs that name it

The compared brands across the filtered answers
Brand	Named or cited	Against primary	Share of compared	Mentions	Answers citing it	Links to its site	Model APIs
Braintrustbraintrust.dev	30.6%11 of 36	+13.9 pts	29.7%	5	11	11	2 of 3
Langfuselangfuse.com	30.6%11 of 36	+13.9 pts	29.7%	25	4	8	3 of 3
Arize AI / Phoenixarize.com	25.0%9 of 36	+8.3 pts	24.3%	8	5	5	2 of 3
LangSmithlangchain.com	16.7%6 of 36	Primary	16.2%	5	5	5	2 of 3

Compared brands named in each answer, by question and model API
Question	OpenAIopenai/gpt-4o-mini	Anthropicanthropic/claude-haiku-4-5	Geminigoogle/gemini-2.5-flash
01What are the best observability platforms for a production application powered by large language models?Recommendation	LangSmithArize AI / PhoenixLangfuse 5 sources`598ccda5`	None named 0 sources`a84dfd26`	LangSmith, cited 1Arize AI / Phoenix, cited 1Langfuse, cited 1 9 sources`bc729d2b`
02Recommend an open-source system for self-hosted language-model tracing, evaluations, and prompt analytics.Recommendation	Langfuse, cited 2 4 sources`5beb5e41`	Langfuse, cited 4 6 sources`5344ffae`	LangfuseBraintrust, cited 1 6 sources`476612f3`
03Which enterprise AI observability platform offers private deployment, role-based access, SSO, audit logs, and data residency?Recommendation	None named 4 sources`c93f2820`	None named 0 sources`e544140e`	None named 7 sources`d0fa6a50`
04What is the best platform for tracing multi-agent workflows, tool calls, handoffs, and Model Context Protocol activity?Recommendation	Arize AI / Phoenix, cited 1LangfuseBraintrust, cited 1 6 sources`3d7bca46`	None named 0 sources`4cf4a32f`	LangSmith, cited 1Langfuse, cited 1 7 sources`d0de3967`
05Recommend an observability tool for debugging retrieval quality, context relevance, groundedness, and hallucinations in RAG systems.Recommendation	LangSmith, cited 1Arize AI / PhoenixBraintrust, cited 1 6 sources`7b11580d`	Langfuse 0 sources`1c7f5267`	LangSmith, cited 1Arize AI / PhoenixLangfuseBraintrust, cited 1 7 sources`03677300`
06Which platform is best for online evaluations, production quality monitoring, failure clustering, and regression alerts?Recommendation	Arize AI / PhoenixLangfuse 4 sources`96933602`	None named 0 sources`027da1e6`	LangSmith, cited 1Braintrust, cited 1 8 sources`3363f62f`
07Recommend a platform for versioned evaluation datasets, prompt experiments, human review, and quality gates in CI.Recommendation	LangfuseBraintrust, cited 1 7 sources`f760c663`	None named 0 sources`0d65f576`	Arize AI / Phoenix, cited 1Braintrust, cited 1 8 sources`ece75029`
08What is the best language-model gateway with request tracing, token cost, latency, caching, and provider reliability analytics?Recommendation	Braintrust, cited 1 7 sources`60f3ac68`	None named 0 sources`59a296fe`	Braintrust, cited 1 7 sources`605a6782`
09How should a team compare AI observability pricing across traces, events, retention, evaluator runs, seats, and data volume?Commercial comparison	Arize AI / Phoenix, cited 1Braintrust, cited 1 7 sources`a6c25445`	None named 0 sources`6e9fad5f`	Braintrust, cited 1 3 sources`2b26de77`
10Which privacy and security criteria matter when buying AI observability, including redaction, sampling, and data retention?Commercial comparison	None named 8 sources`ed6a78a1`	None named 0 sources`60b4cfd7`	None named 8 sources`03dee5e6`
11Should a production AI team prefer OpenTelemetry-compatible instrumentation or a proprietary tracing SDK?Commercial comparison	None named 4 sources`fe24cb0a`	None named 0 sources`59bf59dd`	None named 7 sources`4589bff6`
12When should a team buy a language-model evaluation and observability platform instead of building one internally?Commercial comparison	Arize AI / Phoenix, cited 1 9 sources`4e28be8a`	None named 0 sources`89f7da14`	None named 4 sources`673c2074`

