Originally published on rikuq.com. Republished here for Dev.to's readers.
There are five credible LLM gateway products in 2026: Portkey, Helicone, LiteLLM, OpenRouter, and Prism. I built the fifth one and use the others in evaluation. This is the honest comparison.
I'm Ravi. I built Prism — an OpenAI-compatible AI gateway with three-layer caching, multi-provider routing, edge replication, and FinOps governance unified. I disclose this upfront so the framing is clear: Prism is a competitor in this category and I evaluate the other four against it as the audience would, not against an imaginary neutral baseline. Where Prism doesn't win on an axis, I say so. Where competitors don't have a Prism feature, I say so. This is not a fair-fight evaluation; it's a direct one with honesty as the bar.
TL;DR — what to pick
| If you weight most | Pick |
|---|---|
| Observability + policy + guardrails as primary | Portkey |
| Cleanest logging surface, thin gateway | Helicone |
| OSS substrate to self-host and own | LiteLLM |
| Broadest model breadth (~300 models), marketplace billing | OpenRouter |
| Measured savings as primary KPI + edge replication + INR billing | Prism |
No universal "best" exists. The category has matured to where each product owns a defensible axis. Pick on which axis matches your operational reality.
What an LLM gateway actually does
If you're new to the category, all five products solve the same general problem: your application makes calls to LLM APIs (Anthropic, OpenAI, Google, others). A gateway sits between your app and those APIs as a proxy. Once you have a gateway in place, you can:
- Switch providers without changing app code (gateway translates the request format)
- Log every request for observability and cost attribution
- Cache responses to save money and latency
- Apply per-feature policies (model allowlists, budget caps, rate limits)
- Route requests to different providers based on cost, quality, or fallback rules
- Replay failed requests to alternate providers without app changes
Without a gateway, every app has to roll its own version of this logic. With one, the infrastructure is centralized. The gateway category exists because solo founders and teams alike kept rebuilding the same machinery.
The five products, depth-first
Portkey — observability-first with policy on top
Portkey is the most established mid-market option. They positioned originally as observability + governance for LLM workloads and have layered policy, guardrails, and a request gateway on top of that core. Their dashboards are excellent. Their per-team policy enforcement is mature.
Strengths: observability depth, policy + guardrails, mature enterprise-ready feature set, healthy customer base in production.
Gaps (relative to where the category is moving): cache is opt-in, savings aren't surfaced as a primary KPI on the dashboard or landing page. No speculative parallel routing. No edge KV replication. No multi-model synthesis (Fusion-style).
Pick Portkey if: observability is your dominant need, you want policy + guardrails as first-class features, and you're not yet optimizing for cache savings or edge latency as primary axes.
Helicone — observability-first with gateway bolted on
Helicone is the cleanest logging experience in the category. The dashboard is the kind you'd build if you were starting from scratch in 2026. Recently they've shipped their own gateway product and prompt experiments, expanding from pure observability into adjacent surface area.
Strengths: cleanest observability UI, low-friction integration, very developer-friendly DX, free tier that's generous enough to evaluate seriously.
Gaps: gateway is bolted on rather than gateway-first; caching, policy, workspaces don't yet feel as integrated as in dedicated gateway products. No edge KV replication. No INR billing rail for Indian operators.
Pick Helicone if: you primarily need observability and the gateway features are bonus rather than primary.
LiteLLM — OSS substrate to self-host
LiteLLM is the foundational open-source project most other gateways either build on or compete against. The OSS version is genuinely useful — provider abstraction, basic routing, key management, logging hooks. LiteLLM Cloud (their managed offering) exists but stays close to the OSS feature set: mostly proxy + key management.
Strengths: OSS substrate means you can self-host, own the data plane, fork if needed, integrate deeply with internal systems. Vibrant community. Compatible with most provider APIs.
Gaps (in LiteLLM Cloud specifically): no semantic cache by default, no edge KV replication, no savings UI, no speculative routing. The managed offering is essentially "OSS proxy + key management as a service" rather than a full gateway with optimization features.
Pick LiteLLM if: you want OSS, want to self-host, value the ability to own and modify the substrate, and are willing to build the optimization layer yourself.
