LLM observability engine breadth

As of

All 16 companies by engine breadth

Langfuse leads at 3 engines; the middle of the board sits at 1.

Ahead of second place: 0
Held by the top three: 41%
Middle of the board: 1
Below half the leader: 10 of 16

Rank, name and engine breadthMiddle 1

1Langfuse3 engines
2Braintrust3 engines
3Arize AI / Phoenix3 engines
4LangSmith3 engines
5Datadog LLM Observability3 engines
6Helicone2 engines
7Fiddler1 engines
8Galileo1 engines
9Maxim AI1 engines
10Opik by Comet1 engines
11New Relic AI Observability1 engines
12HoneyHive0 engines
13Humanloop0 engines
14Patronus AI0 engines
15Traceloop0 engines
16W&B Weave0 engines

chatgpt recommends: 0 on 11 of 16 rows · chatgpt names it: 0 on 10 of 16 rows · 3 checks behind each row.

LLM Observability engine breadth current rankings
Rank	Name	Engine breadth	Mention coverage	Share of names	Cited as source	Change	Why
01	Langfuse	3 engines	33.33% · 12 / 36	24% · 12 / 50	29.73% · 11 / 37		This rank My rank
02	Braintrust	3 engines	27.78% · 10 / 36	20% · 10 / 50	21.62% · 8 / 37		This rank My rank
03	Arize AI / Phoenix	3 engines	25% · 9 / 36	18% · 9 / 50	16.22% · 6 / 37		This rank My rank
04	LangSmith	3 engines	22.22% · 8 / 36	16% · 8 / 50	8.11% · 3 / 37	1	Showing My rank
05	Datadog LLM Observability	3 engines	11.11% · 4 / 36	8% · 4 / 50	2.7% · 1 / 37	2	This rank My rank
06	Helicone	2 engines	5.56% · 2 / 36	4% · 2 / 50	8.11% · 3 / 37	3	This rank My rank
07	Fiddler	1 engines	2.78% · 1 / 36	2% · 1 / 50	5.41% · 2 / 37	1	This rank My rank
08	Galileo	1 engines	2.78% · 1 / 36	2% · 1 / 50	2.7% · 1 / 37	2	This rank My rank
09	Maxim AI	1 engines	2.78% · 1 / 36	2% · 1 / 50	2.7% · 1 / 37	5	This rank My rank
10	Opik by Comet	1 engines	2.78% · 1 / 36	2% · 1 / 50	2.7% · 1 / 37	4	This rank My rank
11	New Relic AI Observability	1 engines	2.78% · 1 / 36	2% · 1 / 50	0% · 0 / 37	2	This rank My rank
12	HoneyHive	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	1	This rank My rank
13	Humanloop	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	1	This rank My rank
14	Patronus AI	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	1	This rank My rank
15	Traceloop	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	5	This rank My rank
16	W&B Weave	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37		This rank My rank

chatgpt recommends: 0 on 11 of 16 rows · chatgpt names it: 0 on 10 of 16 rows · 3 checks behind each row.

LLM Observability engine breadth current rankings
Rank	Name	Engine breadth	Mention coverage	Share of names	Cited as source	Change	Why
01	Langfuse	3 engines	33.33% · 12 / 36	24% · 12 / 50	29.73% · 11 / 37		This rank My rank
02	Braintrust	3 engines	27.78% · 10 / 36	20% · 10 / 50	21.62% · 8 / 37		This rank My rank
03	Arize AI / Phoenix	3 engines	25% · 9 / 36	18% · 9 / 50	16.22% · 6 / 37		This rank My rank
04	LangSmith	3 engines	22.22% · 8 / 36	16% · 8 / 50	8.11% · 3 / 37	1	Showing My rank
05	Datadog LLM Observability	3 engines	11.11% · 4 / 36	8% · 4 / 50	2.7% · 1 / 37	2	This rank My rank
06	Helicone	2 engines	5.56% · 2 / 36	4% · 2 / 50	8.11% · 3 / 37	3	This rank My rank
07	Fiddler	1 engines	2.78% · 1 / 36	2% · 1 / 50	5.41% · 2 / 37	1	This rank My rank
08	Galileo	1 engines	2.78% · 1 / 36	2% · 1 / 50	2.7% · 1 / 37	2	This rank My rank
09	Maxim AI	1 engines	2.78% · 1 / 36	2% · 1 / 50	2.7% · 1 / 37	5	This rank My rank
10	Opik by Comet	1 engines	2.78% · 1 / 36	2% · 1 / 50	2.7% · 1 / 37	4	This rank My rank
11	New Relic AI Observability	1 engines	2.78% · 1 / 36	2% · 1 / 50	0% · 0 / 37	2	This rank My rank
12	HoneyHive	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	1	This rank My rank
13	Humanloop	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	1	This rank My rank
14	Patronus AI	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	1	This rank My rank
15	Traceloop	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37	5	This rank My rank
16	W&B Weave	0 engines	0% · 0 / 36	0% · 0 / 50	0% · 0 / 37		This rank My rank