Sites cited in the filtered answers
Site	Answers citing it	Distinct pages	Model APIs	Best position
braintrust.dev	11	8	2	1
openobserve.ai	5	5	2	2
confident-ai.com	5	4	2	1
arize.com	5	4	2	3
langchain.com	5	3	2	1
langfuse.com	4	7	3	2
getmaxim.ai	4	4	2	1
reddit.com	4	3	2	2
fiddler.ai	3	4	2	1
truefoundry.com	3	3	2	2
linkedin.com	3	3	2	5
portkey.ai	3	2	2	1
honeycomb.io	3	2	1	2
github.com	3	1	3	1
acceldata.io	2	2	1	1
datadoghq.com	2	2	2	1
montecarlo.ai	2	2	1	1
rank.ai	2	2	1	1
galileo.ai	2	2	2	2
mlflow.org	2	2	1	2

What this report publishesThe public buying questions, the brands each answer names, the sources it cites and a fingerprint of each answer. Answer text is not published, and no customer data is in it.llm-observability-platforms-2026-07-27 / 4-llm-observability-platforms-2026-07-27-models-64232fd32583

Primary brandQuestion typeModel APIQuestion contains

Run a question live

16.7%

of these answers name or cite LangSmith

6 of 36 answers

16.2%

Its share of the compared brands' appearances

5

Answers citing its own site

5 links to its domains

2 of 3

Model APIs that name it

The compared brands across the filtered answers
Brand	Named or cited	Against primary	Share of compared	Mentions	Answers citing it	Links to its site	Model APIs
Braintrustbraintrust.dev	30.6%11 of 36	+13.9 pts	29.7%	5	11	11	2 of 3
Langfuselangfuse.com	30.6%11 of 36	+13.9 pts	29.7%	25	4	8	3 of 3
Arize AI / Phoenixarize.com	25.0%9 of 36	+8.3 pts	24.3%	8	5	5	2 of 3
LangSmithlangchain.com	16.7%6 of 36	Primary	16.2%	5	5	5	2 of 3

Compared brands named in each answer, by question and model API
Question	OpenAIopenai/gpt-4o-mini	Anthropicanthropic/claude-haiku-4-5	Geminigoogle/gemini-2.5-flash
01What are the best observability platforms for a production application powered by large language models?Recommendation	LangSmithArize AI / PhoenixLangfuse 5 sources`598ccda5`	None named 0 sources`a84dfd26`	LangSmith, cited 1Arize AI / Phoenix, cited 1Langfuse, cited 1 9 sources`bc729d2b`
02Recommend an open-source system for self-hosted language-model tracing, evaluations, and prompt analytics.Recommendation	Langfuse, cited 2 4 sources`5beb5e41`	Langfuse, cited 4 6 sources`5344ffae`	LangfuseBraintrust, cited 1 6 sources`476612f3`
03Which enterprise AI observability platform offers private deployment, role-based access, SSO, audit logs, and data residency?Recommendation	None named 4 sources`c93f2820`	None named 0 sources`e544140e`	None named 7 sources`d0fa6a50`
04What is the best platform for tracing multi-agent workflows, tool calls, handoffs, and Model Context Protocol activity?Recommendation	Arize AI / Phoenix, cited 1LangfuseBraintrust, cited 1 6 sources`3d7bca46`	None named 0 sources`4cf4a32f`	LangSmith, cited 1Langfuse, cited 1 7 sources`d0de3967`
05Recommend an observability tool for debugging retrieval quality, context relevance, groundedness, and hallucinations in RAG systems.Recommendation	LangSmith, cited 1Arize AI / PhoenixBraintrust, cited 1 6 sources`7b11580d`	Langfuse 0 sources`1c7f5267`	LangSmith, cited 1Arize AI / PhoenixLangfuseBraintrust, cited 1 7 sources`03677300`
06Which platform is best for online evaluations, production quality monitoring, failure clustering, and regression alerts?Recommendation	Arize AI / PhoenixLangfuse 4 sources`96933602`	None named 0 sources`027da1e6`	LangSmith, cited 1Braintrust, cited 1 8 sources`3363f62f`
07Recommend a platform for versioned evaluation datasets, prompt experiments, human review, and quality gates in CI.Recommendation	LangfuseBraintrust, cited 1 7 sources`f760c663`	None named 0 sources`0d65f576`	Arize AI / Phoenix, cited 1Braintrust, cited 1 8 sources`ece75029`
08What is the best language-model gateway with request tracing, token cost, latency, caching, and provider reliability analytics?Recommendation	Braintrust, cited 1 7 sources`60f3ac68`	None named 0 sources`59a296fe`	Braintrust, cited 1 7 sources`605a6782`
09How should a team compare AI observability pricing across traces, events, retention, evaluator runs, seats, and data volume?Commercial comparison	Arize AI / Phoenix, cited 1Braintrust, cited 1 7 sources`a6c25445`	None named 0 sources`6e9fad5f`	Braintrust, cited 1 3 sources`2b26de77`
10Which privacy and security criteria matter when buying AI observability, including redaction, sampling, and data retention?Commercial comparison	None named 8 sources`ed6a78a1`	None named 0 sources`60b4cfd7`	None named 8 sources`03dee5e6`
11Should a production AI team prefer OpenTelemetry-compatible instrumentation or a proprietary tracing SDK?Commercial comparison	None named 4 sources`fe24cb0a`	None named 0 sources`59bf59dd`	None named 7 sources`4589bff6`
12When should a team buy a language-model evaluation and observability platform instead of building one internally?Commercial comparison	Arize AI / Phoenix, cited 1 9 sources`4e28be8a`	None named 0 sources`89f7da14`	None named 4 sources`673c2074`