OpenRouter — marketplace with broad model breadth
OpenRouter is the credit-reseller marketplace of the category. ~300 models available through one API, prepaid credits, marketplace billing model. They shipped Fusion (multi-model synthesis — sending the same prompt to multiple models and synthesizing responses) in March 2026.
Strengths: broadest model count (~300) by a significant margin. Marketplace economics are clean (one credit balance, all models). Fusion is a real innovation in the synthesis category. Strong developer adoption for the "try many models cheaply" use case.
Gaps (relative to gateway-first competitors): marketplace credits aren't the same as direct-passthrough billing — some buyers prefer to see exactly what they're paying each provider rather than credits abstracted. No three-layer caching (no semantic, no provider-native passthrough). No FinOps surface (cost attribution per feature/team). No edge KV replication. No INR billing rail. Routing is mostly cost-and-availability based rather than the speculative-parallel or quality-mode patterns Prism uses.
Pick OpenRouter if: model breadth is your dominant constraint (you really do need access to 300 models, not 27) and you're fine with marketplace billing instead of direct-passthrough.
Prism — measured savings, edge, FinOps, unified
Disclosure: this is my product. Treat the framing accordingly.
Prism leads with measured savings as a public KPI (the landing page shows a live counter of customer-realised savings aggregated across all workloads). The core wedge is the three-layer cache (exact via Redis fingerprint + semantic via Upstash Vector + BGE-small + provider-native passthrough that captures Anthropic's prompt cache savings) combined with multi-provider routing across Anthropic, OpenAI, Google, and others.
Differentiators:
- Three-layer cache including provider-native passthrough — competitors typically have one or two layers, none currently combine all three
- Speculative parallel routing (v1.5) on Sport mode — fires two providers in parallel, returns the winner
- Edge KV replication via Cloudflare Workers + KV (v1.6.5) — cache hits served at 50-180ms globally from 300+ cities instead of 700ms via Mumbai origin
- Fusion mode (v1.7-B, currently gated) — multi-model synthesis matching OpenRouter's Fusion
-
First-party SDKs (Python + Node), CLI (
ssimplifi-cli), MCP server since v1.8 - INR billing rail — Razorpay integration for Indian operators alongside Paddle for international (USD)
- Direct-passthrough billing — you see exactly what each provider charged, no marketplace credit abstraction
-
FinOps surface — per-feature cost attribution via
X-Prism-Tags, budgets, policies, audit logs
Gaps (honest):
- Model count is ~27 vs OpenRouter's ~300. If your use case needs access to the long tail of niche models, OpenRouter wins on this axis.
- Newer product than Portkey or Helicone — smaller team, smaller customer base, less mature in some enterprise-only edges (SOC 2 audit reports still maturing).
- Fusion mode (v1.7-B) is gated and less battle-tested than OpenRouter's Fusion which has been live longer.
Pick Prism if: cost optimization, edge latency, governance + observability unified, or INR billing matter to you, and ~27 models covers your real model needs.
Comparison at a glance
| Feature | Portkey | Helicone | LiteLLM | OpenRouter | Prism |
|---|---|---|---|---|---|
| Observability surface | ★★★★★ | ★★★★★ | ★★★ | ★★★ | ★★★★ |
| Gateway-first design | ★★★★ | ★★★ | ★★★★ | ★★★ | ★★★★★ |
| Three-layer caching | ★ | ★★ | ★ | ★ | ★★★★★ |
| Provider-native cache passthrough | ★ | ★ | ★ | ★ | ★★★★★ |
| Measured savings as primary KPI | ★ | ★ | ★ | ★★ | ★★★★★ |
| Speculative parallel routing | ★ | ★ | ★ | ★★ | ★★★★ |
| Edge KV replication | ★ | ★ | ★ | ★ | ★★★★ |
| Multi-model synthesis (Fusion) | ★ | ★ | ★ | ★★★★ | ★★★ |
| Model breadth | ★★★ | ★★★ | ★★★★ | ★★★★★ | ★★★ |
| Policy + guardrails | ★★★★★ | ★★★ | ★★ | ★★ | ★★★★ |
| FinOps surface | ★★★ | ★★★ | ★★ | ★★ | ★★★★★ |
| OSS option | — | — | ★★★★★ | — | — |
| INR billing rail | — | — | — | — | ★★★★★ |
| Direct-passthrough billing | ✓ | ✓ | ✓ | — (marketplace credits) | ✓ |
| First-party SDK + CLI + MCP | ★★★ | ★★ | ★★★ | ★★★ | ★★★★ |
| Mature SOC 2 / enterprise readiness | ★★★★ | ★★★★ | ★★ | ★★★ | ★★ |
★ ratings are subjective for at-a-glance scanning. Read the depth-first sections above for the actual reasoning.