Primary brandQuestion typeModel APIQuestion contains

Run a question live

22.2%

of these answers name or cite LangSmith

8 of 36 answers

20.5%

Its share of the compared brands' appearances

2

Answers citing its own site

3 links to its domains

3 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
Langfuselangfuse.com	33.3%12 of 36	+11.1 pts	30.8%	24	6	11	3 of 3
Braintrustbraintrust.dev	27.8%10 of 36	+5.6 pts	25.6%	12	6	8	3 of 3
Arize AI / Phoenixarize.com	25.0%9 of 36	+2.8 pts	23.1%	10	3	6	3 of 3
LangSmithlangchain.com	22.2%8 of 36	Primary	20.5%	12	2	3	3 of 3

Compared brands named in each answer, by question and model API
Question	OpenAIopenai/gpt-6-luna	Anthropicanthropic/claude-haiku-5.5	Geminigoogle/gemini-3.1-flash-lite
01What are the best observability platforms for a production application powered by large language models?Recommendation	LangSmithArize AI / Phoenix, cited 1Langfuse 1 sources`431582c6`	LangSmithArize AI / Phoenix, cited 1Langfuse 6 sources`1697b09a`	LangSmithArize AI / PhoenixLangfuseBraintrust, cited 1 9 sources`67cba9e6`
02Recommend an open-source system for self-hosted language-model tracing, evaluations, and prompt analytics.Recommendation	Langfuse, cited 4 4 sources`120fbe8e`	Langfuse, cited 1 5 sources`f40dd720`	Langfuse, cited 1Braintrust, cited 1 5 sources`8e2c4463`
03Which enterprise AI observability platform offers private deployment, role-based access, SSO, audit logs, and data residency?Recommendation	Langfuse 0 sources`eaddb70e`	None named 3 sources`196e1109`	None named 5 sources`d20af724`
04What is the best platform for tracing multi-agent workflows, tool calls, handoffs, and Model Context Protocol activity?Recommendation	Langfuse, cited 3 3 sources`941a3822`	LangSmith, cited 1Arize AI / PhoenixLangfuse 2 sources`32d55bac`	LangSmithArize AI / PhoenixLangfuse, cited 1Braintrust 2 sources`95e624fb`
05Recommend an observability tool for debugging retrieval quality, context relevance, groundedness, and hallucinations in RAG systems.Recommendation	Arize AI / Phoenix, cited 4 4 sources`630e9ced`	Braintrust 2 sources`5856a243`	Arize AI / Phoenix 0 sources`e20c0065`
06Which platform is best for online evaluations, production quality monitoring, failure clustering, and regression alerts?Recommendation	Braintrust, cited 3 3 sources`f8fcc6cd`	Braintrust 3 sources`c7b127c6`	Arize AI / PhoenixLangfuse, cited 1Braintrust, cited 1 4 sources`5ac07dba`
07Recommend a platform for versioned evaluation datasets, prompt experiments, human review, and quality gates in CI.Recommendation	LangSmith 0 sources`36309931`	LangfuseBraintrust 4 sources`77a758c4`	LangSmithArize AI / Phoenix 0 sources`76db1691`
08What is the best language-model gateway with request tracing, token cost, latency, caching, and provider reliability analytics?Recommendation	None named 4 sources`a4ad0e6c`	None named 4 sources`cccc65d9`	Braintrust, cited 1 7 sources`7544997f`
09How should a team compare AI observability pricing across traces, events, retention, evaluator runs, seats, and data volume?Commercial comparison	None named 0 sources`198efece`	None named 0 sources`889a5d1d`	None named 0 sources`68265d17`
10Which privacy and security criteria matter when buying AI observability, including redaction, sampling, and data retention?Commercial comparison	None named 2 sources`f4b640d9`	Braintrust, cited 1 1 sources`f4a20ffc`	None named 0 sources`0d08f4c7`
11Should a production AI team prefer OpenTelemetry-compatible instrumentation or a proprietary tracing SDK?Commercial comparison	None named 2 sources`d50c7389`	None named 4 sources`f7e2c334`	None named 0 sources`0c1c4ec8`
12When should a team buy a language-model evaluation and observability platform instead of building one internally?Commercial comparison	LangSmith, cited 2 2 sources`6b259639`	None named 0 sources`6611a26b`	None named 0 sources`f6733b55`