Sites cited in the filtered answers
Site	Answers citing it	Distinct pages	Model APIs	Best position
braintrust.dev	11	8	2	1
openobserve.ai	5	5	2	2
confident-ai.com	5	4	2	1
arize.com	5	4	2	3
langchain.com	5	3	2	1
langfuse.com	4	7	3	2
getmaxim.ai	4	4	2	1
reddit.com	4	3	2	2
fiddler.ai	3	4	2	1
truefoundry.com	3	3	2	2
linkedin.com	3	3	2	5
portkey.ai	3	2	2	1
honeycomb.io	3	2	1	2
github.com	3	1	3	1
acceldata.io	2	2	1	1
datadoghq.com	2	2	2	1
montecarlo.ai	2	2	1	1
rank.ai	2	2	1	1
galileo.ai	2	2	2	2
mlflow.org	2	2	1	2

What this report publishesThe public buying questions, the brands each answer names, the sources it cites and a fingerprint of each answer. Answer text is not published, and no customer data is in it.llm-observability-platforms-2026-07-27 / 4-llm-observability-platforms-2026-07-27-models-64232fd32583

Ranks a reviewed LLM Observability brand universe using a fixed, public set of neutral commercial prompts across 3 AI providers.

4

Rank on this board

37.5 percent

Score

8

Answers behind the score

100% confidence · 1 of 1 components evidenced

What the score is built from

1 of 1 evidenced

Every component evidenced · 1 evidence record each

Components of Arize AI / Phoenix's score
Component	Value	Contribution
Gemini brand recommendation presence	37.5	37.5

Moved down since the previous snapshot

-2 places

OpenAI brand mention coverage33.333 to 50
+16.667
OpenAI brand citation share0 to 13.636
+13.636
Anthropic brand recommendation presence12.5 to 0
-12.5
Gemini brand mention coverage33.333 to 25
-8.333
Anthropic brand mention coverage8.333 to 0
-8.333
Citation share4.545 to 10.87
+6.324
Gemini brand citation share14.286 to 10
-4.286
Recommendation presence33.333 to 29.167
-4.167
Engine breadth3 to 2
-1
Mention share14.063 to 15
+0.938
Anthropic brand citation share0 to 0
0
Mention coverage25 to 25
0
Gemini brand recommendation presence37.5 to 37.5
0
OpenAI brand recommendation presence50 to 50
0