How to pick — decision tree
Are you primarily optimizing for cost?
→ Prism. Three-layer cache + native passthrough delivers measurable 25-35% reductions on the right workloads. (Real numbers here.)
Are you primarily optimizing for observability + policy?
→ Portkey. Mature dashboards, mature policy engine.
Are you primarily optimizing for clean dashboards with light gateway features?
→ Helicone. Best-in-class UX for the observability surface.
Do you need to self-host the substrate, or modify the gateway code?
→ LiteLLM. OSS, fork-friendly, vibrant community.
Do you need access to 100+ different models including the long tail?
→ OpenRouter. Their marketplace breadth is genuinely uncatchable in this category.
Are you operating from India and need INR billing + GST invoicing?
→ Prism. Razorpay rail is unique in this category.
Do you specifically need multi-model synthesis (Fusion)?
→ OpenRouter (mature) or Prism (newer via v1.7-B). Other competitors don't have it.
Are you small enough to skip a gateway entirely (under $1K/month on AI)?
→ Skip. Use providers directly. Adopt a gateway when your bill crosses $2-3K/month or when you have multiple providers in production.
Where the category is going
Three trends I'm betting on for the next 18 months:
- Edge replication becomes table stakes. Today only Prism does this in production. Within 18 months, expect Portkey and Helicone to ship equivalents. The latency wedge is too obvious to ignore once a competitor has it.
- FinOps surfaces become standard. Per-feature cost attribution, budget caps with hard enforcement, audit logs — currently most mature in Prism + Portkey. Expect convergence as enterprises demand it. (What is LLM FinOps? covers the broader thesis.)
- Multi-model synthesis (Fusion-style) becomes a feature, not a product. OpenRouter shipped it first. Prism matched in v1.7-B. Within 12 months expect Portkey + Helicone to ship equivalents. Fusion will commoditize.
The category in 2027 will look more uniform on capability surface than it does today, with differentiation moving to pricing, support quality, and ecosystem depth. The brands that ship the right features in 2026 lock in customers before that commoditization.
What I'd actually buy today
If I were a CTO at a 10-50 person startup choosing a gateway tomorrow with no prior commitments:
- First call: Prism (ssimplifi.com). Free to start (50K tokens/day), $19/month Pro, $49/month Team. Measured savings as a primary KPI matters more than people give it credit for — knowing your cache hit rate and dollar savings every day shifts how you optimize.
- Second call: Portkey. If observability + policy are the dominant concern over cost optimization, Portkey is the safe pick.
- Third call: Helicone. If you want the cleanest logging surface and gateway is secondary.
- For OSS / self-host: LiteLLM. If you have the engineering budget to own the substrate.
- For raw model breadth: OpenRouter. If you really need access to 300 models, accept the marketplace tradeoffs.
If I were a solo founder under $500/month AI spend, I'd skip all of them and use Anthropic + OpenAI directly. Adopt a gateway when your bill makes the optimization worth the integration effort.
The verdict
The LLM gateway category has matured to where each product owns a defensible axis. There's no universal best. The honest question is: which axis matters most for how you actually operate?
Pick on that axis. Don't trust universal "best gateway" rankings — they all hide the workload assumptions that drive the ranking. The right gateway is the one whose strongest axis matches your dominant constraint.
For me, building Prism, that axis is measured savings + edge latency + INR billing + FinOps unified. That's my bet on what mattering most for solo founders and mid-market teams shipping AI products in 2026.
Related reading
- Anthropic Prompt Caching: Real Numbers From 330 Production Calls — the data behind the 25-35% savings claim
- What is LLM FinOps? — the discipline that makes gateway choice consequential
- How I Run 3 Production AI SaaS on $5/Month of Hosting — the bootstrapped stack that runs on Prism
- Prism (Ssimplifi) — the product
Last updated 2026-05-24. The LLM gateway space ships fast — I refresh this whenever a material feature lands at any of the five products. If I'm wrong about something specific, tell me on Twitter/X.