Sites cited in the filtered answers
Site	Answers citing it	Distinct pages	Model APIs	Best position
braintrust.dev	6	8	3	1
langfuse.com	6	8	3	1
openobserve.ai	4	4	3	1
reddit.com	4	3	2	2
arize.com	3	6	2	1
github.com	3	1	2	1
digitalapplied.com	2	2	1	1
zenml.io	2	2	1	3
openlit.io	2	2	2	5
portkey.ai	2	1	2	2
turingpost.com	2	1	2	2
agenta.ai	2	1	2	4
confident-ai.com	1	2	1	1
docs.langchain.com	1	2	1	1
helicone.ai	1	2	1	1
opentelemetry.io	1	2	1	1
fiddler.ai	1	2	1	2
comet.com	1	1	1	1
cribl.io	1	1	1	1
docs.datadoghq.com	1	1	1	1

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-16124f05e021

Primary brandQuestion typeModel APIQuestion contains

Run a question live

22.2%

of these answers name or cite LangSmith

8 of 36 answers

20.5%

Its share of the compared brands' appearances

2

Answers citing its own site

3 links to its domains

3 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
Langfuselangfuse.com	33.3%12 of 36	+11.1 pts	30.8%	24	6	11	3 of 3
Braintrustbraintrust.dev	27.8%10 of 36	+5.6 pts	25.6%	12	6	8	3 of 3
Arize AI / Phoenixarize.com	25.0%9 of 36	+2.8 pts	23.1%	10	3	6	3 of 3
LangSmithlangchain.com	22.2%8 of 36	Primary	20.5%	12	2	3	3 of 3