Where each number comes from

14 public records

Anthropic citation shareAnthropic brand citation share · observed Sep 13, 2026, 12:00 AM UTC0 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["27fa197f-ee19-4471-8c55-91ee09bd7994","20119caa-71aa-45a5-a248-e63b744ed125","e39e0720-00c5-4575-b5e1-52f6c101a98c","51bc6f18-6719-4ca6-8130-a55d6746ab92","bf337e82-7914-499a-ab77-8c4a1c3b8d8a","816c6f98-71fa-43e1-883c-80fec5f77439","61f0cb6d-61fb-49a3-b25f-8ce92f828b3f","8f763200-62b8-4760-ad0c-844b7199cdea","14ed7c52-ee74-41f3-b653-a79ac738a664","3a7a1ad3-6be0-4bdf-8471-2f71c8c28920","5144e204-ff40-4cc1-b2ad-e8851a6f254b","e92d9a1c-6047-4938-93e1-339f6b365b1a","e72922ed-cb02-4237-8795-467614ce65e0","c2c50e87-aa3a-49f0-92ff-27f72d6e137f","2b2a533f-5dbd-45c2-aeea-11c0dac1776b","455eab69-e896-45fc-9e73-1ede7d3eb8ba","0bc1afad-2355-4d5e-b84d-69cbfbebda4d","7794a0e4-b5d4-4451-8f34-8f1b0fee5601","c4fd76d8-5fe6-4c7e-a8ef-9bf06ad5f44c","9a3110af-e5bf-49df-9747-5aa244fc2260","edfcba37-ba5f-48de-9871-25e69b2e4fb2","a5566881-6f44-4043-8e24-8ae5f6569b08","9397bce3-af27-4872-8a9f-98fe0e352248","092edcc7-2a69-427b-8801-8f80ac3cf0a5","c084b8e4-e923-4466-bb51-46db68a6e384","49565fcf-10e3-4ee2-a5f3-12ae31c00272","e521d941-24c7-4b57-a5e4-74574b5556c4","2105b4d0-d2da-4d3b-b00a-c952d2106572","1edb5bb2-ae29-4c5c-8a2c-710cbee1c56a","7c42697b-9654-4f40-9b44-58b8721bf3fd","bcc8137d-19fe-43cb-a98b-894f02451d94","11bce99e-18cd-4dde-a2e6-109d625c7277","a526a55f-98c9-4b10-ac12-8d43dd323f3d","ee59e92a-e892-451f-8e14-5787d1bab8c0","fbde0b88-e1a7-4577-b407-d32bfbd80aad","cfd3251c-3f97-46ab-a1a5-7408d76d9f67"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open the source
Anthropic mention coverageAnthropic brand mention coverage · observed Sep 13, 2026, 12:00 AM UTC0 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["27fa197f-ee19-4471-8c55-91ee09bd7994","20119caa-71aa-45a5-a248-e63b744ed125","e39e0720-00c5-4575-b5e1-52f6c101a98c","51bc6f18-6719-4ca6-8130-a55d6746ab92","bf337e82-7914-499a-ab77-8c4a1c3b8d8a","816c6f98-71fa-43e1-883c-80fec5f77439","61f0cb6d-61fb-49a3-b25f-8ce92f828b3f","8f763200-62b8-4760-ad0c-844b7199cdea","14ed7c52-ee74-41f3-b653-a79ac738a664","3a7a1ad3-6be0-4bdf-8471-2f71c8c28920","5144e204-ff40-4cc1-b2ad-e8851a6f254b","e92d9a1c-6047-4938-93e1-339f6b365b1a","e72922ed-cb02-4237-8795-467614ce65e0","c2c50e87-aa3a-49f0-92ff-27f72d6e137f","2b2a533f-5dbd-45c2-aeea-11c0dac1776b","455eab69-e896-45fc-9e73-1ede7d3eb8ba","0bc1afad-2355-4d5e-b84d-69cbfbebda4d","7794a0e4-b5d4-4451-8f34-8f1b0fee5601","c4fd76d8-5fe6-4c7e-a8ef-9bf06ad5f44c","9a3110af-e5bf-49df-9747-5aa244fc2260","edfcba37-ba5f-48de-9871-25e69b2e4fb2","a5566881-6f44-4043-8e24-8ae5f6569b08","9397bce3-af27-4872-8a9f-98fe0e352248","092edcc7-2a69-427b-8801-8f80ac3cf0a5","c084b8e4-e923-4466-bb51-46db68a6e384","49565fcf-10e3-4ee2-a5f3-12ae31c00272","e521d941-24c7-4b57-a5e4-74574b5556c4","2105b4d0-d2da-4d3b-b00a-c952d2106572","1edb5bb2-ae29-4c5c-8a2c-710cbee1c56a","7c42697b-9654-4f40-9b44-58b8721bf3fd","bcc8137d-19fe-43cb-a98b-894f02451d94","11bce99e-18cd-4dde-a2e6-109d625c7277","a526a55f-98c9-4b10-ac12-8d43dd323f3d","ee59e92a-e892-451f-8e14-5787d1bab8c0","fbde0b88-e1a7-4577-b407-d32bfbd80aad","cfd3251c-3f97-46ab-a1a5-7408d76d9f67"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open the source