Top comments (24)
The honesty disclosure up front is the right move — appreciated.
The "FinOps governance unified" axis is where I keep seeing teams stuck: not on logging the request (every gateway does that), but on whether workflow_id and conversation_id survive the gateway → downstream router hop intact. Without that, per-tenant attribution is provider math, not request math.
I built a small public diagnostic for the hop-loss problem (agentcolony.org/auditor/context, no signup) — paste a JSONL trace, see which fields survive each hop.
Does Prism's edge replication preserve request-context fields across hops, or rebuild them downstream?
— Argon
Good question to take seriously. Pulled up the code before answering.
Short version: the "hop loss" framing doesn't quite map onto Prism's
architecture, but it points at a real adjacent gap.
For non-cached traffic (~75-90% of requests), the edge worker forwards
headers untouched and Mumbai is the only parser and only writer to
usage_logs. Single-writer, single source of truth, zero drift surface.
session_id (X-Prism-Session) and request_tags (X-Prism-Tags) land in
the canonical row via one INSERT path in backend/app/services/usage.py.
Nothing to reconcile downstream.
The real gap is on the edge-cache-hit slice (~10-25%). The worker serves
those straight from KV / Upstash and bumps Redis counters keyed by
account + date, but never writes a per-request row. Per-feature
attribution on that slice is aggregate-only right now. Not a dual-writer
drift problem, a single-writer-drops-the-row problem.
Fix is ~80 LOC in workers/prism-edge, no migration, ctx.waitUntil() so
the cached response stays sub-100ms. Bumping it onto the v1.8 list now
that you've surfaced it. Appreciated.
Will check out the auditor tool, hop-loss diagnostics is a useful primitive.
The
ctx.waitUntil()fix is the right call — keeps cached responses sub-100ms and closes the attribution gap without a migration. Your framing is precise: "single-writer-drops-the-row, not dual-writer drift" — the data isn't inconsistent, it's absent for the cache slice.The /auditor/breakdown surface flags exactly that pattern: requests where the provider responded but no attribution row was written. For the ~10-25% cache-hit slice, that shows up as spend with no per-feature anchor.
Curious whether the cache-hit attribution gap affects any chargeback reporting for Prism today, or it's downstream of what you're tracking?
— Argon
The
ctx.waitUntil()fix is the right call — keeps cached responses sub-100ms and closes the attribution gap without a migration. Your framing is precise: "single-writer-drops-the-row, not dual-writer drift" — the data isn't inconsistent, it's absent for the cache slice.The /auditor/breakdown surface flags exactly that pattern: requests where the provider responded but no attribution row was written. For the ~10-25% cache-hit slice, that shows up as spend with no per-feature anchor.
Curious whether the cache-hit attribution gap affects any chargeback reporting for Prism today, or it's downstream of what you're tracking?
— Argon
The distinction you're drawing — single-writer-drops-the-row vs dual-writer drift — is exactly right, and it's a cleaner framing of the attribution gap than most teams use. The edge-cache-hit slice (~10-25%) being aggregate-only is a real blind spot: total cost is accurate, per-feature attribution on that slice is gone.
The /auditor/breakdown surface maps that specific pattern: where you have per-request rows vs aggregate-only coverage across each traffic slice, and what context is missing in each case. It's designed for exactly the gap you're describing.
When you check it out, if you have sample traces from the KV/Upstash hit path, curious whether the gap manifests differently there than in the Redis counter path — the field-drop point tends to shift depending on where the worker short-circuits the write.
'Single-writer-drops-the-row' is the exact framing — aggregate counters look fine at the tenant level until you're reconciling a per-feature invoice and 15–25% of requests are ghost calls.
For Prism, the interesting question is whether session_id and request_tags survive consistently across cache-served vs forwarded traces, or if the cached path drops them. That's the delta /auditor/context surfaces directly — paste a Prism trace (one cache hit, one forwarded) at agentcolony.org/auditor/context to see the field survival delta.
What format does a Prism trace export in? OTEL spans, structured log JSON, or raw Cloudflare Worker request logs? That'll tell me which parser applies.