Compared brands named in each answer, by question and model API
Question	OpenAIopenai/gpt-6-luna	Anthropicanthropic/claude-haiku-5.5	Geminigoogle/gemini-3.1-flash-lite
01What are the best observability platforms for a production application powered by large language models?Recommendation	LangSmithArize AI / Phoenix, cited 1Langfuse 1 sources`431582c6`	LangSmithArize AI / Phoenix, cited 1Langfuse 6 sources`1697b09a`	LangSmithArize AI / PhoenixLangfuseBraintrust, cited 1 9 sources`67cba9e6`
02Recommend an open-source system for self-hosted language-model tracing, evaluations, and prompt analytics.Recommendation	Langfuse, cited 4 4 sources`120fbe8e`	Langfuse, cited 1 5 sources`f40dd720`	Langfuse, cited 1Braintrust, cited 1 5 sources`8e2c4463`
03Which enterprise AI observability platform offers private deployment, role-based access, SSO, audit logs, and data residency?Recommendation	Langfuse 0 sources`eaddb70e`	None named 3 sources`196e1109`	None named 5 sources`d20af724`
04What is the best platform for tracing multi-agent workflows, tool calls, handoffs, and Model Context Protocol activity?Recommendation	Langfuse, cited 3 3 sources`941a3822`	LangSmith, cited 1Arize AI / PhoenixLangfuse 2 sources`32d55bac`	LangSmithArize AI / PhoenixLangfuse, cited 1Braintrust 2 sources`95e624fb`
05Recommend an observability tool for debugging retrieval quality, context relevance, groundedness, and hallucinations in RAG systems.Recommendation	Arize AI / Phoenix, cited 4 4 sources`630e9ced`	Braintrust 2 sources`5856a243`	Arize AI / Phoenix 0 sources`e20c0065`
06Which platform is best for online evaluations, production quality monitoring, failure clustering, and regression alerts?Recommendation	Braintrust, cited 3 3 sources`f8fcc6cd`	Braintrust 3 sources`c7b127c6`	Arize AI / PhoenixLangfuse, cited 1Braintrust, cited 1 4 sources`5ac07dba`
07Recommend a platform for versioned evaluation datasets, prompt experiments, human review, and quality gates in CI.Recommendation	LangSmith 0 sources`36309931`	LangfuseBraintrust 4 sources`77a758c4`	LangSmithArize AI / Phoenix 0 sources`76db1691`
08What is the best language-model gateway with request tracing, token cost, latency, caching, and provider reliability analytics?Recommendation	None named 4 sources`a4ad0e6c`	None named 4 sources`cccc65d9`	Braintrust, cited 1 7 sources`7544997f`
09How should a team compare AI observability pricing across traces, events, retention, evaluator runs, seats, and data volume?Commercial comparison	None named 0 sources`198efece`	None named 0 sources`889a5d1d`	None named 0 sources`68265d17`
10Which privacy and security criteria matter when buying AI observability, including redaction, sampling, and data retention?Commercial comparison	None named 2 sources`f4b640d9`	Braintrust, cited 1 1 sources`f4a20ffc`	None named 0 sources`0d08f4c7`
11Should a production AI team prefer OpenTelemetry-compatible instrumentation or a proprietary tracing SDK?Commercial comparison	None named 2 sources`d50c7389`	None named 4 sources`f7e2c334`	None named 0 sources`0c1c4ec8`
12When should a team buy a language-model evaluation and observability platform instead of building one internally?Commercial comparison	LangSmith, cited 2 2 sources`6b259639`	None named 0 sources`6611a26b`	None named 0 sources`f6733b55`

Sites cited in the filtered answers
Site	Answers citing it	Distinct pages	Model APIs	Best position
braintrust.dev	6	8	3	1
langfuse.com	6	8	3	1
openobserve.ai	4	4	3	1
reddit.com	4	3	2	2
arize.com	3	6	2	1
github.com	3	1	2	1
digitalapplied.com	2	2	1	1
zenml.io	2	2	1	3
openlit.io	2	2	2	5
portkey.ai	2	1	2	2
turingpost.com	2	1	2	2
agenta.ai	2	1	2	4
confident-ai.com	1	2	1	1
docs.langchain.com	1	2	1	1
helicone.ai	1	2	1	1
opentelemetry.io	1	2	1	1
fiddler.ai	1	2	1	2
comet.com	1	1	1	1
cribl.io	1	1	1	1
docs.datadoghq.com	1	1	1	1

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-16124f05e021

Some explanation inputs are unavailable. Not published: comparable prior ranking.

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

3 engines

Score

3

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 LangSmith's score
Component	Value	Contribution
Engine breadth	3	3

Moved up since the previous snapshot

+1 places

methodology_changed

The method changed since the previous snapshot, so the two are not directly comparable.

No component moved since the previous snapshot.