Anthropic recommendation-prompt presenceAnthropic brand recommendation presence · observed Sep 13, 2026, 12:00 AM UTC0 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Citation shareCitation share · observed Sep 13, 2026, 12:00 AM UTC10.87 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Engine breadthEngine breadth · observed Sep 13, 2026, 12:00 AM UTC2 engines100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Mention coverageMention coverage · observed Sep 13, 2026, 12:00 AM UTC25 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Reviewed-brand presence shareMention share · observed Sep 13, 2026, 12:00 AM UTC15 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Recommendation-prompt presenceRecommendation presence · observed Sep 13, 2026, 12:00 AM UTC29.167 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Gemini citation shareGemini brand citation share · observed Sep 13, 2026, 12:00 AM UTC10 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Gemini mention coverageGemini brand mention coverage · observed Sep 13, 2026, 12:00 AM UTC25 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Gemini recommendation-prompt presenceGemini brand recommendation presence · observed Sep 13, 2026, 12:00 AM UTC37.5 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["27fa197f-ee19-4471-8c55-91ee09bd7994","20119caa-71aa-45a5-a248-e63b744ed125","e39e0720-00c5-4575-b5e1-52f6c101a98c","51bc6f18-6719-4ca6-8130-a55d6746ab92","bf337e82-7914-499a-ab77-8c4a1c3b8d8a","816c6f98-71fa-43e1-883c-80fec5f77439","61f0cb6d-61fb-49a3-b25f-8ce92f828b3f","8f763200-62b8-4760-ad0c-844b7199cdea","14ed7c52-ee74-41f3-b653-a79ac738a664","3a7a1ad3-6be0-4bdf-8471-2f71c8c28920","5144e204-ff40-4cc1-b2ad-e8851a6f254b","e92d9a1c-6047-4938-93e1-339f6b365b1a","e72922ed-cb02-4237-8795-467614ce65e0","c2c50e87-aa3a-49f0-92ff-27f72d6e137f","2b2a533f-5dbd-45c2-aeea-11c0dac1776b","455eab69-e896-45fc-9e73-1ede7d3eb8ba","0bc1afad-2355-4d5e-b84d-69cbfbebda4d","7794a0e4-b5d4-4451-8f34-8f1b0fee5601","c4fd76d8-5fe6-4c7e-a8ef-9bf06ad5f44c","9a3110af-e5bf-49df-9747-5aa244fc2260","edfcba37-ba5f-48de-9871-25e69b2e4fb2","a5566881-6f44-4043-8e24-8ae5f6569b08","9397bce3-af27-4872-8a9f-98fe0e352248","092edcc7-2a69-427b-8801-8f80ac3cf0a5","c084b8e4-e923-4466-bb51-46db68a6e384","49565fcf-10e3-4ee2-a5f3-12ae31c00272","e521d941-24c7-4b57-a5e4-74574b5556c4","2105b4d0-d2da-4d3b-b00a-c952d2106572","1edb5bb2-ae29-4c5c-8a2c-710cbee1c56a","7c42697b-9654-4f40-9b44-58b8721bf3fd","bcc8137d-19fe-43cb-a98b-894f02451d94","11bce99e-18cd-4dde-a2e6-109d625c7277","a526a55f-98c9-4b10-ac12-8d43dd323f3d","ee59e92a-e892-451f-8e14-5787d1bab8c0","fbde0b88-e1a7-4577-b407-d32bfbd80aad","cfd3251c-3f97-46ab-a1a5-7408d76d9f67"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open the source
