OpenObserve vs Speko: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of OpenObserve and Speko — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

OpenObserve

Freemium

Open-source unified observability for logs, metrics and traces, with an AI SRE agent that correlates signals and an LLM cost and eval monitor.

Unified Telemetry Store: Holds logs, metrics, traces, RUM, session replay and error tracking in a single system instead of separate tools per signal type.
Columnar Parquet Storage in Rust: Built on the DataFusion engine with no index to build, which underpins the claimed 140x storage and 30x compute reduction versus Elasticsearch.
Autocorrelation Engine: Continuously pairs signals across frontend, API, application, database, network and infrastructure layers at over a million signals per second.
AI SRE Agent: Investigates an incident by building a service graph, quantifying SLO and revenue impact, identifying the root cause from trace evidence, and applying a corrective action such as a rollback.
Proactive Daily Briefing: Reviews every service over a rolling 14-day window and flags the ones degrading, with the deploy or change that coincided with the regression.
Agentic and LLM Observability: Tracks token spend, per-model usage mix and error rates across models in production, with failed evaluations shown alongside prompt, output and grader score.
Transparent Usage Pricing: Charges per GB ingested and per GB queried with retention included, rather than tiered seat or host licensing.
Self-Hosted or Managed Cloud: The same platform can run entirely inside your own infrastructure or as a fully managed service, including BYOB for enterprise deployments.

Cutting Observability Spend: Replace an Elastic or Datadog deployment while keeping a year of log retention, using far less storage and compute for the same data.
Automated Incident Triage: Let the SRE agent correlate an error-rate spike to a specific deploy and propose the rollback before an engineer is paged.
Monitoring LLM Applications in Production: Track token cost, model mix and evaluation failures across several models serving live traffic.
Catching Slow Regressions: Surface a service whose p95 latency quietly tripled after an index rebuild, which threshold alerting would miss.
Full-Stack Root Cause Analysis: Trace a checkout failure from the browser through the API and into the database on one correlated timeline.
Compliance-Constrained Deployments: Self-host the whole observability stack so telemetry never leaves your own infrastructure.
SLO Management: Measure which service level objectives an ongoing incident is putting at risk and how much of a user flow is affected.

Freemium

A router for voice AI that benchmarks 56 speech and language models language by language, then routes each session to the one that wins.

Language-by-Language Benchmarks: 56 speech and language models measured across ten languages, exposing that a model topping the English table may have no published rank anywhere else.
Cost-Versus-Accuracy Charts: Word error rate plotted against dollars per minute for each pipeline stage — STT, LLM, TTS and speech-to-speech — so a model choice is a priced trade-off, not a guess.
Managed Router: A hosted, provider-neutral data plane at router.speko.dev that selects the winning model per session using the measurements and fails over before a response is returned.
Typed Contracts: Public OpenAPI and AsyncAPI specifications for the Router, so integrations are generated rather than hand-rolled against an undocumented endpoint.
Open Gateway Runtime: A customer-side runtime with native LiveKit and Pipecat services offering provider-direct streaming and local BYOK credentials, keeping your vendor relationships intact.
Drop-In Framework Integration: Swap STT, LLM and TTS in a LiveKit AgentSession for Speko equivalents with credential_source="auto" and model="auto" and get routing without restructuring the agent.
MCP Server: Point Claude Code or Cursor at mcp.speko.ai to query models, voices and benchmark data from inside a coding agent.
Two Pricing Paths: Either add 5% on top of a provider's published rate while routing your own spend, or take the bundled Speko infrastructure rate of $0.09 per minute across all three legs.

Multilingual Voice Products: Ship a voice agent to non-English markets and route each language to the model that actually performs best there.
Vendor Selection Research: Use the published benchmarks to decide which STT or TTS provider to sign with before committing to a contract.
Voice Cost Optimization: Trade a small accuracy delta for a large cost reduction by picking a model at the right point on the WER-versus-price curve.
Failover and Reliability: Keep voice sessions alive through a provider outage with pre-response failover across the routed model pool.
LiveKit or Pipecat Migration: Add measured routing to an existing agent stack by swapping in Gateway services rather than rewriting the pipeline.
Agent-Assisted Model Research: Query the benchmark data from Claude or Cursor over MCP while writing the voice integration.