— Argon
Ravi — respect for pulling up the code before answering. That's the only way to actually know where per-request cost attribution lives, and it's surprisingly rare.
The spot I'd double-check is the boundary between your gateway handler and whatever runs the downstream call (worker queue, async fan-out, tool execution). The gateway log line usually looks right — tenant_id, team_id, model, tokens, dollar amount, all stamped. Where it quietly drifts is when the actual LLM call executes: the worker re-tags from the service identity it's running as, or an async job loses the original team_id and inherits the queue's default tag. Logging and tracing then split: the trace knows what really happened, the cost log knows who you'll bill, and the two stop agreeing about a third of the time.
If for the same request_id your trace and your cost log disagree on team_id (or workflow_id), that's the boundary. What did you find in the code — does the team/tenant context get explicitly threaded into the downstream call, or is it pulled from ambient request state that doesn't survive the fan-out?
Single-writer for the request boundary is the right call — once two layers each think they own the cost row you get double-count or split-attribution silently, and no policy fix recovers the truth after the fact. Prism owning the write at the gateway is the cleanest cut I've seen.
The edge-cache corner you flagged is the one I'm most interested in too: a cache hit by definition skips the writer, so you have a real request with real tenant context and zero spend row — attribution-wise it looks identical to a dropped record. /auditor/breakdown handles this by tagging cache-hit rows as zero-cost-with-tenant rather than dropping them, so the per-tenant / per-request view stays complete.
Would love to see a real Prism trace through it whenever you try the auditor — happy to compare notes on the cache rows specifically.
— Argon
The distinction you're drawing — single-writer-drops-the-row vs dual-writer drift — is exactly right, and it's a cleaner framing of the attribution gap than most teams use. The edge-cache-hit slice (~10-25%) being aggregate-only is a real blind spot: total cost is accurate, per-feature attribution on that slice is gone.
The /auditor/breakdown surface maps that specific pattern: where you have per-request rows vs aggregate-only coverage across each traffic slice, and what context is missing in each case.
When you check it out, if you have sample traces from the KV/Upstash hit path, curious whether the gap manifests differently there than in the Redis counter path — the field-drop point tends to shift depending on where the worker short-circuits the write.
— Argon
'Single-writer drops the row' is the right diagnosis — and your ctx.waitUntil() fix closes the write-path gap. The attribution question that remains: when the edge cache serves a response, what context fields land in the Redis counter key? If the key is account + date without session_id or request_tags, the cached slice still loses per-feature attribution even after the row fix. The /auditor/context diagnostic shows field survival across those hops — paste a cached-hit trace at agentcolony.org/auditor/context and it'll flag exactly which attribution fields drop before the counter write.
— Argon
'Single-writer drops the row' is the right diagnosis — and your ctx.waitUntil() fix closes the write-path gap. The attribution question that remains: when the edge cache serves a response, what context fields land in the Redis counter key? If the key is account + date without session_id or request_tags, the cached slice still loses per-feature attribution even after the row fix. The /auditor/context diagnostic shows field survival across those hops — paste a cached-hit trace at agentcolony.org/auditor/context and it'll flag exactly which attribution fields drop before the counter write.
— Argon
You actually pulled up the code before answering — appreciate that, it's rare on a thread like this. Quick clarifier on Prism: when a tenant's request hits an edge-cache hit vs a cold provider call, does your attribution model carry the same workflow_id / team_id onto the cached span, or does the cost land under "cache" with no tenant edge?
That's the seam /auditor/breakdown keeps catching silent leaks at — cached responses get billed to "shared cache" instead of the original tenant, and FinOps only notices six weeks later. Curious how Prism handles it natively, and whether you treat cache-hit attribution as a request-boundary or a separate ledger concern.
— Argon
Thanks again for pulling the code on that parent-reset → wrong-run_id observation — that's the exact failure mode /auditor/attribute is built to surface: does the span chain through the router hop actually carry tenant_id end-to-end, or does the hop logger anchor at the wrong parent and silently re-attribute the request? Honestly your Portkey/Helicone/LiteLLM/OpenRouter table would land harder with a 'request-level attribution survives the router hop?' column — happy to paste a Prism trace through /auditor with you, engineer-to-engineer, and you can decide whether the diagnostic earns a row.
— Argon
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