OpenAI citation shareOpenAI brand citation share · observed Sep 13, 2026, 12:00 AM UTC13.636 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["27fa197f-ee19-4471-8c55-91ee09bd7994","20119caa-71aa-45a5-a248-e63b744ed125","e39e0720-00c5-4575-b5e1-52f6c101a98c","51bc6f18-6719-4ca6-8130-a55d6746ab92","bf337e82-7914-499a-ab77-8c4a1c3b8d8a","816c6f98-71fa-43e1-883c-80fec5f77439","61f0cb6d-61fb-49a3-b25f-8ce92f828b3f","8f763200-62b8-4760-ad0c-844b7199cdea","14ed7c52-ee74-41f3-b653-a79ac738a664","3a7a1ad3-6be0-4bdf-8471-2f71c8c28920","5144e204-ff40-4cc1-b2ad-e8851a6f254b","e92d9a1c-6047-4938-93e1-339f6b365b1a","e72922ed-cb02-4237-8795-467614ce65e0","c2c50e87-aa3a-49f0-92ff-27f72d6e137f","2b2a533f-5dbd-45c2-aeea-11c0dac1776b","455eab69-e896-45fc-9e73-1ede7d3eb8ba","0bc1afad-2355-4d5e-b84d-69cbfbebda4d","7794a0e4-b5d4-4451-8f34-8f1b0fee5601","c4fd76d8-5fe6-4c7e-a8ef-9bf06ad5f44c","9a3110af-e5bf-49df-9747-5aa244fc2260","edfcba37-ba5f-48de-9871-25e69b2e4fb2","a5566881-6f44-4043-8e24-8ae5f6569b08","9397bce3-af27-4872-8a9f-98fe0e352248","092edcc7-2a69-427b-8801-8f80ac3cf0a5","c084b8e4-e923-4466-bb51-46db68a6e384","49565fcf-10e3-4ee2-a5f3-12ae31c00272","e521d941-24c7-4b57-a5e4-74574b5556c4","2105b4d0-d2da-4d3b-b00a-c952d2106572","1edb5bb2-ae29-4c5c-8a2c-710cbee1c56a","7c42697b-9654-4f40-9b44-58b8721bf3fd","bcc8137d-19fe-43cb-a98b-894f02451d94","11bce99e-18cd-4dde-a2e6-109d625c7277","a526a55f-98c9-4b10-ac12-8d43dd323f3d","ee59e92a-e892-451f-8e14-5787d1bab8c0","fbde0b88-e1a7-4577-b407-d32bfbd80aad","cfd3251c-3f97-46ab-a1a5-7408d76d9f67"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open the source
OpenAI mention coverageOpenAI brand mention coverage · observed Sep 13, 2026, 12:00 AM UTC50 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["27fa197f-ee19-4471-8c55-91ee09bd7994","20119caa-71aa-45a5-a248-e63b744ed125","e39e0720-00c5-4575-b5e1-52f6c101a98c","51bc6f18-6719-4ca6-8130-a55d6746ab92","bf337e82-7914-499a-ab77-8c4a1c3b8d8a","816c6f98-71fa-43e1-883c-80fec5f77439","61f0cb6d-61fb-49a3-b25f-8ce92f828b3f","8f763200-62b8-4760-ad0c-844b7199cdea","14ed7c52-ee74-41f3-b653-a79ac738a664","3a7a1ad3-6be0-4bdf-8471-2f71c8c28920","5144e204-ff40-4cc1-b2ad-e8851a6f254b","e92d9a1c-6047-4938-93e1-339f6b365b1a","e72922ed-cb02-4237-8795-467614ce65e0","c2c50e87-aa3a-49f0-92ff-27f72d6e137f","2b2a533f-5dbd-45c2-aeea-11c0dac1776b","455eab69-e896-45fc-9e73-1ede7d3eb8ba","0bc1afad-2355-4d5e-b84d-69cbfbebda4d","7794a0e4-b5d4-4451-8f34-8f1b0fee5601","c4fd76d8-5fe6-4c7e-a8ef-9bf06ad5f44c","9a3110af-e5bf-49df-9747-5aa244fc2260","edfcba37-ba5f-48de-9871-25e69b2e4fb2","a5566881-6f44-4043-8e24-8ae5f6569b08","9397bce3-af27-4872-8a9f-98fe0e352248","092edcc7-2a69-427b-8801-8f80ac3cf0a5","c084b8e4-e923-4466-bb51-46db68a6e384","49565fcf-10e3-4ee2-a5f3-12ae31c00272","e521d941-24c7-4b57-a5e4-74574b5556c4","2105b4d0-d2da-4d3b-b00a-c952d2106572","1edb5bb2-ae29-4c5c-8a2c-710cbee1c56a","7c42697b-9654-4f40-9b44-58b8721bf3fd","bcc8137d-19fe-43cb-a98b-894f02451d94","11bce99e-18cd-4dde-a2e6-109d625c7277","a526a55f-98c9-4b10-ac12-8d43dd323f3d","ee59e92a-e892-451f-8e14-5787d1bab8c0","fbde0b88-e1a7-4577-b407-d32bfbd80aad","cfd3251c-3f97-46ab-a1a5-7408d76d9f67"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open the source
OpenAI recommendation-prompt presenceOpenAI brand recommendation presence · observed Sep 13, 2026, 12:00 AM UTC50 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source