Where each number comes from

14 public records

Anthropic citation shareAnthropic brand citation share · observed Oct 22, 2026, 12:00 AM UTC14.286 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Anthropic mention coverageAnthropic brand mention coverage · observed Oct 22, 2026, 12:00 AM UTC16.667 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["9e2a075d-3c64-485d-bdfd-13e9245f16f5","0eb1990c-941b-49a2-80d0-f3bc3cfbb426","70386ebd-4204-400c-bcdc-c1411631dff9","8780f9b8-3818-4862-9513-ecc905c3a0e7","ed5cf0b9-9957-4969-bb48-b24d38e23646","f2a28396-a94d-44e6-b0b6-eaf998ad5b6b","34a281bc-590a-4eb2-9c13-f3777cf9a541","e46bcc40-3682-4c64-8b4d-17989e6a4654","8c2c47af-c353-4cf0-824c-aacf2193b8e1","6ff2d38e-7f01-42cd-b044-bbfd5b5d5b56","e88e0529-0d37-4d8f-b516-f871cdd8c0de","b9ae6168-6aec-4389-8632-694f833da5cb","943be5e4-46cf-4f42-bbc3-4416b7677295","83df98d6-f8c7-4a59-8b31-7185eb1ebdc9","18605475-ed6b-42a5-85f5-a7deb970132f","19064a15-420d-4041-8723-8fccbb2a6456","b98f420d-e0ea-4d91-8733-2729e3b80ecf","4f901c58-051b-42db-9876-5ddc63419a1d","1963b455-fdc0-4a99-a55f-c296285728d0","38394a02-d6c2-4846-abf4-64ce8a4b9c4a","4fc8cde5-c00b-4ddb-8798-c16f2228d530","b31ac5e8-1280-4042-b419-03aa306f547c","c89d17a0-9c84-4377-a323-74fc7304fa8e","5ffa7349-6072-4424-ab05-2734d123425d","c0f2490d-e777-4e91-a116-8fc53bff299a","a88e6bab-ee3f-4eea-818e-8245f751aac1","ee1dbcfa-e3ab-4fc9-810b-9f2622473c56","bded4d61-f3fe-4543-8be7-00be577c1509","42299b33-91cb-495c-9965-81c755becc17","f2e0659f-510c-4a39-808c-ebe34163b330","7b488461-2238-422f-917a-e3ae541ce9ef","60378b0d-57de-4271-b5ac-389a9a62da62","fb053530-e6be-440d-9736-e4ec7ed20d53","fbd94541-8a94-4a9a-8c06-dd00187164c4","7cad07e0-10ce-48b1-b339-2799d416afd3","fa47562d-4c56-4dd1-bd7f-f607fdc7593b"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-16124f05e021","providerMatrixVersion":"16124f05e021"}
Open the source
Anthropic recommendation-prompt presenceAnthropic brand recommendation presence · observed Oct 22, 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
Citation shareCitation share · observed Oct 22, 2026, 12:00 AM UTC8.108 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["9e2a075d-3c64-485d-bdfd-13e9245f16f5","0eb1990c-941b-49a2-80d0-f3bc3cfbb426","70386ebd-4204-400c-bcdc-c1411631dff9","8780f9b8-3818-4862-9513-ecc905c3a0e7","ed5cf0b9-9957-4969-bb48-b24d38e23646","f2a28396-a94d-44e6-b0b6-eaf998ad5b6b","34a281bc-590a-4eb2-9c13-f3777cf9a541","e46bcc40-3682-4c64-8b4d-17989e6a4654","8c2c47af-c353-4cf0-824c-aacf2193b8e1","6ff2d38e-7f01-42cd-b044-bbfd5b5d5b56","e88e0529-0d37-4d8f-b516-f871cdd8c0de","b9ae6168-6aec-4389-8632-694f833da5cb","943be5e4-46cf-4f42-bbc3-4416b7677295","83df98d6-f8c7-4a59-8b31-7185eb1ebdc9","18605475-ed6b-42a5-85f5-a7deb970132f","19064a15-420d-4041-8723-8fccbb2a6456","b98f420d-e0ea-4d91-8733-2729e3b80ecf","4f901c58-051b-42db-9876-5ddc63419a1d","1963b455-fdc0-4a99-a55f-c296285728d0","38394a02-d6c2-4846-abf4-64ce8a4b9c4a","4fc8cde5-c00b-4ddb-8798-c16f2228d530","b31ac5e8-1280-4042-b419-03aa306f547c","c89d17a0-9c84-4377-a323-74fc7304fa8e","5ffa7349-6072-4424-ab05-2734d123425d","c0f2490d-e777-4e91-a116-8fc53bff299a","a88e6bab-ee3f-4eea-818e-8245f751aac1","ee1dbcfa-e3ab-4fc9-810b-9f2622473c56","bded4d61-f3fe-4543-8be7-00be577c1509","42299b33-91cb-495c-9965-81c755becc17","f2e0659f-510c-4a39-808c-ebe34163b330","7b488461-2238-422f-917a-e3ae541ce9ef","60378b0d-57de-4271-b5ac-389a9a62da62","fb053530-e6be-440d-9736-e4ec7ed20d53","fbd94541-8a94-4a9a-8c06-dd00187164c4","7cad07e0-10ce-48b1-b339-2799d416afd3","fa47562d-4c56-4dd1-bd7f-f607fdc7593b"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-16124f05e021","providerMatrixVersion":"16124f05e021"}
Open the source