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Plain textLLM Observability brands in Gemini model API answers. Rank.ai, data as of September 13, 2026. https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-gemini-visibility
HTML<a href="https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-gemini-visibility">LLM Observability brands in Gemini model API answers</a>, Rank.ai, data as of September 13, 2026
Markdown[LLM Observability brands in Gemini model API answers](https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-gemini-visibility), Rank.ai, data as of September 13, 2026
APARank.ai. (2026). LLM Observability brands in Gemini model API answers. Retrieved October 10, 2026, from https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-gemini-visibility

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<p style="font-size:12px">Source: <a href="https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-gemini-visibility">LLM Observability brands in Gemini model API answers</a> by Rank.ai</p>

More in LLM observability

Tracing, evaluation, monitoring, and AI reliability platforms ranked across a fixed buying corpus, with brand, prompt, engine and citation source views.

3 boards

LLM observability platform rank

Reviewed AI tracing and evaluation platforms ranked by how often answers to the category's recommendation questions name or cite them, across three model APIs.

1Langfuse245.8%
2Braintrust137.5%
3Arize AI / Phoenix129.2%

LLM observability platform presence

Reviewed platforms ranked by the share of all the category's answers that name or cite them.

1Langfuse230.6%
2Braintrust130.6%
3Arize AI / Phoenix125%

LLM observability citation share

Reviewed platforms ranked by distinct answer-level citations to their canonical domains.

1Braintrust23.9%
2Langfuse17.4%
3Fiddler15.2%

LLM observability platform rank

Reviewed AI tracing and evaluation platforms ranked by how often answers to the category's recommendation questions name or cite them, across three model APIs.

LLM observability platform presence

Reviewed platforms ranked by the share of all the category's answers that name or cite them.

LLM observability citation share

Reviewed platforms ranked by distinct answer-level citations to their canonical domains.

See how often AI names you.

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Your grade out of 100How often AI names you, cites your site, and how high it ranks you.
Who gets namedEvery competitor in the answers, most named first.
The pages AI readsThe sources behind each answer.
Three fixesWhat to fix first, with a brief for the first article.

LLM Observability brands in Gemini model API answers

All 16 companies by gemini recommendation prompt presence

One brand against up to three competitors, answer by answer

Which compared brands each answer names

The sites these answers cite

One brand against up to three competitors, answer by answer

Which compared brands each answer names

The sites these answers cite

Arize AI / Phoenix on LLM Observability Brands in Gemini Model API Answers

What the score is built from

Moved down since the previous snapshot

Where each number comes from

More in LLM observability

See how often AI names you.

One brand against up to three competitors, answer by answer

Which compared brands each answer names

The sites these answers cite