Engine breadthEngine breadth · observed Oct 22, 2026, 12:00 AM UTC3 engines100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["9e2a075d-3c64-485d-bdfd-13e9245f16f5","0eb1990c-941b-49a2-80d0-f3bc3cfbb426","70386ebd-4204-400c-bcdc-c1411631dff9","8780f9b8-3818-4862-9513-ecc905c3a0e7","ed5cf0b9-9957-4969-bb48-b24d38e23646","f2a28396-a94d-44e6-b0b6-eaf998ad5b6b","34a281bc-590a-4eb2-9c13-f3777cf9a541","e46bcc40-3682-4c64-8b4d-17989e6a4654","8c2c47af-c353-4cf0-824c-aacf2193b8e1","6ff2d38e-7f01-42cd-b044-bbfd5b5d5b56","e88e0529-0d37-4d8f-b516-f871cdd8c0de","b9ae6168-6aec-4389-8632-694f833da5cb","943be5e4-46cf-4f42-bbc3-4416b7677295","83df98d6-f8c7-4a59-8b31-7185eb1ebdc9","18605475-ed6b-42a5-85f5-a7deb970132f","19064a15-420d-4041-8723-8fccbb2a6456","b98f420d-e0ea-4d91-8733-2729e3b80ecf","4f901c58-051b-42db-9876-5ddc63419a1d","1963b455-fdc0-4a99-a55f-c296285728d0","38394a02-d6c2-4846-abf4-64ce8a4b9c4a","4fc8cde5-c00b-4ddb-8798-c16f2228d530","b31ac5e8-1280-4042-b419-03aa306f547c","c89d17a0-9c84-4377-a323-74fc7304fa8e","5ffa7349-6072-4424-ab05-2734d123425d","c0f2490d-e777-4e91-a116-8fc53bff299a","a88e6bab-ee3f-4eea-818e-8245f751aac1","ee1dbcfa-e3ab-4fc9-810b-9f2622473c56","bded4d61-f3fe-4543-8be7-00be577c1509","42299b33-91cb-495c-9965-81c755becc17","f2e0659f-510c-4a39-808c-ebe34163b330","7b488461-2238-422f-917a-e3ae541ce9ef","60378b0d-57de-4271-b5ac-389a9a62da62","fb053530-e6be-440d-9736-e4ec7ed20d53","fbd94541-8a94-4a9a-8c06-dd00187164c4","7cad07e0-10ce-48b1-b339-2799d416afd3","fa47562d-4c56-4dd1-bd7f-f607fdc7593b"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-16124f05e021","providerMatrixVersion":"16124f05e021"}
Open the source
Mention coverageMention coverage · observed Oct 22, 2026, 12:00 AM UTC22.222 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["9e2a075d-3c64-485d-bdfd-13e9245f16f5","0eb1990c-941b-49a2-80d0-f3bc3cfbb426","70386ebd-4204-400c-bcdc-c1411631dff9","8780f9b8-3818-4862-9513-ecc905c3a0e7","ed5cf0b9-9957-4969-bb48-b24d38e23646","f2a28396-a94d-44e6-b0b6-eaf998ad5b6b","34a281bc-590a-4eb2-9c13-f3777cf9a541","e46bcc40-3682-4c64-8b4d-17989e6a4654","8c2c47af-c353-4cf0-824c-aacf2193b8e1","6ff2d38e-7f01-42cd-b044-bbfd5b5d5b56","e88e0529-0d37-4d8f-b516-f871cdd8c0de","b9ae6168-6aec-4389-8632-694f833da5cb","943be5e4-46cf-4f42-bbc3-4416b7677295","83df98d6-f8c7-4a59-8b31-7185eb1ebdc9","18605475-ed6b-42a5-85f5-a7deb970132f","19064a15-420d-4041-8723-8fccbb2a6456","b98f420d-e0ea-4d91-8733-2729e3b80ecf","4f901c58-051b-42db-9876-5ddc63419a1d","1963b455-fdc0-4a99-a55f-c296285728d0","38394a02-d6c2-4846-abf4-64ce8a4b9c4a","4fc8cde5-c00b-4ddb-8798-c16f2228d530","b31ac5e8-1280-4042-b419-03aa306f547c","c89d17a0-9c84-4377-a323-74fc7304fa8e","5ffa7349-6072-4424-ab05-2734d123425d","c0f2490d-e777-4e91-a116-8fc53bff299a","a88e6bab-ee3f-4eea-818e-8245f751aac1","ee1dbcfa-e3ab-4fc9-810b-9f2622473c56","bded4d61-f3fe-4543-8be7-00be577c1509","42299b33-91cb-495c-9965-81c755becc17","f2e0659f-510c-4a39-808c-ebe34163b330","7b488461-2238-422f-917a-e3ae541ce9ef","60378b0d-57de-4271-b5ac-389a9a62da62","fb053530-e6be-440d-9736-e4ec7ed20d53","fbd94541-8a94-4a9a-8c06-dd00187164c4","7cad07e0-10ce-48b1-b339-2799d416afd3","fa47562d-4c56-4dd1-bd7f-f607fdc7593b"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-16124f05e021","providerMatrixVersion":"16124f05e021"}
Open the source
Reviewed-brand presence shareMention share · observed Oct 22, 2026, 12:00 AM UTC16 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 Oct 22, 2026, 12:00 AM UTC29.167 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"llm-observability-platforms-2026-07-27","runArtifactIds":["9e2a075d-3c64-485d-bdfd-13e9245f16f5","0eb1990c-941b-49a2-80d0-f3bc3cfbb426","70386ebd-4204-400c-bcdc-c1411631dff9","8780f9b8-3818-4862-9513-ecc905c3a0e7","ed5cf0b9-9957-4969-bb48-b24d38e23646","f2a28396-a94d-44e6-b0b6-eaf998ad5b6b","34a281bc-590a-4eb2-9c13-f3777cf9a541","e46bcc40-3682-4c64-8b4d-17989e6a4654","8c2c47af-c353-4cf0-824c-aacf2193b8e1","6ff2d38e-7f01-42cd-b044-bbfd5b5d5b56","e88e0529-0d37-4d8f-b516-f871cdd8c0de","b9ae6168-6aec-4389-8632-694f833da5cb","943be5e4-46cf-4f42-bbc3-4416b7677295","83df98d6-f8c7-4a59-8b31-7185eb1ebdc9","18605475-ed6b-42a5-85f5-a7deb970132f","19064a15-420d-4041-8723-8fccbb2a6456","b98f420d-e0ea-4d91-8733-2729e3b80ecf","4f901c58-051b-42db-9876-5ddc63419a1d","1963b455-fdc0-4a99-a55f-c296285728d0","38394a02-d6c2-4846-abf4-64ce8a4b9c4a","4fc8cde5-c00b-4ddb-8798-c16f2228d530","b31ac5e8-1280-4042-b419-03aa306f547c","c89d17a0-9c84-4377-a323-74fc7304fa8e","5ffa7349-6072-4424-ab05-2734d123425d","c0f2490d-e777-4e91-a116-8fc53bff299a","a88e6bab-ee3f-4eea-818e-8245f751aac1","ee1dbcfa-e3ab-4fc9-810b-9f2622473c56","bded4d61-f3fe-4543-8be7-00be577c1509","42299b33-91cb-495c-9965-81c755becc17","f2e0659f-510c-4a39-808c-ebe34163b330","7b488461-2238-422f-917a-e3ae541ce9ef","60378b0d-57de-4271-b5ac-389a9a62da62","fb053530-e6be-440d-9736-e4ec7ed20d53","fbd94541-8a94-4a9a-8c06-dd00187164c4","7cad07e0-10ce-48b1-b339-2799d416afd3","fa47562d-4c56-4dd1-bd7f-f607fdc7593b"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-16124f05e021","providerMatrixVersion":"16124f05e021"}
Open the source
Gemini citation shareGemini brand citation share · observed Oct 22, 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":["9e2a075d-3c64-485d-bdfd-13e9245f16f5","0eb1990c-941b-49a2-80d0-f3bc3cfbb426","70386ebd-4204-400c-bcdc-c1411631dff9","8780f9b8-3818-4862-9513-ecc905c3a0e7","ed5cf0b9-9957-4969-bb48-b24d38e23646","f2a28396-a94d-44e6-b0b6-eaf998ad5b6b","34a281bc-590a-4eb2-9c13-f3777cf9a541","e46bcc40-3682-4c64-8b4d-17989e6a4654","8c2c47af-c353-4cf0-824c-aacf2193b8e1","6ff2d38e-7f01-42cd-b044-bbfd5b5d5b56","e88e0529-0d37-4d8f-b516-f871cdd8c0de","b9ae6168-6aec-4389-8632-694f833da5cb","943be5e4-46cf-4f42-bbc3-4416b7677295","83df98d6-f8c7-4a59-8b31-7185eb1ebdc9","18605475-ed6b-42a5-85f5-a7deb970132f","19064a15-420d-4041-8723-8fccbb2a6456","b98f420d-e0ea-4d91-8733-2729e3b80ecf","4f901c58-051b-42db-9876-5ddc63419a1d","1963b455-fdc0-4a99-a55f-c296285728d0","38394a02-d6c2-4846-abf4-64ce8a4b9c4a","4fc8cde5-c00b-4ddb-8798-c16f2228d530","b31ac5e8-1280-4042-b419-03aa306f547c","c89d17a0-9c84-4377-a323-74fc7304fa8e","5ffa7349-6072-4424-ab05-2734d123425d","c0f2490d-e777-4e91-a116-8fc53bff299a","a88e6bab-ee3f-4eea-818e-8245f751aac1","ee1dbcfa-e3ab-4fc9-810b-9f2622473c56","bded4d61-f3fe-4543-8be7-00be577c1509","42299b33-91cb-495c-9965-81c755becc17","f2e0659f-510c-4a39-808c-ebe34163b330","7b488461-2238-422f-917a-e3ae541ce9ef","60378b0d-57de-4271-b5ac-389a9a62da62","fb053530-e6be-440d-9736-e4ec7ed20d53","fbd94541-8a94-4a9a-8c06-dd00187164c4","7cad07e0-10ce-48b1-b339-2799d416afd3","fa47562d-4c56-4dd1-bd7f-f607fdc7593b"],"benchmarkVersion":"4-llm-observability-platforms-2026-07-27-models-16124f05e021","providerMatrixVersion":"16124f05e021"}
Open the source
Gemini mention coverageGemini brand mention coverage · observed Oct 22, 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 Oct 22, 2026, 12:00 AM UTC37.5 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
OpenAI citation shareOpenAI brand citation share · observed Oct 22, 2026, 12:00 AM UTC9.524 percent100% confidence
Source
Rank.ai LLM Observability Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
OpenAI mention coverageOpenAI brand mention coverage · observed Oct 22, 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
OpenAI recommendation-prompt presenceOpenAI brand recommendation presence · observed Oct 22, 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

Cite this data

Free to use with attribution. Copy a citation, or link to this page: it keeps its address when the board updates.

Plain textLLM Observability engine breadth. Rank.ai, data as of October 22, 2026. https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-engine-breadth
HTML<a href="https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-engine-breadth">LLM Observability engine breadth</a>, Rank.ai, data as of October 22, 2026
Markdown[LLM Observability engine breadth](https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-engine-breadth), Rank.ai, data as of October 22, 2026
APARank.ai. (2026). LLM Observability engine breadth. Retrieved October 26, 2026, from https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-engine-breadth

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<iframe src="https://www.rank.ai/embed/boards/llm-observability-platforms-brand-engine-breadth" title="LLM Observability engine breadth" width="100%" height="420" loading="lazy" style="border:1px solid #111;max-width:720px"></iframe>
<p style="font-size:12px">Source: <a href="https://www.rank.ai/rankings/llm-observability/llm-observability-platforms-brand-engine-breadth">LLM Observability engine breadth</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.

1Langfuse50%
2Braintrust37.5%
3Arize AI / Phoenix37.5%

LLM observability platform presence

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

1Langfuse33.3%
2Braintrust27.8%
3Arize AI / Phoenix25%

LLM observability citation share

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

1Langfuse129.7%
2Braintrust121.6%
3Arize AI / Phoenix116.2%

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.

Enter your website. In about two minutes, Rank.ai asks ChatGPT, Claude and Gemini 12 questions your customers ask and grades how often they name you.

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 engine breadth

All 16 companies by engine breadth

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

LangSmith on LLM Observability Engine Breadth

What the score is built from

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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